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Investigating the Expansion of Angiosperms during the Cretaceous Period using a Modeling Approach
by
Anastasia Gousseva
A thesis submitted in conformity with the requirements for the degree of Master of Science
Graduate Department of Geography University of Toronto
© Copyright by Anastasia Gousseva 2010
ii
Investigating the Expansion of Angiosperms during the Late
Cretaceous using a Modeling Approach
Anastasia Gousseva
Master of Science
Graduate Department of Geography University of Toronto
2010
Abstract
The use of Dynamic Global Vegetation Models (DGVMs) in paleo-vegetation studies is a
practical new approach in paleo-ecology as it allows for process-based investigations within a
flexible framework. The goal of this study is to evaluate the applicability of Lund Potsdam Jena
(LPJ) – DGVM in a paleo-study of Cretaceous angiosperm spread, while testing several pre-
existing theories regarding the spread through model experimentation. I assessed the
independent and interactive role of climate variables (temperature, precipitation, atmospheric
CO2 concentration, and seasonality), latitudinal light regime, soil structure, and plant
characteristics (tree versus grass, and deciduousness) in influencing angiosperm expansion by
simulating the response of Cretaceous land cover to changes in each factor. I found that
temperature and light were the most influential variables in determining angiosperm success,
while plant structure and deciduousness may carry implications for early angiosperm
establishment and community competition dynamics. LPJ showed great potential for refinement
and effective future use in paleo-applications.
iii
Acknowledgments
Immeasurably huge thank-you to Dr. Sharon Cowling, my wise and caring supervisor, who made
so many things (including this thesis) possible for me. I cannot say enough good things about
her kind, intelligent nature and her work. Thank you to Dr. Danny Harvey for his mentorship
and his bottomless pit of knowledge. Thank you to Dr. Sarah Finkelstein and Dr. Bill Gough, for
their guidance, help, and encouragement. Thank you to Dr. Brad Bass and his team for lending
me their resources and for the good times. Thank you to all of the U of T professors that have
been part of my learning experience. Also, thank you to the U of T Centre for Global Change
Science, for providing unique internship and travel opportunities to students, without which I
would surely not be where I am now.
Sincere thank you to Carlos Avendaño for his academic advice, for the endless awesome
experiences, and for what is sure to be a life-long friendship. Thank you to Younglan Shin for
generously sharing her immense knowledge and skills. Thank you to Rebecca Snell, and Jenn
Weaver for letting me pick their brains and for the girl-talk. Thank you to Nicole Chow for
popping into the office with hilarious commentary and for spearing me having to deal with
admin work for GGR101. Thank you to all the PGB inhabitants who make university feel almost
like home.
Thank you to all my good friends for all sorts of things. Special thank you to Marius, Kris, and
Rita for keeping me out of trouble, to Georgiy, Bogdan, Jaime, and Belinda for creating the
occasional trouble (and fun), and to Ruth, Oleksandra, and Mariya for sticking around despite the
trouble. And thank you to Jesus M. Hervas being my partner in good times and bad, for inspiring
me with his patience and love, and for always telling me “you can do it”.
Thank you to my family, my parents Olga and Alexandre and my brother Nik, for supporting me
and giving me a home that I can always go to.
Thank you to God for this beautiful adventure.
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Table of Contents
Contents
Acknowledgments.......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
List of Appendices ......................................................................................................................... xi
Introduction ......................................................................................................................................1
1.1 Setting the Context ...............................................................................................................1
1.2 My General Research Approach ..........................................................................................5
Literature Review.............................................................................................................................6
2.1 Cretaceous Climate Overview .............................................................................................6
2.1.1 Paleogeography ........................................................................................................7
2.1.2 Temperature .............................................................................................................8
2.1.3 Precipitation ...........................................................................................................12
2.1.4 Atmospheric CO2 ...................................................................................................13
2.2 Angiosperms in the Cretaceous .........................................................................................14
2.2.1 First Evidence ........................................................................................................14
2.2.2 Latitudinal Migration .............................................................................................14
2.2.3 Biotic Replacement and Rise to Dominance .........................................................15
2.2.4 Hypotheses about Early Angiosperms ...................................................................19
2.2.5 Hypotheses about Angiosperm Diversification and Spread ..................................20
2.3 A Place for Modeling in Paleoecology: Building a Case for LPJ......................................25
2.3.1 Modeling Simplification and Validation ...............................................................27
v
2.3.2 PFT modification ...................................................................................................28
2.3.3 Experiment Types ..................................................................................................31
Methods..........................................................................................................................................33
3 Introduction – Modeling Methods ............................................................................................33
3.1 Data ....................................................................................................................................33
3.2 Plant Representation ..........................................................................................................34
3.3 Experimental Design ..........................................................................................................34
3.3.1 Experimental Protocol ...........................................................................................35
3.4 Analysis of Model Output Values......................................................................................41
Results and Discussion ..................................................................................................................42
4 Introduction ...............................................................................................................................42
4.1 Concepts and Abbreviations ..............................................................................................42
4.2 Early Angiosperms in a Gymnosperm-Dominated Forest .................................................43
4.2.1 Structure and Traits of Early Angiosperms ...........................................................43
4.2.2 Cretaceous Angiosperm Trees and LPJ .................................................................46
4.3 Climate Change and Angiosperm Spread ..........................................................................47
4.3.1 Temperature Effects ...............................................................................................47
4.3.2 Precipitation Effects ...............................................................................................50
4.3.3 Atmospheric CO2 Effects .......................................................................................53
4.4 Role of Soils .......................................................................................................................55
4.5 Light Regime .....................................................................................................................57
4.5.1 Light as an Isolated Factor .....................................................................................57
4.5.2 Light – Temperature Interaction ............................................................................60
4.6 Seasonality .........................................................................................................................64
4.7 Deciduousness....................................................................................................................68
4.8 Success of LPJ in the Angiosperm Paleo-Application ......................................................76
vi
4.8.1 Strengths of LPJ as a Paleo-Research Tool ...........................................................76
4.8.2 Problems and Limitations of LPJ ...........................................................................77
Conclusion .....................................................................................................................................80
5.1 Angiosperms in the Cretaceous .........................................................................................80
5.2 Future directions: Paleo-vegetation research and development of LPJ .............................81
References ......................................................................................................................................84
Appendix 1 .....................................................................................................................................98
Appendix 2 .....................................................................................................................................99
vii
List of Tables
Table 1: Control climate and vegetation specifications. ................................................................35
Table 2: Temperature, Precipitation and Atmospheric CO2 experimental simulation anomalies.
VSC represents Variable Subjected to Change in each simulation of the experiment and Variable
Levels indicate the degree of change in each simulation. ..............................................................36
Table 3: Interactive High Latitude scenario protocol. ...................................................................37
Table 4: Seasonality experiment protocol......................................................................................38
Table 5: Deciduousness experiment PFTs and climate anomalies. ...............................................39
Table 6: 3-PFT deciduous interactive scenario PFTs. ...................................................................40
Table 7: 5-PFT deciduous interactive scenario PFTs and latitudes. ..............................................41
Table 8: Abbreviations used in discussion.....................................................................................42
Table 9: Net primary production and precipitation changes. .........................................................51
Table 10: Net primary production and atmospheric carbon dioxide concentration changes. ........54
Table 11: Net primary production and light regime changes. Temperature and precipitation
conditions are equivalent to Control. .............................................................................................58
Table 12: High latitude scenario specifications. ............................................................................60
Table 13: Net primary production and high latitude scenarios. .....................................................61
Table 14: Seasonality simulations specifications. .........................................................................65
Table 15: Net primary production; alternating deciduousness in seasonal temperature climate. ..69
Table 16: Simulation specifications for 5-PFT scenarios. .............................................................72
viii
List of Figures
Figure 1: Cretaceous period in the geological time scale (British Geological Survey). ..................6
Figure 2: Earth paleogeography 100 million years ago (Ma), modified from Hay et al (1999)
after Barron (1987). .........................................................................................................................7
Figure 3: Latitudinal temperature gradients estimated from d18O values from benthic and
planktic foraminifera, modified from Huber et al. (2002). Circles represent sea surface
temperature estimates from planktic adjusted for influence of local factors (solid) and unadjusted
(hollow). Squares represent temperature estimates from benthic foraminifera from upper bathyal
(crossed lines), middle bathyal (gray), and lower bathyal (black) zones. The solid line is the
best-fit polynomial through adjusted planktic foraminiferal data. Dashed line represents the
modern temperature gradient. ..........................................................................................................8
Figure 4: Cretaceous sea surface paleotemperature estimates in the subtropics (30-35 deg N)
from d18O values of fish teeth from the western Tethys, modified from Puceat et al. (2003).
Solid curve represents the least- squared best fit mean temperature. Temperatures are calculated
using the equation of Kolodny et al. (1983) with d18O of seawater of a) -1% and b) 0%. See
Puceat et al. (2003) for further details. ..........................................................................................10
Figure 5: Cretaceous sea surface paleotemperature estimates from d18O values in
sclerochronological sections of rudist bivalves, modified from Steuber et al. (2005). Samples are
from Jamaica (yellow), Oman (pink), Turkey (blue), Austria (brown), central Greece (green),
Croatia and Ionian islands, Greece (violet), southern Spain (red), northern Spain and western
France (dashed red lines), and southern France (black). Circles indicate mean values for the
shells that most probably recorded complete intra-annual variation. See Steuber et al. (2005) for
further details. ................................................................................................................................11
Figure 6: Cretaceous atmospheric carbon dioxide concentrations compiled from different studies,
modified from Bice and Norris (2002). Red-shaded region denotes the warmest period of the
Cretaceous based on Puceat et al. (2003) and Hay (2008).............................................................13
ix
Figure 7: LPJ-simulated vegetation composition in Carboniferous mire forest and higher altitude
regions compared to fossil record. .................................................................................................30
Figure 8: Fractional land cover occupied by each PFT, alternating between broad-leaf trees (BE)
in Control and broad-leaf grass (BG) in Grass and Grass2. ..........................................................44
Figure 9: Fractional land cover occupied by each PFT, and temperature changes........................47
Figure 10: Fractional land cover occupied by each PFT, and precipitation changes.....................51
Figure 11: Fractional land cover occupied by each PFT, and atmospheric carbon dioxide
concentration changes. ...................................................................................................................53
Figure 12: Fractional land cover occupied by each PFT, and soil type. ........................................56
Figure 13: Fractional land cover occupied by each PFT, and light regime changes (expressed
through latitude). Temperature and precipitation conditions are equivalent to Control. ...............58
Figure 14: Fractional land cover occupied by each PFT in high latitude scenarios. Climate
conditions for each scenario are outlined in detail in Table 3 in Methods and in brief in Table 12
(above). ..........................................................................................................................................61
Figure 15: Fractional land cover occupied by each PFT in high latitude and low latitude
scenarios. LL3 and LL4 have the same climate specifications as HL3 and HL4, respectively.
LL3 and LL4 were conducted at equatorial latitudes. ...................................................................63
Figure 16: Fractional land cover occupied by each PFT in seasonal conditions: hot dry summer,
warm wet winter. ...........................................................................................................................66
Figure 17: Fractional land cover occupied by each PFT in seasonal conditions: cool wet summer,
warm dry winter. ............................................................................................................................66
Figure 18: Fractional land cover occupied by each PFT; evergreen and deciduous needle-leaf
trees. ...............................................................................................................................................69
Figure 19: Fractional land cover occupied by each PFT; alternating deciduousness in seasonal
temperature climate. .......................................................................................................................71
x
Figure 20: Fractional land cover occupied by each PFT; competition between deciduous and
evergreen trees at various latitudes. Temperatures are cool and seasonal, with exception of
D0warm, which has a warm uniform climate. ...............................................................................74
xi
List of Appendices
Appendix 1. ....................................................................................................................................97
Appendix 2. ....................................................................................................................................98
1
Introduction
1.1 Setting the Context
The expansion of angiosperms during the Cretaceous (145.5 – 65.5 Ma) is one of the most
important processes that shaped today’s terrestrial ecosystems. However, despite the global
prevalence of angiosperms today and the constantly emerging botanical and paleoecological
information, there is still no consensus as to the origin, physiology, and function of early
angiosperms and their environments including the dynamics of their spread (Feild et al. 2004,
Herman 2002, Wing and Boucher 1998). There are numerous and sometimes vastly different
propositions regarding the initial appearance and characteristics of early angiosperms and the
factors involved in their diversification and rise to dominance during the Cretaceous.
Hypotheses range from evolution of micro-scale plant features (Feild and Arens 2007, Midgley
and Bond 1991, Stebbins 1981) to importance of global climate change (Cantrill and Poole 2002,
Jahren et al. 2001, McElwain et al. 2005, Morley 2003). It should be mentioned that this is a
complex, multidimensional topic, involving multiple spatial and temporal scales, in which
vegetation processes may not have necessarily taken place in the same way in separate instances
and locations. There are several gradients to keep in mind including phylogeny, latitude, and
environmental conditions (Feild et al. 2004). Furthermore, it is implicit that the fossil evidence,
upon which almost all of the existing information about the Cretaceous period is based, is limited
and open to a wide range of interpretations. Consequently, even though recent studies on the
topic are concerned with explaining why the angiosperms were able to gain dominance around
the globe, there are still numerous remaining questions as to how they spread, when and where
they spread, and even what exactly Cretaceous vegetation was like.
The Cretaceous period spans tens of millions of years. During this period (~ 140 – 65.5 Ma),
angiosperms appeared in terrestrial ecosystems (Hickey and Doyle 1977), evolved and
diversified taxonomically such that they ranged from small herbaceous plants to large trees,
developed various species-specific traits, and eventually came to colonize and dominate a variety
of environments (Herman 2002). From the available fossil studies, it is difficult if not
impossible to pinpoint the time and way in which these developments took place. For example,
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Wing and Boucher (1998) point out that in almost all previous studies there is an implied
assumption that taxonomic diversification in angiosperms took place at the same time as the
expansion of their ecological strategies and growth forms. However, they argue that there could
be an alternate interpretation in which the angiosperms’ ecological strategies and growth forms
could have occurred with a lag of about 30 million years following their taxanomic
diversification. In another point, Wing and Boucher (1998) note that by the Cenomanian (~ 99
Ma) angiosperms were spread globally with some of their sub-lineages having had over 20
million years to evolve independently in different habitats and under diverse environmental
conditions. One should also not neglect the fact that during this time period other plant types
would have also experienced evolution or extinction (Herman 2002, Schneider et al. 2004),
landscape topography and geographic position of the continents would have undergone change
(Donnadieu et al. 2006b, Hay 2008), and climate would have also changed on a local as well as
global scale (Bice and Norris 2002, Huber et al. 2002, Puceat et al. 2003, Spicer and Chapman
1990). Within this framework, we can examine several moments in the history of angiosperm
development, but we must be mindful of the limitations of our basis and be careful of making
unfounded generalizations.
Using the currently available methodology, researchers were able to construct a very general
picture of Cretaceous vegetation and climate through a collection of “snapshots” of local
vegetation assemblages and isotope indicators (Brenner 1996, Herman 2002, Hickey and Doyle
1977, Retallack and Dilcher 1981). Further insight was drawn from other types of observational
studies such as analysis of physiological and ecological plant traits and phylogenetics (Feild and
Arens 2005, 2007, Spicer and Parrish 1990). As a result, some climatic and vegetational patterns
were derived and many researchers have proposed theories to explain the patterns and make
inferences about the types of processes that may have taken place on earth during the Cretaceous.
However, even assuming that the methodology used was correct and with a reasonably small
margin of error, the amount of information gathered is miniscule considering the size of the
planet and the length of the time period covered. Just as there is limited hard evidence for
constructing the theories, there is also limited evidence to dispute them. Therefore, most
theories, though often convincing, remain in a “propositional” status, untested and unchallenged,
with little new emerging information. Thus, in order to start filling in the informational gaps, or
at least to start testing the existing theories, new methodology is clearly needed.
3
The use of Dynamic Global Vegetation Models (DGVMs) is a novel approach in paleo-ecology
which, in complement to existing fossil data, will allow for experimentation and hypothesis
testing to determine the dynamics behind vegetation cover change in past time periods
(Diffenbaugh and Sloan 2002, Doherty et al. 2000, Shellito and Sloan 2006). In fact, modeling
is arguably the only currently available method which can allow for process-focused exploration
of past ecosystems and simulation of vegetation and environmental conditions that no longer
exist in the present. No work has previously been done using the vegetation modeling approach
to simulate possible traits of early angiosperms and testing them within a dynamic framework
with various competitive and environmental scenarios. It is the intention of my work to apply
the modeling approach to test some of the existing theories regarding the angiosperm rise to
dominance during the Cretaceous period in order to provide insight on the topic from the
modeling perspective.
Examination of the modeling methodology is a large part of this study as the use of DGVMs has
only very recently been introduced in the field of paleo-ecology, particularly for experimentation
with the deep past (pre-Quaternary periods), and much work is still needed for improving the
applicability of DGVMs in the study of past ecosystems (Kaplan et al. 2002, Kohler and Fischer
2004, Shellito and Sloan 2006). Thus, the approach taken in this work is cautious and critical,
and the conclusions derived from the modeling experiments will be general and mindful of the
limitations of the model.
Fossil record information from past studies serves as the basis for comparison and verification of
the results of this study. However, it should be mentioned again that there are enormous data
gaps in the available fossil record for the Cretaceous period and it too should be approached
critically. Nevertheless, there are several fairly clear patterns that can be drawn from the record
with regard to angiosperm spread. Firstly, it appears that angiosperms had begun their
development in the tropics (Brenner 1996), where they first became abundant locally in disturbed
habitats (Herman 2002, Scott and Smiley 1979, Taylor and Hickey 1992), and then penetrated
into more stable and light/nutrient-limited habitats and dispersed pole-ward (Drinnan and Crane
1990, Herman 2002). In middle latitudes, angiosperms had once again initially dominated in
disturbed habitats such as stream margins and coastal plains and later spread into other areas
(Coiffard et al. 2007). However, their dominance was not as clear in stable and stressed
environments in the higher latitudes as some areas remained dominated by other plant types
4
(Herman and Spicer 1997, Spicer et al. 2002). These vegetation patterns provide a context for
the model experiments in my study and a background for some of the theories regarding
angiosperm spread put forth in previous research (discussed later) which will be re-examined in
this study from the modeling perspective.
Although the main intention of my research is to bring innovation for a better understanding of
past ecosystems and climate, it carries many implications for current issues in science and
society. In light of the ongoing energy-intensive anthropogenic activities that release millions of
tonnes of greenhouse gases into the atmosphere, there is mounting concern over the potential
climatic and ecological impacts of this trend. Researchers have presented clear evidence of
climate change and climate-change-related weather extremes and ecological consequences, with
a high probability of more impacts on the way (IPCC 2007). However, due to the complexity of
earth systems, it is difficult to make predictions for the future, particularly with respect to
responses of the biosphere to climate (Hargreaves and Annan 2009). There is evidence of
significant climate change at several times during the Cretaceous (Hay 2008, Huber et al. 2002,
Mutterlose et al. 2009, Puceat et al. 2003), which may or may not have affected global vegetation
patterns. This study may shed light on the issue, determining the relative importance of climate
for success of certain plant groups with respect to other variables such as plant competition. The
results carry implications for understanding plant establishment and extinction under different
environmental conditions. Understanding ecosystem functions and climate interactions is
imperative in building strategies and policies to protect ecosystem health. Studies of the past can
provide realistic perspective on the Earth’s natural cycles as well as short-term and long-term
responses to human activity, which will improve our foresight in our actions and help us to make
better decisions.
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1.2 My General Research Approach
This study uses the Lund Potsdam Jena (LPJ) DGVM with a modified Plant Functional Type
(PFT) vegetation classification scheme to simulate vegetation cover under various environmental
conditions corresponding to the Cretaceous period. The purpose of this research can be divided
into two themes: assessment of the LPJ modeling methodology in paleoecology (methodology),
and investigation of the spread of angiosperm during the Cretaceous period (application). Since
the methodology is integrated in the application process, some questions regarding both themes
will be addressed together in the discussion of results. Considered separately, the research
objectives are the following:
Methodology:
1) Identifying the kinds of paleoecological questions that can and cannot be addressed
with the current state of the LPJ model structure and PFT-based vegetation
classification system
2) Suggesting future directions in developing LPJ for improving its applicability in
paleovegetation modeling
Application:
1) Identifying the role of the following environmental variables in the spread of
angiosperms during the Cretaceous period using LPJ:
a. Temperature change
b. Precipitation change
c. Change in atmospheric carbon dioxide concentration
d. Competition with other plant types
e. Height and physical structure of angiosperms
f. Soil characteristics
g. Light regime
h. Seasonality of climate
i. Deciduousness of competing gymnosperms
Chapter 2 (Literature Review) will explain the underlying conceptual theory of my research
questions.
6
Literature Review
2
2.1 Cretaceous Climate Overview
The Cretaceous corresponds to a geological period roughly 145.5 to 65.5 million years ago (Ma).
During that time, the earth was characterized by unique geological processes that shaped the
oceans and continental shelves, atmospheric features that influenced climate and terrestrial
processes, and a specific configuration of the continents that influenced ocean currents
(Donnadieu et al. 2006b, Hay 2008). The Cretaceous period forms an extensive research field
and there are ongoing studies investigating Cretaceous paleogeography and climate with
numerous topics still under debate.
Figure 1: Cretaceous period in the geological time scale (British Geological Survey).
7
2.1.1 Paleogeography
Figure 2: Earth paleogeography 100 million years ago (Ma), modified from Hay et al (1999) after
Barron (1987).
The above illustration (Figure 2) represents the approximate configuration of the continents at
roughly 100 Ma (middle of the Cretaceous period) as reconstructed by Hay et al. (1999). This
arrangement was by no means static throughout the Cretaceous as there was tectonic movement
as well as sea level changes. In general, the tropical regions corresponded to present-day
northern half of Africa and South America, Central America, and South-East Asia. Southern
Africa and South America, Australia, southern half of North America, and most of Europe and
Asia were in the mid-latitudes. Northern parts of North America and Asia, as well as Antarctica
were in the high latitudes and close to the poles. Towards the late Cretaceous, the continents
were more spread apart, opening up the Central Atlantic Ocean. Due to orogenic processes and
changes in sea level, some parts of the continents became submerged in water while others were
lifted up (Donnadieu et al. 2006b). Overall, the surface of the earth was very dynamic during the
Cretaceous with constant changes taking place at different scales of the landscape.
8
2.1.2 Temperature
Reconstruction of the Cretaceous paleotemperature curves has been done in a number of studies
at different spatial scales and using various methods. Isotope analysis (Huber et al. 2002, Puceat
et al. 2003, Steuber et al. 2005), paleovegetation analysis (Spicer and Parrish 1990, Upchurch et
al. 2007), and climate modeling (Donnadieu et al. 2006a) were among the most common
approaches used, with some studies reporting conflicting results (Bice et al. 2006, Horrell 1990,
Pearson et al. 2001, Sellwood et al. 1994). Nevertheless, there is a reasonable consensus about
the general picture of Cretaceous climate and the main trends that took place.
Figure 3: Latitudinal temperature gradients estimated from d18O values from benthic and planktic
foraminifera, modified from Huber et al. (2002). Circles represent sea surface temperature
estimates from planktic adjusted for influence of local factors (solid) and unadjusted (hollow).
Squares represent temperature estimates from benthic foraminifera from upper bathyal (crossed
lines), middle bathyal (gray), and lower bathyal (black) zones. The solid line is the best-fit
polynomial through adjusted planktic foraminiferal data. Dashed line represents the modern
temperature gradient.
9
Cretaceous climate was generally warmer and more equable compared to today, with polar
regions being significantly warmer (Sloan and Barron 1990). Today, the latitudinal gradient
between the equator and the North Pole is about 50 degrees Celsius, and 90 °C between the
equator and the South Pole. In the Late Cretaceous the gradient in both hemispheres was
estimated to be 35 °C or less (Hay 2008). The figure above (Figure 3), modified from Huber et
al. (2002), shows a comparison between today’s latitudinal temperature gradient (dashed line)
and the approximate gradient at different times during the Cretaceous (solid line) based on δ18
O
values in planktic foraminifera. The figure also illustrates changes in average temperature
throughout the Cretaceous (going backwards in time) with graph 1 representing late
Maastrichtian age (65.5 – 68.5 Ma) , graph 2 representing the late Campanian (75.4 – 76.4 Ma),
graph 3 showing temperatures from the Turonian (92 – 94 Ma), and graph 4 – temperatures from
the latest Albian (99 – 100 Ma). The latter two graphs correspond to periods associated with the
warmest temperatures in the Cretaceous, while the first two denote a period of cooling.
A compilation of studies, primarily based on foraminiferal δ18
O records, indicates that there were
warm and cool episodes during the Cretaceous. The approximate Cretaceous paleotemperature
curve for the subtropical region (30 – 35 °N) as reconstructed by Puceat et al. (2003) is presented
in Figure 4 (on following page). The earliest Cretaceous (Berriasian – Barremian) was
characterized by large and relatively short-term temperature fluctuations (Price et al. 2000,
Puceat et al. 2003, van de Schootbrugge et al. 2000) with possible cooling events taken place
during the Valanginian (Puceat et al. 2003). The Aptian - Albian (middle Cretaceous) was
mostly a period of global warming, which was especially pronounced towards the late Albian –
early Cenomanian and Turonian (Puceat et al. 2003), although a recent study of nannofossil
assemblages found that there was likely at least one cold interlude in the late Aptian – early
Albian (Mutterlose et al. 2009). Early Aptian was allegedly a transition period between icehouse
and greenhouse conditions, and this period notably corresponded with the initial radiation of
angiosperms (Jahren et al. 2001).
Most researchers agree that the middle of the Cretaceous (Cenomanian - Coniacian) had the
warmest temperatures relative to today in both the tropics and the poles (Hay 2008). Mean
annual temperatures (MAT) in the sub-tropics were likely around 13 – 14 °C and may have
reached up to 29 °C during the Cenomanian and Turonian (Puceat et al. 2003). Leaf-margin
analysis of dicotyledonous angiosperms showed that the mean annual temperatures in the high
10
paleolatitudes (75 °N) were approximately 10 °C during the Cenomanian (Spicer and Parrish
1990), where the cold month mean was not likely lower than -11 °C while the warm month
means may have been greater than 25 °C (Parrish et al. 1987). Angiosperm foliar physiognomy
analysis showed an increase in mean annual temperatures (to 12-13 °C) in the same area in the
Coniacian (Spicer and Parrish 1990).
Figure 4: Cretaceous sea surface paleotemperature estimates in the subtropics (30-35 °N) from
δ18
O values of fish teeth from the western Tethys, modified from Puceat et al. (2003). Solid curve
represents the least- squared best fit mean temperature. Temperatures are calculated using the
equation of Kolodny et al. (1983) with δ18
O of seawater of a) -1% and b) 0%. See Puceat et al.
(2003) for further details.
Global temperatures appear to have undergone a sharp decline during the late Campanian and
Maastrichtian, when the temperatures were similar to those of the early Cretaceous; around 12 -
20 °C in the sub-tropics (Puceat et al. 2003, Wolfe and Upchurch 1987b). In the high latitudes
(85 °N), MAT may have dropped to about 5 °C (Spicer and Parrish 1990). There is some
controversy regarding whether or not there was ice cover present in the high latitudes (Hay
2008).
11
Steuber et al. (2005) found evidence of intra-annual temperature seasonality at tropical latitudes
(8 to 31 °N) using intra-shell variation of 18
O value if rudist bivalves (Hippuritoidea). They
inferred that during the warmest periods, the maximum temperatures reached were 35 to 37 °C
between 20 and 30 °N and the seasonal variability was relatively low, less than 12 °C. However,
in the cooler periods of the Cretaceous the variability was notably higher, up to 18 °C. They
suggest that the increase in variability could be explained by the presence of polar ice sheets
during the cool episodes. See Figure 5 for the approximation of the Cretaceous paleo-
temperature curve based on several samples by Steuber et al. (2005), noting the decrease in intra-
annual temperature variation during the warmest ages (late Turonian and Santonian).
Figure 5: Cretaceous sea surface paleotemperature estimates from δ18
O values in
sclerochronological sections of rudist bivalves, modified from Steuber et al. (2005). Samples are
from Jamaica (yellow), Oman (pink), Turkey (blue), Austria (brown), central Greece (green),
Croatia and Ionian islands, Greece (violet), southern Spain (red), northern Spain and western
France (dashed red lines), and southern France (black). Circles indicate mean values for the shells
that most probably recorded complete intra-annual variation. See Steuber et al. (2005) for further
details.
12
In summary, the following temperature trends have been agreed upon in most Cretaceous climate
research and were taken as a basis for paleovegetation modeling in this study:
1) Tropical sea surface temperatures were close to or a few degrees warmer than today
throughout most of the Cretaceous period.
2) Latitudinal temperature gradient was relatively low and the high latitude areas were much
warmer than today.
3) Temperatures fluctuated in the early Cretaceous, increased during the Aptian and Albian
peaking around the Cenomanian and Turonian, and decreased during the later part of the
Cretaceous.
4) Annual temperature seasonality was likely higher during the cold periods.
2.1.3 Precipitation
Precipitation is not typically discussed on a global scale as it tends to be region-specific. Thus,
no attempt has yet been made to put together a comprehensive overview of Cretaceous
precipitation patterns.
A few characteristics can be noted regarding Cretaceous precipitation. Tyszka (2009) presented
evidence of high seasonality in the mid-latitudes during the middle Albian, with relatively dry
summers and wet winters. In the Late Cretaceous low middle latitudes, there was probably
moderate to low precipitation as leaf size tended to be small (Wolfe and Upchurch 1987b). In
the Campanian, around 50 °N and above 65 °N, the leaves were typically larger indicating wetter
regimes at these latitudes (Wolfe and Upchurch 1987b).
Ufnar et al. (2003) estimated that during the Albian warming there was an increase in
atmospheric heat transfer, which would have intensified the hydrologic cycle causing increased
precipitation at mid and high latitudes and an increased moisture deficit in the tropics. Modeling
results in combination with stable isotope records in Ufnar et al. (2008) showed that North
American precipitation further intensified in the Cenomanian; it was approximately 1.8 times the
present values around the equator (5 °N), 3.6 times the present values in the 45 – 50 °N range,
and 2.0 times the present values in high latitudes (75 °N). Precipitation would have been 2.5
times less than modern rates in the 15 °N dry belt.
13
The high arctic latitudes were probably not water stressed in the late Early Cretaceous (Spicer et
al. 1993) and there precipitation was probably uniform throughout the growing season in the
Early Cretaceous and the Cenomanian, although some drying may have occurred in the
Maastrichtian (Spicer and Parrish 1990).
2.1.4 Atmospheric CO2
Figure 6: Cretaceous atmospheric carbon dioxide concentrations compiled from different studies,
modified from Bice and Norris (2002). Red-shaded region denotes the warmest period of the
Cretaceous based on Puceat et al. (2003) and Hay (2008).
Figure 6 shows estimates of Cretaceous atmospheric carbon dioxide concentrations from several
studies. Atmospheric CO2 level was several times higher than today’s level throughout the entire
Cretaceous period. Most estimates place the CO2 level at around 1000 - 2000 ppm in the Early
14
Aptian (Berner and Kothavala 2001, Freeman and Hayes 1992, Retallack 2001). Although some
researchers believe that atmospheric CO2 decreased through the Aptian – Turonian periods
(Berner and Kothavala 2001, Ekart et al. 1999, Freeman and Hayes 1992), there is significant
evidence of a sharp upward trend in the Aptian (Berner 1990, Cerling 1991, Volk 1987) possibly
going as high as 4000 ppm (Bice and Norris 2002, Jahren et al. 2001, Retallack 2001). In the
Late Cretaceous, atmospheric CO2 decreased to under 1000 ppm (Andrews et al. 1995, Berner
and Kothavala 2001, Ekart et al. 1999, Retallack 2001), with some estimates close to today’s
value, around 300 ppm (Beerling 2002, Royer 2005).
2.2 Angiosperms in the Cretaceous
2.2.1 First Evidence
The initial appearance of angiosperms dates back to the Velanginian-Hauterivian. The earliest
palynological evidence of angiosperms was found in the Northern Gondwana province around
the paleoequator (Brenner 1996). In Asia and North America, in regions corresponding to
middle paleolatitudes, the oldest found angiosperm remains were dated to the Barremian –
Aptian time (~125 Ma) (Herman and Spicer 1999, Retallack and Dilcher 1986, Samylina 1968,
Spicer et al. 2002). Most of the oldest angiosperm remains were found in alluvial deposits
(Samylina 1968, Scott and Smiley 1979, Smiley 1969a, Spicer and Parrish 1990).
2.2.2 Latitudinal Migration
The age of the earliest angiosperm fossils tends to decrease with increasing latitude, thus
following a pole-ward migration pattern. On the Gondwana continent, the monosulcates
(angiosperms), of which the earliest specimens dating to the Velanginian – Hauterivian (~ 135
Ma) were found around the paleoequator, were present between roughly 30 degrees N to 60
degrees S paleolatitude by the late Barremian – early Aptian (~125 Ma) (Hickey and Doyle
1977). In the western North-American middle paleolatitudes, the first records of angiosperm
pollen were from the early-middle Albian (~105 Ma) (Crabtree 1987), while the oldest
angiosperm megafossils from Alaska date to the late Albian (~100 Ma) (Scott and Smiley 1979).
15
In the Southern Hemisphere, following the rapid expansion of monosulcates in the Barremian –
Aptian (~ 125 Ma), an expansion of eudicot pollen (eudicots are a large angiosperm group
encompassing most trees, having two cotyledons in the seed) took place, beginning around the
paleoequator in the Aptian, and spreading to the high latitudes by the Cenomanian (~95 Ma)
(Drinnan and Crane 1990). The oldest angiosperm record from South America is from the
Aptian (~120 Ma) (Romero and Archangelsky 1986), while in Antarctica, the oldest angiosperm
evidence is from the early Albian (~110 Ma) (Truswell 1990). From the results of palynological
and sedimentary studies from Israel and West Africa, it has been suggested that for about the
first 10 million years, early angiosperms evolved in the tropics under moist or seasonal
conditions, and some species were able to colonize more arid regions as well as migrate towards
the poles (Doyle 1992).
2.2.3 Biotic Replacement and Rise to Dominance
Biotic replacement in vegetative cover was a prominent process during the Cretaceous. Many
instances of change in vegetation cover have been observed in the fossil record, where various
plant groups experienced expansion and/or decline to the point of near- or complete extinction in
different parts of the globe (Herman 2002, Upchurch and Doyle 1981). The most prominent
instances of biotic replacement at this time involved invasion by the angiosperms, correlated
with a decline of pre-existing vegetation (Herman 2002, Lidgard and Crane 1990, Midgley and
Bond 1991). However, in all cases, it remains undetermined whether the decline of other
vegetation types is a direct result of angiosperm expansion (i.e. they were out-competed by the
angiosperms), or if there had been other, perhaps more important, factors at play.
By the late Albian, angiosperms had expanded geographically and were present from pole to
pole. However, up until and during the Late Cretaceous, they only dominated locally in certain
regions, while on a large scale, gymnosperms and ferns remained the main ecological plant
groups (Herman 2002).
In the Asian North Pacific region during the early and middle Albian, the vegetation was
composed of mainly ferns, ginkgoaleans, cycadophytes, and conifers. Along with horsetails and
liverwarts, angiosperms were rare and had low diversity (Herman 2002). Coastal plains in
16
North-eastern Asia and Alaska were among the angiosperm-dominated areas (Golovneva 1994,
Herman 1993, Herman and Spicer 1997, Spicer and Parrish 1990). Angiosperms also had
significant presence in parts of the volcanic belt in Asia and in continental interior depressions,
which are also areas with frequent disturbance (Herman and Spicer 1999, Samylina 1974).
Studies of the Grebenka Flora in North-eastern Asia in the latest Albian-Cenomanian showed
that although angiosperm fossils dominated the megafossil assemblages, followed by conifers,
ferns, and other groups of plants, angiosperm pollen grains were not predominant among the
local pollen and spores (Spicer et al. 2002). Generally, the distribution of angiosperm fossils
throughout northern Asia is dispersed and heterogenous. Herman (2002) suggests that this could
indicate that the regional vegetation was likely dominated by ferns and gymnosperms, while
angiosperms may have dominated some local depositional basins.
The margins of the Arctic Ocean in the Early Cretaceous were likely occupied by humid forests,
the canopy of which was dominated by deciduous conifers, some of which were broad-leaved, as
well as ginkgophytes. The understory was composed mainly of ferns, sphenophytes,
pteridosperms, and cycadophytes (Scott and Smiley 1979, Smiley 1969b). In the sub-tropical
and tropical paleolatitudes, the vegetation was mainly xeric, with abundant microphyllous
conifers (Spicer et al. 1993).
In North America, angiosperm pollen and megafossils from the middle to late Albian were
common in the Rocky Mountains, while fossil assemblages from other regions, including the
Wayan Formation in southeast Idaho and the deltaic region of the Mowry Formation in
Wyoming were fern-dominated (Crabtree 1987). Another site in Eastern Kansas (38 °N
paleolatitude) was predominantly covered by conifers (Upchurch 1995). In the mid Cretaceous,
angiosperms began to gain local dominance replace other plant groups on the North American
continent (Upchurch and Doyle 1981). For example, cheirolepidiaceous conifers (family of
conifers that shed the cone together with the seeds), which had been prevalent during the Early
Cretaceous, particularly in the tropical regions, had declined dramatically in abundance and
diversity after the Aptian, as evidenced by the records of fossil pollen Classopollis, which is
produced by cheirolepidiaceous conifers (Brenner 1963). At least two genres of these conifers,
Pseudofrenelopsis and Frenelopsis, had become extinct by the Late Cretaceous as the invading
angiosperms became prominent in areas previously dominated by the conifers (Alvin 1982,
Upchurch and Doyle 1981). It must be noted, however, that it took considerable time for
17
angiosperms to disperse into Pseudofrenelopsis forests, whereas levees and stream margins
which were previously dominated by Frenelopsis and cycadeoids were already dominated by
angiosperms in the Albian (Fontaine 1889, Upchurch and Doyle 1981). Similar to Frenelopsis,
cycadeoids in freshwater margins and coastal plains were also displaced by the angiosperms by
the Middle Albian (Retallack and Dilcher 1981). Many kinds of ferns experienced extinction
throughout the world within coastal swamps and marshes (Brenner 1963, Oishi 1940, Rushforth
1970). The tree fern Tempskaya had also become extinct during the angiosperm spread in North
America (Berry 1911).
In North America, conifer families Taxodiaceae and Pinaceae were among the least affected
plant groups during the angiosperm spread (Brenner 1963, Glaser 1969, Miller 1977). In the
inland regions of mid- to northern United States, cycadeoids, conifers, and ferns maintained their
dominance in the vegetation composition into the mid-Cretaceous (Delevoryas 1971, Weiland
1916). In northeastern Russia, there was a significant reduction and gradual disappearance of
ginkgophytes, cycadophytes, and ferns during the Late Cretaceous, accompanied by a steady
increase in angiosperm diversity (Golovneva 2000).
Throughout the Cretaceous, Australia was mostly covered by coniferous forests (Dettmann et al.
1992). The high palaeolatitudes of south-eastern Australia (possibly as high as 85 °S)
experienced a complete disappearance of bennettitaleans (cycadeoids), taeniopterids (ferns), and
sphenopsids (horsetails) during the time of angiosperm spread by the early Albian. Meanwhile,
the broad-leaved and microphillous conifers continued to dominate the canopy, while ferns
persisted as major understory components (Chapman and Smellie 1992, Francis 1986). By the
Late Cretaceous, Australian coniferous forests had experienced a loss of Ginkgo species while
new angiosperm species had become part of the canopy (Dettmann et al. 1992). A similar trend
was observed in Antarctica, where bryophytes, bennettites and other seed plants declined rapidly
in diversity throughout the Late Cretaceous, while conifers remained relatively stable (Cantrill
and Poole 2002).
In many areas in the mid to late Cretaceous, angiosperms were the most diverse plant group but
not necessarily the most dominant (Lupia et al. 1999). For instance, a early Campanian
vegetation assemblage from the Two Medicine Formation in Montana had 30 angiosperm, 2
conifer, and 6 fern species (Crabtree 1987). However, angiosperms were most abundant in
18
channel margins, crevasses, and levees, while swamps were dominated by conifers, and channel
margins were still dominated by palm species (Wing and Boucher 1998). Similarly, leaf
assemblage data from the Black Hawk Formation in Utah shows that conifers and ferns were
dominant in coal swamps, while angiosperms were dominant in channel margins (Parker 1975).
Throughout the Late Cretaceous, angiosperms typically dominated in environments that
experienced frequent disturbance, especially north of 45 degrees paleolatitude in North America.
In more stable environments the vegetation assemblage mainly comprised of conifers, ferns,
cycadophytes, and ginkgos (Spicer 1987). However, by the late Albian-Cenomanian,
angiosperms were expanding into more stable habitats within forest interiors (Herman 2002).
The trends of biotic replacement differ in different parts of the world. However, it is clear that
the rapid angiosperm radiation, beginning in the Aptian – Albian, was a global phenomenon.
The continent-level species richness increased from under 5% of average values to over 40%
within about 40 million years (Lidgard and Crane 1988, 1990, Lupia et al. 1999). During the
same time period, relative abundance of angiosperms in North America (Lupia et al. 1999) and
Australia (Nagalingum et al. 2002) had also increased, signifying the angiosperm rise to
dominance (McElwain et al. 2005).
Overall, in the fossil record it appears that gymnosperms and free-sporing plants largely
dominated the vegetation composition until about the Aptian - Albanian (~145 – 122 Ma), when
the angiosperms began to radiate and diversify. Starting in the late Cenomanian (~95 – 93.5
Ma), and into the Turonian and late Santonian (~85 Ma), angiosperms came to dominate in many
communities throughout the globe (McElwain et al. 2005). Gymnosperms in most areas were
not entirely competitively replaced and some species were more affected than others.
Cheirolepidaceae, which were arid-adapted conifers, were among the gymnosperm groups that
were completely replaced, presumably by angiosperms, while other groups including pine were
not as affected (Lupia et al. 1999). The degree of increase in angiosperm dominance and
complementary replacement of other plants seems also dependent on latitudinal location; while
angiosperms quickly came to dominate in the low latitudes, gymnosperms remained the
prevailing plant group in high latitude areas (Cantrill and Poole 2002, Delevoryas 1971,
Nagalingum et al. 2002). In most cases, free-sporing plants groups underwent angiosperm
replacement most intensely in low as well as high latitudes.
19
2.2.4 Hypotheses about Early Angiosperms
The initial occurrence and development of angiosperm-dominated areas has been under research
for some decades and several propositions have emerged regarding the ecophysiology of early
angiosperms, the function they played within their environment, and their interaction with other
pre-existing plant types.
It has been previously suggested that, based on the near-channel location of most angiosperm
remains, angiosperms were of a mountain origin, and their leaves and pollen may have been
transported by rivers from highland regions and deposited in channel beds (Samylina 1968,
Vakhrameev 1947, 1952). However, later research disputed this hypothesis and proposed that
angiosperms likely originated in basins where they colonized disturbed near-channel habitats
(Farley and Dilcher 1986, Herman 2002).
One view supporting the near-channel origin is that angiosperms began as rhizomatous
herbaceous perennial weeds that lived in disturbed, nutrient-rich riparian habitats, close to river
channels (Hickey and Doyle 1977, Taylor and Hickey 1992, Taylor and Hickey 1996). Royer et
al. (2010) supported the view that the early angiosperms were fast-growing, weedy herbs with
short leaf lifespan and high photosynthetic rates. Dilcher et al. (2007) and Sun et al. (2008) went
further to say that early herbaceous angiosperms may have originated in aquatic habitats and
marshes, based on their finding of a possible aquatic angiosperm ancestor in North-Easthern
China.
Based on the analysis of ecophysiological traits of several angiosperm lineages, Feild et al.
(2004) inferred yet another possibility; that the angiosperms may have began as woody plants in
the forest understory and/or in shady stream-sides, where the environment is generally dark and
experiences frequent disturbance. The findings from Coiffard et al. (2007) also suggested the
possibility of a “dark and disturbed” origin. Another hypothesis is that angiosperms began in dry
environments, having had xeromorphic features (Mohr and Eklund 2003, Mohr and Friis 2000,
Mohr and Rydin 2002). Conversely, Field et al. (2009) argue that early angiosperms were
xerophobic and evolved in moist tropical terrestrial environments.
Although many theories have been put forth regarding angiosperm origin, a consensus is yet to
be reached as many questions remain about early angiosperm ecophysiology and habitat. Part of
20
the debate stems from the limited availability of fossil samples as well as questions regarding the
correct interpretation of the findings.
2.2.5 Hypotheses about Angiosperm Diversification and Spread
In explaining the success of angiosperms, one must first consider angiosperm physiology and
innovative traits. Traits that have not been common in preceding plant groups, including
reproductive traits such as double fertilization, presence of xylem vessels, appearance of flowers
and various leaf forms, and development of mutualisms with insect pollinators, are considered to
be “innovations” that could have aided in establishment and competition with other plants
(Burger 1981, Feild and Arens 2007, Midgley and Bond 1991). Some species have developed
specific defense mechanisms for protection against herbivory (Wing and Boucher 1998). Early
angiosperms likely had rapid growth rates and life cycles, which would have allowed for quick
and effective spread, particularly into freshly disturbed areas with high levels of light, water and
nutrients (Bond 1989, Midgley and Bond 1991, Taylor and Hickey 1996).
Early theories regarding angiosperm spread focus primarily on angiosperm reproductive traits,
namely their co-evolution with insect pollinators and animal dispersers (Burger 1981, Crepet
1983, Regal 1977). Unlike gymnosperms, which relied on inefficient wind dispersal,
angiosperms were able to disperse over longer distances through vertebrates and produce more
outcrossed offspring through insect pollination (Crepet 1983, Regal 1977). Regal (1977)
specifically cited the limitation of wind pollination in gymnosperms as the main cause of
angiosperm success and the eventual confinement of gymnosperms to stressed environments.
Doyle and Donoghue (1986) hypothesized that angiosperms rose to dominance because of their
high speciation rates, which, according to them, were also a product of insect pollination and
vertebrate dispersal. Also in support of the reproductive superiority hypothesis, Knoll (1986)
and Niklas (1988) noted that over the past 420 million years, the diversification trend of plants
has followed the evolution of their reproductive structures.
Stebbins (1981) argued that pollination vectors had not played a dominant role in angiosperm
diversification until more recent stages in their evolution. Instead, he emphasized the importance
of traits that quickened seed production and helped dispersal and seedling establishment:
21
reduction of female gametophyte to an eight-nucleate embryo sac, development of double-
fertilization, the closed carpel, and the stylar canal. These developments made angiosperms
flexible in terms of reproduction. Some species were able to produce large seeds which made it
possible for them to establish in dimly lit-conditions such in a forest floor, as large seeds contain
nutrients which aide the plant during the early stages of growth until it is able to compete for
light. Stebbins (1981) also argued that the developed vascular system and certain features of
their biochemistry enabled the angiosperms to have generally higher growth rates than their
competition, which would have also favoured them among other plant types. Bond (1989)
supported this view, adding that the rapid reproductive cycle and leaf characteristics, namely
herbaceousness and venation, may have also favoured the angiosperms especially in the early
growth stage. Midgley (1991) also supported Stebbins in stressing the importance of
reproductive and vegetative growth rates and regeneration.
Although physiological and reproductive traits are certainly important in understanding the
dynamics of evolution and spread of early angiosperms, they cannot be looked at in isolation
from environmental factors. Several theories have been put forth focusing on aspects of the
Cretaceous environment and the impact they would have had on angiosperms and other
vegetation types.
Cretaceous climate was different from today and evidence of several major and minor periods of
climate change and/or fluctuation during this time has been recorded. It is unclear how the
vegetation composition would have changed in response to climate, although it is likely that
some impact would have ensued (Feild and Arens 2005). Retallack and Dilcher (1981) proposed
that, as ruderal-strategy early succession plants, angiosperms were more adapted to changes in
environment, such as warming and increases in disturbance. Gymnosperms were tolerant of
climate and nutrient stress and tended to persist in high-stress environments (Regal 1977).
However, they were not active colonizers due to relatively slow growth rates especially in
juvenile stages (Bond 1989).
Coiffard et al. (2007) linked the Early Cretaceous angiosperm invasion of Europe specifically to
environmental factors, giving special attention to moisture regimes and drought. They suggested
that European aquatic angiosperm taxa were mainly constrained by water and carbon dioxide
supplies aside from their reproductive cycles, and that angiosperms may have colonized
22
floodplains in Europe during the semi-arid phase of the Early Cretaceous. Feild et al. (2009)
proposed that early angiosperms required ever-wet environments and they linked global
angiosperm spread with a global increase in wet, humid environments due to the spread of high
precipitation belts, break-up of continents (resulting in a greater land surface area exposed to
oceans), and an increase in sea level during mid to Late Cretaceous. They claimed that the
dependence of angiosperm on abundance of water would also explain why angiosperms did not
become dominant in the Late Jurassic and early Cretaceous, as these times were characterized by
globally dry conditions (Rees et al. 2000).
Several studies suggested that global warming may have played a significant role in latitudinal
expansion of angiosperms. The fact that angiosperms dispersed between tectonic plates, which
is unlikely unless the climate is favorable, shows that phases of global warming may have
provided pole-ward dispersal opportunities for angiosperms (Morley 2003). In high-latitude
regions of Antarctica, the peak of floristic replacement occurred during the warmest period of the
Cretaceous (Turonian) while before this time the steep climatic gradient at the poles was likely a
successful barrier to angiosperm invasion (Cantrill and Poole 2002). Feild et al. (2009) also
noted that most basal angiosperms are intolerant to frost conditions. Therefore, increases in
temperature may have been required for angiosperm migration to high-altitude coastal fringes.
Coiffard et al. (2007) proposed that angiosperms in European regions could have been pushed
pole-ward by warming temperatures in the Albian. Golovneva (2000) observed that changes in
temperature during the Late Cretaceous in general were correlated to the increases and decreases
in occurrence of some plant groups in northeastern Russia.
The changing atmospheric carbon dioxide concentrations over the Cretaceous period may have
also been influential to vegetation change. Bazzaz et al. (1990) found that angiosperms tend to
respond more strongly to increasing CO2 than conifers. Jahren et al. (2002) explained that the
rapid increase in CO2, such as the one resulting from a methane hydrate release in the Early
Cretaceous, could have assisted in the angiosperm spread into established plant communities.
Because there is an inevitable trade-off between gas intake and water loss via leaf stomata,
decreasing CO2 concentrations would have presumably favored plants with more efficient
stomatal control mechanisms and high potential maximum stomatal conductance (Robinson
1994). Compared to gymnosperms and pteridophytes, angiosperms usually have higher
maximum stomatal conductance and shorter duration of stomatal opening and closing cycles
23
(Robinson 1994). McElwain et al. (2005) demonstrated a correlation between declining
atmospheric CO2 and increase in angiosperm species richness and, conversely, a positive
relationship between atmospheric CO2 and the richness and abundance of gymnosperms and
pteridophytes. They suggested that reticulate venation, xylem vessels, and rapid stomatal
control mechanisms would have competitively favored the angiosperms in a lower CO2
environment. This would have been most noticeable in light-limited tropical areas and in
seasonally (but not permanently) arid environments because gymnosperms and pteridophytes
would have been severely limited by declining CO2, especially in dry and light-limited
conditions (McElwain et al. 2005).
Among the less-studied factors at this stage in angiosperm research are the possible roles of
seasonality, light regimes, deciduousness, and nutrient availability. Although seasonality has
been mentioned as a co-factor in theories involving precipitation (Mutterlose et al. 2003), light ,
and CO2 regimes (McElwain et al. 2005), it has never been tested as an independent factor. In
an early theory, Axelrod (1970) stated that angiosperms evolved in areas with seasonal drought.
Somewhat in concurrence with this theory, McElwain et al. (2005) insinuated that seasonal
aridity would have increased the success of angiosperms under low CO2 conditions. Similarly,
the rise of angiosperms in southern Europe during the Early Cretaceous semi-arid phase
(Mutterlose et al. 2003) could potentially be linked with the marked precipitation seasonality in
this region during this time. However, some regions in Africa and Australia that experience dry
summers are abundant in endemic conifers (Midgley and Bond 1989), although this may be
explained by low soil-nutrient availability in these areas. On a global scale, Donnadieu et al.
(2006) found that changes in global geography from the Early to Mid-to-Late Cretaceous would
have likely caused a substantial decrease of the seasonal temperature cycle, which may or may
not have affected the broad-scale angiosperm success.
The significance of the difference in light regime between low and high latitudes has been
studied in association with plant adaptations such as deciduousness and ever-greenness (Herman
and Spicer 2010) and not so much in terms of competition of angiosperms with other plant types.
References have been made to the potential adaptability of angiosperms to low-light
environments (Coiffard et al. 2007, Feild et al. 2004), however, it remains undetermined how
(and if) this would apply in conditions of highly seasonal light, as in high latitude regions. There
are a few details in the history of angiosperm spread that remain largely unexplained by other
24
theories, and they may or may not be linked to light regimes. One fact is that angiosperms were
relatively slow to reach high-latitude regions, despite the probable temperature and CO2
increases, and favorable precipitation (Hickey and Doyle 1977, Retallack and Dilcher 1986).
Even after angiosperms reached these habitats towards the end of the Cretaceous, conifers kept
their position as the dominant plant group (Drinnan and Crane 1990, Scott and Smiley 1979),
which was not the case in many low-latitude examples. Another mystery is why angiosperms
emerged from equatorial regions as opposed to mid-latitudes and/or boreal regions, where the
climate considerations could have also been favorable (Feild et al. 2009).
Just as unclear is the function of deciduousness in polar vegetation during the Cretaceous. Leaf
physiognomic studies found that most polar vegetation was deciduous, including the conifers
(Equiza et al. 2006, Falcon-Lang et al. 2004), which may have been an adaptation to the high
arctic light regime (Herman and Spicer 2010). Interestingly, Royer et al. (2005) found no clear
advantage of deciduous species over evergreens; in fact their experimental results suggested that
evergreen species would be more strongly favored in high latitude environments. They also
found that trees grown in high CO2 conditions were more affected by the polar summer
photosynthetic rate decrease, which was a feature of polar forests. This effect may point to a
connection between CO2 fluctuations during the Cretaceous and plant-dynamics in the polar
regions. However, much more research is needed on this topic to determine concrete
relationships between the deciduous tendency of Cretaceous high-latitude vegetation and
environmental variables, as well as the potential significance of this phenomenon to angiosperm
establishment.
Among the more current developments in unraveling the angiosperm mystery is the examination
of the role of soil types and nutrients. The angiosperm affinity for nutrient-rich habitats has been
previously noted in early studies of ruderal tendencies of angiosperms and competitive nature of
gymnosperms (Regal 1977, Retallack and Dilcher 1981). In a recent study, Berendse and
Scheffer (2009) cited angiosperm interaction with the soil nutrient supply as a potential critical
element in pushing angiosperms to dominance. Because angiosperms typically have relatively
high growth rates, they would benefit more from an increase in nutrients in the soil than
competing plants while at the same time producing more easily decomposable litter which would
increase soil nutrients. Therefore, angiosperms would have created a positive feedback which
25
would have helped them in competing against gymnosperms and other slower-growing plant
types (Berendse and Scheffer 2009).
Other more complex hypotheses regarding angiosperm spread and rise to dominance have been
emerging recently and they involve other factors as well as combinations of factors and their
interactions and feedbacks. One study found that high atmospheric oxygen tends to increase
plant sensitivity to water stress (Rachmilevitch et al. 1999) and this may have been a factor in the
Late Cretaceous which was a period of high O2/CO2 ratio in the atmosphere (Beerling et al.
2002). Another study notes the importance of including mycorrhizal evolution and function in
symbiosis with both gymnosperm and angiosperm tree roots in the current representation of the
carbon cycle to understand the Earth’s climate and vegetation history (Taylor et al. 2009).
Llorens et al. (2009) looked at the interaction of CO2-rich atmosphere and seasonal variation in
light at high latitudes with water use efficiency of Cretaceous conifers, finding that higher CO2
tended to improve water use efficiency while leaving transpiration unaffected. Although their
results provide some insight into the effect of rising CO2 on Cretaceous polar forests, further
research is needed to understand the integration of angiosperms in the ecosystem. Another
recent find is that the evolution of leaf vein density in angiosperms may have caused a surge in
photosynthetic capacity by increasing the ability of assimilating CO2; this would have inevitably
caused an increase in angiosperm competitiveness between 140 and 100 Ma (Brodribb and Feild
2010).
Many developments on the Cretaceous vegetation topic are fairly new and have yet to be
integrated and re-examined critically from a different perspective. It is the goal of my work to
test some of the discussed hypotheses within a DGVM modeling framework and provide new
insights on angiosperm spread.
2.3 A Place for Modeling in Paleoecology: Building a Case for LPJ
Lund-Potsdam-Jena (LPJ) model, is a process-based dynamic global vegetation model (DGVM)
that incorporates large-scale terrestrial vegetation processes and land-atmosphere carbon and
water cycling. This model is currently used to study ecosystem dynamics and interactions
between vegetation and atmosphere in terms of biophysical and biochemical systems. Based on
26
the BIOME family of models (Haxeltine and Prentice 1996, Kaplan et al. 2002), LPJ takes on
many features from these models, including structure of the model and bioclimatic limits of plant
functional types (PFTs). The model includes parameterization involved in plant establishment,
competition, biomass turnover, mortality, and fire disturbance. LPJ simulates dynamic responses
in ecosystem processes including photosynthesis, transpiration, resource competition, turnover of
organic matter in soil, litter, and vegetation, and fire disturbance (Sitch et al. 2003). For each
grid cell (0.5º x 0.5º), the model outputs the annual amount of soil, vegetation, and litter carbon
(in gC/m²yr), net primary and ecosystem production (in gC/m²yr), burned biomass, runoff,
heterotrophic respiration, actual evapotranspiration (mm/yr), and foliar projective cover (% of
grid occupied by each PFT). The desired climate data (temperature, precipitation, and
atmospheric CO2 level) are input into the model, but are not directly coupled with the model.
Therefore, feedback of plant processes onto climate is not simulated by LPJ. However, LPJ can
be coupled with climate and earth system models if desired (Sitch et al., 2003).
Numerous studies have applied DGVMs to examine current vegetation dynamics and simulate
potential scenarios for the future. LPJ has proven to be a valuable tool in studying vegetation
cover distributions and feedbacks between climate and the biosphere. Many studies using LPJ
reported that the results were in good agreement with observational data in most cases: Sitch et
al. 2003, Gordon et al. 2004, McCloy and Lucht 2004, and Cowling et al. 2007, to name a few.
However, researchers have only recently begun to explore the potential of DGVMs in answering
ecological questions from the pre-Holocene (>10 ka) time periods. LPJ was used in a few such
studies. For example, Kaplan et al. (2002) included LPJ simulations in modeling global carbon
storage since the Last Glacial Maximum (~ 21 ka BP). In another study investigating the CO2
increase between 8 ka BP and the pre-industrial time, LPJ was used as a component of a coupled
Carbon Cycle Climate Model (Joos et al. 2004). Lunt et al. (2007) went further back in time to
explain the role of climate in the expansion of C4 grasses during the Late Miocene (~ 9-7 Ma).
Shellito and Sloan (2006) also looked at C4 grasses, along with soil texture and atmospheric
partial pressure of CO2 in photosynthetic calculations for the Eocene period (~ 56 – 34 Ma).
Other than that, however, there have been no similar attempts to use LPJ or other DGVMs to
simulate the vegetation cover in the deep-past.
In their 2006 paper, Shellito and Sloan argued for the possibility of successful use of DGVMs for
paleoclimate studies, emphasizing the great potential of improving paleoclimate simulations by
27
adding more detailed lower boundary conditions (namely the ground cover) through DGVMs.
Although they recognized the challenges in using DGVMs, which were developed for current
climate and vegetation conditions, for paleo-applications, they maintained that the models were
still valuable provided that the results are interpreted appropriately. Furthermore, DGVMs,
particularly LPJ have several features that make them flexible and particularly appropriate for
this type of application especially since some of the outlined problems can be addressed with
relatively unsophisticated model modifications. Shellito and Sloan (2006) looked at the usage of
DGVMs as a means of improving climatic simulations by adding a more accurate and dynamic
representation of the biosphere, which responds to (and produces feedbacks on) factors such as
atmospheric CO2 concentrations, temperature and precipitation. In my study, however, we
venture further to show that DGVMs can be used to study the vegetation itself and help to
explain some of the patterns observed in the fossil record.
The following discussion addresses some of the key concerns regarding DGVM usage in paleo-
modeling and how they can be remedied through proper contextualization of the research they
are used for and through model improvements.
2.3.1 Modeling Simplification and Validation
Over-simplification of reality has been repeatedly identified as a problem with modeling in
general (Allen and Fulton 2010, Claussen et al. 2002). Although it is true that models have to
assume a certain degree of simplification at the expense of fine-scale features of a real
ecosystem, they are still able to represent the broad-scale processes, for which they are usually
intended (Adams et al. 2004, Hughes et al. 2006, Woodward and Beerling 1997). In fact, in
many cases, simplification actually enhances the model’s practicality because complicating the
model can lead to increased computation time and increased error without necessarily improving
the accuracy of the output (Ciret and Henderson-Sellers 1998, Cramer et al. 2001, Sitch 2000).
In our case, the simplification is one of the features that allows for the use of the model for
paleo-simulations as LPJ focuses on the functional aspects of vegetation and the way vegetation
relates to its environment, which remain relatively constant through time (Shellito and Sloan
2006). It does not simulate details such as colour, shape, and specifics of the internal structure,
which can differ significantly from plant to plant and in different time periods causing problems
28
for modeling, but not necessarily make a difference to the general function of the plant within its
habitat.
One simplification in LPJ is the use of plant functional types (PFTs) to represent its vegetational
component. A PFT is a fairly broad group of diverse species of vegetation with similar
morphological and physiological traits that would respond similarly to changes in environmental
factors (for example, boreal needleleaf evergreen trees) (Gitay and Noble 1997, Lavorel et al.
2007, Lavorel and Garnier 2002). The plant characteristics and bioclimatic limits represented in
the parameterization scheme of PFTs are the ones assumed to be most relevant to plant growth
and survival across various environmental conditions (Gitay and Noble 1997), although further
development is still needed to improve plant representation (Sitch et al. 2003, Smith et al. 1997).
The current plant parameterization scheme in LPJ encompasses some of the key plant traits
related to carbon and water cycling as well as response to temperature and light levels, which are
just as applicable to past ecosystems as they are to the present. Thus, in using this scheme, we
can make inferences about general changes in land cover patterns with respect to environmental
variables. However, this scheme would not be appropriate for studying the behaviour of specific
plant species or biodiversity, or for conducting simulations on a local scale. Although in any
case, it would arguably not be possible or worthwhile to model the response of individual species
to environmental change using this type of model, for any time period (Cramer 1997), given its
spatial and temporal resolution and the broad nature of the plant features that it includes.
Therefore, for the intended purposes of the DGVM, the PFT scheme is a reasonable system to
use as it is appropriate for the level of detail of the model structure itself, and it also allows for
enough flexibility in plant description to make it functional for a wide range of applications,
including paleo-studies, without being so broad that it takes away from the model’s ability to
produce meaningful results (Lavorel et al. 2007).
2.3.2 PFT Modification
Shellito and Sloan (2006) argued that even with the original scheme of PFTs, which were
derived from and intended for modeling modern vegetation, it is reasonable to model past
environments as it would be fair to assume that in a rough sense, the general functional role of
plant groups would not have significantly changed in most cases. Given that the physiological
29
characteristics of major plant groups such as ferns, conifers, and angiosperms have remained
fairly stable throughout time (Beerling and Woodward 1996), it can be assumed that past
vegetation types would have had similar responses to environmental factors such as atmospheric
carbon dioxide concentration, moisture levels, and sunlight (Shellito and Sloan 2006).
Nevertheless, Shellito and Sloan (2006) did identify the application of modern plant types to
paleo-simulations as a disadvantage and potential source of error.
One of the main advantages of LPJ in the context of my study is that, as noted in the original
documentation for the LPJ model and in Sitch et al. (2003), the given PFT classification can be
modified and developed further. Shellito and Sloan (2006) commented that the ability to add,
remove, and change PFTs in a DGVM is an ideal feature for paleo-modeling, although they also
pointed out the potential difficulty of parameterizing past vegetation, especially in cases when
species from the past have no living relatives in the present.
In a study preceding this report, Cowling and Gousseva (2010) endeavored to modify the PFT
parameterization in LPJ, adjusting parameters in the original scheme as well as adding new PFTs
that would have been relevant in past time periods. The original LPJ contains 10 PFTs: eight
woody and two herbaceous plant functional types. These are defined by a set of thirty four
physiological parameters, phenological parameters, and bioclimatic limits. Woody PFTs include
two tropical, three temperate, and three boreal; herbaceous PFTs include a C3 temperate
perennial grass, and a C4 tropical perennial grass. Refer to Appendix 1 for a complete list of
original PFTs. Cowling and Gousseva (2010) added seven new PFTs to the model: Bryophytes,
Arborescent Lycopods, Ferns, Horsetails (giant), Tree Ferns, Cordaites (ancestors of modern
conifers), and Tropical Needleaf Evergreen Trees. Of these, Bryophytes and Ferns are
herbaceous PFTs. See Appendix 2 for the final parameter values of all PFTs after modification.
Also, refer to Cowling and Gousseva (2010) for the PFT modification methodology and
justification.
30
Figure 7: LPJ-simulated vegetation composition in Carboniferous mire forest and higher altitude
regions compared to fossil record.
The study documented in this report is, in part, a follow-up to the PFT modification project,
aimed to utilize the new scheme in a paleo-application. Part of the scheme has already been
tested in a short experiment for the Carboniferous period (359 – 299 Ma), simulating mire forest
(peat bog) vegetation and a higher altitude forest (Cowling and Gousseva 2010). Simulation
results were in generally good agreement with fossil data (Figure 7), where both the mire forest
and the higher altitude forest had the vegetation composition, relative abundance and dominance
consistent with fossil observations. Although there was one notable discrepancy with the
Lycopods appearing in cooler and dryer environments (which is not consistent with fossil data),
the overall result or the experiment was promising, showing great potential of the method for
future development and further application.
31
Overall, the modified PFT scheme opens up opportunity for simulating vegetation representative
of past ecosystems, thereby minimizing the potential error in paleo-climate simulations and
allowing for research aimed specifically at studying paleo-vegetation patterns.
2.3.3 Experiment Types
In light of the inevitable limitations of using the DGVM approach for research of past
ecosystems, one important aspect of minimizing error is ensuring the proper interpretation of
results as well as the appropriateness of the experiments conducted. Special attention should be
paid to the scale of the experiments in time and space.
In a paleo-study, it may be tempting to use LPJ for long-running simulations (spanning
thousands or even millions of years, for example) and make conclusions based on the obtained
time-series. However, the deep-time dimension is not currently represented in LPJ in terms of
plant evolution, large landscape changes (i.e. orogenic processes, tectonic movement), and other
important long-term processes such as silicate weathering. These aspects would have to be
addressed in some other way, perhaps through the use of a different model or another method.
Similarly, one has to keep in mind the scale of the model processes in the interpretation of
simulation results. For instance, although model experimentation with parameter values may
provide some hints regarding the significance of evolving morphological plant traits, the main
mechanics of the model deal with plant response to climate and environmental variables, and
plant competition. Therefore, the discussion of results as well as the design of the experiment
itself must be mindful of the purpose of the model, avoiding making conclusions regarding
things that the model was not intended for.
One way in which LPJ can be successfully used is for testing the sensitivity of PFT
establishment, survival, and growth to changes in climate (temperature, precipitation,
atmospheric CO2 concentration), as well as light regime, soil texture, addition and removal of
other PFTs, and other mid-scale environmental variables. Coupled with a general circulation
model, LPJ can also be used to explore carbon cycling and changes in atmospheric chemistry and
plant-climate feedbacks (Shellito and Sloan 2006), which are more global processes. In such an
32
experiment, simplifications would need to be made for representing global plant distribution, and
paleogeography would likely have to be represented in some way.
In this project, the focus is on the potential effect of environmental variables on plant
composition, without significant consideration for plant feedbacks on climate, large-scale
temporal processes such as changing paleogeography, or fine-scale plant dynamics such as
species-specific interaction. For this purpose the simulations were conducted on a medium scale
using an arbitrary land patch, with alternating patch location, climatologies, soil, and allowable
PFTs.
33
Methods
3 Introduction – Modeling Methods
A number of experimental simulations were carried out using the LPJ DGVM. A summary of
the relevant features of the model is found earlier (section 2.1), and in Sitch et al. (2003). The
goal of the simulations was to establish the effect of several variables on species composition
and dominance including temperature, precipitation, atmospheric CO2, seasonality, angiosperm
height/structure, soil type, light regime, and deciduousness. The main focus was alteration of
fractional land cover between angiosperms and warm-adapted conifers with a second view
towards ecosystem NPP.
3.1 Data
Simulations were conducted for an arbitrary experimental parcel of land, an area representing a
typical tropical lowland with high solar radiation and medium-high precipitation levels. The
region location corresponds to the present day Amazon Basin (South America) bound by
coordinates 3º N to 7.5º S and 63º W to 73º W. In experiments dealing with light regimes, the
latitude was changed to 24.5 to 35 º N, 64.5 to 54 º N, and 85 to 74.5 °N. This study does not
consider effects of regional topography or large scale paleogeographic elements such as tectonic
movement; these aspects are kept constant in all simulations. Modern solar constant and global
insolation regime were used.
Control climate was obtained from an observational global database for a period between 1901
and 1998 (New et al., 2003). The Amazon Basin region currently experiences a mean monthly
temperature of 24.3ºC and 202 mm of mean monthly precipitation (based on 1901-98 data)
(Ramankutty and Foley, 1999). Atmospheric CO2 was kept at 2000 ppm for most simulations
(testing the effect of factors other than CO2 level). The control soil type parameters are typical
of a tropical forest and were derived from Zobler’s modified soil database (Post and Zobler
2000).
To create the control climatology dataset, a 1000 year spin-up simulation was done using the
1901 – 1920 portion of the modern dataset to ensure a realistic representation of carbon in the
34
soil, vegetation, and litter. Hence, all simulations begin with the same spin-up protocol. The full
98-year database was manipulated in post-spin-up scenarios.
The climate anomaly approach was used to construct data for experimental simulations, where
each anomaly was applied independently or interactively with other anomalies. Soil type,
latitude, allowable PFTs, and PFT parameters were modified according to experimental scenario.
3.2 Plant Representation
LPJ currently has no way of representing reproductive characteristics of plants. The main
differences between plant groups are expressed through leaf type (i.e. needle versus broadleaf
versus grass), traits related to nutrient and moisture cycling, phenology, and other traits that
would determine the plant’s function within the ecosystem. Therefore, simulated competition
was not strictly between gymnosperms and angiosperms (for example), but between needle-leaf
tree forms with physiological parameters that would have encompassed most gymnosperms
during the Cretaceous period (not including ginkgos and cycads, which are not needle-leaved),
and broad-leaf tree forms which would have been representative of most angiosperm tree species
at that time. This type of generalization is not necessarily a disadvantage as it is impossible to
effectively represent the large range of gymnosperm and angiosperm plant forms that existed
(which may not be a useful approach), and most determinant traits for competition are preserved
in this type of scheme. In the future, however, including reproductive traits in LPJ wound help
to improve realistic plant representation.
3.3 Experimental Design
A number of simulations were conducted to test vegetation response to changes in each chosen
climate and phenology parameter. Climate variable values were chosen to represent a realistic
range of conditions during the Cretaceous period as determined from previous studies of fossils.
LPJ distinguishes between climate-adapted plant subgroups, for example, Boreal versus
Temperate and Tropical Broadleaf Evergreen Trees. However, considering the fact that
Cretaceous high-latitude climate was much more mild than today’s, to avoid complicating the
35
analysis by adding extra variables, the number of allowable PFTs was kept small, between 3 and
5, in all simulations and the PFT types were kept the same in simulations of different locations
(i.e. warm-adapted needle-leaf trees were used in tropical as well as high-latitude simulations),
unless the experiment specifically called for alteration in allowable PFTs (i.e. Deciduousness
simulations) and/or PFT parameters (i.e. Grass simulations). Therefore, in the following
sections, it is assumed that all tree PFTs are adapted to tropical climate in that they do not
represent specifically boreal or temperate species. The following experimental set-up was used
for testing sensitivity to each variable.
3.3.1 Experimental Protocol
The Control simulation assumes a warm and moderately wet Cretaceous climate at tropical
latitude, with annual temperature and precipitation being similar to todays. Atmospheric CO2
was set to 2000 ppm, corresponding to many estimates of mid-Cretaceous atmospheric
characteristics (Bice and Norris 2002). All scenario simulations were compared to this Control
protocol (Table 1).
Table 1: Control climate and vegetation specifications.
Scenario Allowable PFTs Latitude Climate Specifications
Control - Ferns
- Needle-leaf
Evergreen Trees
- Broad-leaf
Evergreen Trees
3 °N to 7.5 °S Variable Annual
Average
Unit
CO2 2000 ppm
Soil Temp. 24.3 °C
Mostly fine,
non-vertisol
Precip. 2424 mm /yr
The sensitivity of plant composition to temperature, precipitation, and atmospheric CO2 was
tested in the following experiments (Table 2) by systematically altering the input dataset of the
36
climate variable in question in each simulation. Other than the anomalies indicated in the table,
all specifications were equivalent to Control.
Table 2: Temperature, Precipitation and Atmospheric CO2 experimental simulation anomalies.
VSC represents Variable Subjected to Change in each simulation of the experiment and Variable
Levels indicate the degree of change in each simulation.
Experiment VSC Variable Levels Unit
Temperature Temperature +10, +5, -5, -10 (Anomalies) ºC of Control values
Precipitation Precipitation 40, 70, 130, 160 (Anomalies) % of Control values
CO2 Atmospheric
CO2 Level
200, 1000, 3000, 5000 ppm
Two experiments, Grass and Grass2, were conducted to test the effect of the height of
angiosperms in competition with gymnosperm trees. Because plant height cannot be directly
manipulated in LPJ, the structural designation of the Broad-leaf Evergreen Tree PFT was
changed from “tree” to “grass” in Grass and Grass2 to mirror a decrease in height, while all
other parameters were kept equivalent to the Broad-leaf Evergreen Tree. In Grass2, the
maximum crown area of Needle-leaf Evergreen Trees was decreased to 5m (from 15m) to
represent a more open canopy to further test the competition dynamics. The Grass scenario was
also simulated with lower precipitation with a negligible difference in results (results not shown).
The Light experiment was intended to test the individual role of the latitude-specific light regime
in angiosperm – gymnosperm competition. Annual hours of light and the seasonal light
variations are linked to the latitude in LPJ. Therefore, to compare vegetation response in middle
and high latitude light regimes, simulations were conducted at three latitudinal regions: 24.5 to
35 ºN, 54 to 64.5 ºN, and 74.5 to 84 ºN, with all other environmental variables kept constant
(equivalent to Control).
37
In addition to testing independently for sensitivity to light, the following scenario protocols were
simulated, representing a few of the potential climates that could have been possible in the high
latitudes during the Cretaceous. The goal was to obtain a general picture of possible vegetation
composition under such conditions. All interactive High Latitude Scenarios (Table 3) were
simulated with PFTs and soil data from Control with 2000 ppm atmospheric CO2 concentration.
The summer and autumn months referred to in HL4 include May, June, July, August, September,
and October (MJJASO); the winter and spring months include November, December, January,
February, March, and April (NDJFMA).
Table 3: Interactive High Latitude scenario protocol.
Scenario Description Latitude Climate Anomalies
HL1 High latitude, relatively cold and
dry
54 to 64.5 ºN Temperature: -14.3 ºC
Precipitation: 70%
HL2 Arctic latitude, cold, Control
precipitation
74.5 to 84 ºN Temperature: -14.3 ºC
HL3 Arctic latitude, warmer than HL1
and HL2, Control precipitation
74.5 to 84 ºN Temperature: -7 ºC
HL4 Arctic latitude, seasonal
temperature, Control precipitation
74.5 to 84 ºN Temperature: -5 ºC in summer and
autumn months, -14.3 ºC in winter
and spring months
The Soil experiment, which tested the vegetation response to soil type, consisted of two
simulations at low latitude with protocol identical to Control with altered soil type. The major
soil type from Control (fine non-vertisol soil) was changed to coarse sandy soil, and organic soil
in the respective simulations. Coarse sandy soil has a relatively low water-holding capacity and
relatively high thermal diffusivity compared to the original fine non-vertisol soil. Organic soil
has a high K (mm/day) parameter in the percolation equation, relatively high volumetric water
holding capacity and low thermal diffusivity compared to fine non-vertisol soil (Sitch and Smith
38
2003). The two chosen soil types were chosen for their most extreme water-holding and
diffusivity characteristics out of the soil types represented in LPJ.
The Seasonality experiment explored the effect of two types of climate seasonality. The first
type of seasonality (HD/WW) has a relatively hot and dry summer/autumn half of the year
(MJJASO), and a relatively warm and wet winter/spring half of the year (NDJFMA). The
second seasonality (CW/WD) has a relatively cool and wet summer/autumn season and a warm
and dry winter/spring. Of course other types of seasonal climates could have been possible
during the Cretaceous. However, this experiment was intended as an introductory inquiry aimed
to test the general effect of seasonality (if any), with possible further investigation in mind. The
temperature and precipitation anomalies used in the Seasonality experiment are described in
Table 4. The experiment was conducted at low latitude (3 °N to 7.5 °S), with Control atmospheric
CO2, allowable PFTs, and soil input.
Table 4: Seasonality experiment protocol.
Scenario Temperature Anomalies Precipitation Anomalies
HD/WW1 Summer/autumn: +5 ºC
Winter/spring: -5 ºC
Summer/autumn: 80%
Winter/spring: 120%
HD/WW2 Summer/autumn: +10 ºC
Winter/spring: -5 ºC
Summer/autumn: 80%
Winter/spring: 120%
HD/WW3 Summer/autumn: +10 ºC
Winter/spring: -5 ºC
Summer/autumn: 60%
Winter/spring: 140%
CW/WD1 Summer/autumn: ---
Winter/spring: -5 ºC
Summer/autumn: 120%
Winter/spring: 80%
CW/WD2 Summer/autumn: +5 ºC
Winter/spring: -5 ºC
Summer/autumn: 120%
Winter/spring: 80%
CW/WD3 Summer/autumn: ---
Winter/spring: -5 ºC
Summer/autumn: 140%
Winter/spring: 60%
39
The Deciduousness experiment (Table 5) includes two simulations testing for sensitivity of plant
composition when Needle-leaf Trees are made deciduous. In the first scenario (DecidNT), the
Needle-leaf Trees were changed to deciduous with all other PFTs environmental characteristics
equivalent to Control. The second scenario (DecidNTcold) has the same PFTs as DecidNT, but
it imitates more realistic, colder conditions of the higher latitude areas. The two scenarios were
conducted at 54 to 64.5 °N latitude with Control soil and atmospheric CO2. The results of the
two scenarios were compared to an earlier simulation from the Light experiment at 54 to 64.5 °N
latitude with PFTs and all other specifications equivalent to Control (here, this simulation is
named EvNT for comparison purposes).
To convert a PFT to “deciduous”, the phenology parameter was set to “summergreen”, meaning
that the plant would shed its leaves for the cold season. A deciduous PFT in LPJ keeps its leaves
for the number of months specified by the leaf longevity parameter, or for a maximum of 210
days (approximately 5 months) if the specified leaf longevity is greater. The plant begins to shed
its leaves when daily temperature falls below 5 °C and keeps them off until at least 75 days after
the middle day of the coldest month (Sitch and Smith 2003).
Table 5: Deciduousness experiment PFTs and climate anomalies.
Scenario Allowable PFTs Climate Anomalies
EvNT - Ferns
- Needle-leaf Evergreen Trees
- Broad-leaf Evergreen Trees
---
DecidNT - Ferns
- Needle-leaf Deciduous Trees
- Broad-leaf Evergreen Trees
---
DecidNTcold Same as in DecidNT Temperature: -10 ºC
40
Three interactive Deciduous Scenarios with 3 PFTs and five interactive scenarios with 5 PFTs
were also conducted to further test the effect of deciduousness in plant competition. The 3-PFT
scenarios (Table 6) were carried out at 64 to 64.5 ºN latitude with seasonal temperature (-5 ºC
anomaly in summer/autumn and -10 ºC anomaly in winter/spring), with Control soil,
precipitation, and CO2. The third simulation (ND/NE) tested the competition between Needle-
leaf Deciduous Trees and Needle-leaf Evergreen Trees in the absence of broad-leaf trees.
Table 6: 3-PFT deciduous interactive scenario PFTs.
Scenario Allowable PFTs
ND/BE - Ferns
- Needle-leaf Deciduous Trees
- Broad-leaf Evergreen Trees
ND/BD - Ferns
- Needle-leaf Deciduous Trees
- Broad-leaf Deciduous Trees
ND/NE - Ferns
- Needle-leaf Deciduous Trees
- Needle-leaf Evergreen Trees
The 5-PFT scenarios (Table 7) included broad-leaf and needle-leaf trees with both evergreen and
deciduous phenologies under Control soil, precipitation, and CO2. The first four scenarios (D0,
D30, D60, and D80) were conducted under the same seasonal temperatures as in the previous 3-
PFT scenarios temperature (-5 ºC anomaly in summer/autumn and -10 ºC anomaly in
winter/spring) in varying latitudes. The D0warm simulation had the same warm uniform climate
as Control.
41
Table 7: 5-PFT deciduous interactive scenario PFTs and latitudes.
Scenario Allowable PFTs Latitude
D80 - Ferns
- Needle-leaf Evergreen Trees
- Needle-leaf Deciduous Trees
- Broad-leaf Evergreen Trees
- Broad-leaf Deciduous Trees
74.5 to 84 ºN
D60 Same as in D80 54 to 64.5 ºN
D30 Same as in D80 24.5 to 35 ºN
D0 Same as in D80 3 °N to 7.5 °S
D0warm Same as in D80 3 °N to 7.5 °S
3.4 Analysis of Model Output Values
An average of each output variable (i.e. NPP) was taken for the entire region (from output for all
grid cells) for each year in each scenario. Because simulations tend to stabilize towards the end
of each run, final results were obtained by taking an average of the last 20 years of the simulation
results. Model output values that were analyzed included: fractional cover taken up by each
PFT, net primary production (NPP), litter carbon, vegetation carbon, fire carbon, soil carbon, net
ecosystem production (NEP), and heterotrophic respiration (Rh), in G/m2 of carbon. These
variables are typically used in analyses in vegetation modeling studies (Bondeau et al. 2007,
Morales et al. 2007, Scholze et al. 2008, Scholze et al. 2003). Because plant composition and
dominance are of the most interest in this study, PFT fractional cover output was given the most
consideration. NPP results were also taken into account for gauging the overall productivity of
the plant assemblage under given conditions. 20-year averages of different simulations are
compared to determine the effect of the variable in question.
42
Results and Discussion
4 Introduction
The LPJ simulation results in this study have many implications for theories of angiosperm
emergence and spread, as well as for the future use of LPJ in similar paleo-ecological studies. In
this section, the simulation results will first be discussed in the context of angiosperm expansion
during the Cretaceous, providing insight from a modeling perspective. Then, a discussion of the
methodology itself will be presented, outlining the strengths and weaknesses with the current
state of the model, and suggested improvements.
4.1 Concepts and Abbreviations
To avoid wordiness, the following abbreviations are used in the next sections.
Table 8: Abbreviations used in discussion.
PFT Abbreviation
Needle-leaf Evergreen Trees NE
Needle-leaf Deciduous Tree ND
Broad-leaf Evergreen Tree BE
Broad-leaf Deciduous Tree BD
Short Broad-leaf plants (grass structure) BG
Net Primary Production in grams of carbon per square meter per
year (gC/ m2yr)
NPP
It is important to note that LPJ predicts vegetation composition without considering land use by
humans (i.e. “potential” vegetation). This is a beneficial feature for Cretaceous studies in which
human land use is not applicable. Although LPJ does simulate fire disturbance, other types of
disturbance such as herbivory, disease, seismic activity, and wind storms are not simulated.
43
4.2 Early Angiosperms in a Gymnosperm-Dominated Forest
4.2.1 Structure and Traits of Early Angiosperms
Many authors agree that the earliest angiosperms were small, herbaceous plants with weedy
growth strategies (Herman 2002, Hickey and Doyle 1977, Sun et al. 2007, Taylor and Hickey
1992, Taylor and Hickey 1996). They were likely fast-growing, with a short leaf lifespan and
high photosynthetic rates (Royer et al. 2010), and they did not initially infiltrate the forest
interior, but dominated in open, disturbed habitats such as river channel beds (Farley and Dilcher
1986, Herman 2002, Taylor and Hickey 1996). Field et al. (2004) propose that angiosperms
started off as shrubby woody plants that inhabited frequently disturbed low-light environments.
However, there are very few specimens of angiosperm wood that were found dating before the
late Cretaceous (Ramanujan 1972, Wheeler and Baas 1993). Angiosperm wood was most rare in
high latitude regions where most wood came from gymnosperms, clearly suggesting dominance
of gymnosperm trees in these areas. In the tropics, however angiosperm wood was abundant in
many locations by the late Cretaceous (Boucher and Wing 1997, Wheeler et al. 1995).
In the LPJ simulations in this study, angiosperms are represented as broad-leaf trees. Therefore
it is assumed that by the simulated time period angiosperms would have already evolved into
trees at least in the tropics. Therefore the resultant relative vegetation composition in Control
could be a reasonable representation of a warm, moist tropical forest in the later stages of
angiosperm radiation, probably towards the middle or even late Cretaceous, when angiosperms
began to dominate in the forest canopy (Dettmann et al. 1992, Specht et al. 1992, Wheeler et al.
1995).
Two simulations – Grass and Grass2 – were conducted to test the effect of height and structure
of angiosperms on their competition with NEs. In Grass and Grass2, broad-leaf trees were
changed to a grass, with all other parameters kept equivalent to BE. The resulting Grass
simulation yielded 93% cover of needle-leaf trees compared to 12% in Control. The BG-PFT
only occupied 3% of cover, while Ferns took up 4% (Figure 8).
NPP dropped from 1279 gC/m2yr in Control to 1040 gC/m
2yr in Grass and Grass2. This
suggests that BE tend to be more productive than NE given that the total fractional cover of trees
44
is the same in Control and Grass. The drop in NPP is substantial but not too large, implying that
the productivity of NE is still comparable to that of BE, though slightly lower.
Figure 8: Fractional land cover occupied by each PFT, alternating between broad-leaf trees (BE) in
Control and broad-leaf grass (BG) in Grass and Grass2.
In Grass2, the NE maximum crown area was decreased to 5m (from 15m in Control) to give the
grass PFTs more opportunity to obtain light. However, fractional cover of BG remained the
same as in the Grass simulation for all three PFTs, indicating grass is still not able to compete
with trees.
These results show that short, herbaceous broad-leaf plants were considerably less competitive
than needle-leaf trees. However, this interpretation requires careful scrutiny because this result
may be a product of the architecture of the model. LPJ treats grass PFTs differently from tree
PFTs as it ignores the individual structure of grasses (i.e. individual density, leaf carbon mass,
and root carbon mass). It also effectively sets the crown area of grasses to the proportion of the
grid cell that is not already occupied by trees (Sitch and Smith 2003). Thus, the model
0
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BE/BG
45
automatically favors tree PFTs in competition and it is probably meaningless to try to compete
trees with grass.
Although the fractional cover results cannot be used to make definitive conclusions due to the
structure of LPJ, this experiment demonstrates the need to incorporate shrubs (which do not
function as a grass) into the model to improve realistic interpretations of grass-tree ecotones.
This would also greatly enhance LPJ’s applicability to paleo-studies since it would allow for
more accurate representation of smaller, more primitive trees.
Because of LPJ’s design, grass PFTs are inevitably out-competed by trees since they are denied
access to light by the canopy plants (Sitch and Smith 2003). Although this is a problematic
model simplification in many cases, it is arguably still reasonable and perhaps even pertinent in
the early angiosperm case. Fossil evidence indicates that in almost all cases the early herbaceous
angiosperms lived in unstable environments characterized by high light and nutrient availability
and frequent disturbance and they were rarely present in stable forest interiors (Grime 1979,
Retallack and Dilcher 1986, Upchurch and Doyle 1981, Wing and Boucher 1998). For example,
a North American study found that early angiosperms were much more effective at displacing
Frenelopsis conifers at estuary margins, than Pseudofrenelopsis in more stable forest interiors in
the early Cretaceous (Retallack and Dilcher 1986), where both Frenelopsis and
Pseudofrenelopsis were probably trees (Alvin 1982). Furthermore, throughout the Cretaceous
period, angiosperms had relatively small seeds, with almost no change in seed size by the end of
the Cretaceous (Tiffney 1984). Seed size is directly correlated with growth rate in shaded
conditions such as under a forest canopy (Grime 1979) and species with small seeds tend to
establish in disturbed areas and usually display weedy, invasive behavior (Marzluff and Dial
1991). Meanwhile, compared to today’s vegetation, subtropical gymnosperm forests in the early
Cretaceous were more luxuriant and dense (Fontaine 1889, Retallack and Dilcher 1986, Wing
and Boucher 1998), further decreasing the possibility of establishment for weedy herbaceous
angiosperms inside the forest. Therefore, it is very likely that the competition between the
earliest angiosperms and the established gymnosperm forest trees closely resembled the LPJ-
simulated dynamics between NE and BG, where BG is only given a chance to establish in areas
not already occupied by NE, which would have been forest margins and disturbed patches. So
the technical conditioning of LPJ is actually quite consistent with the plant interactions observed
46
in the fossil record, and Grass and Grass2 results may be a fairly accurate picture of an early
Cretaceous gymnosperm forest interior.
4.2.2 Cretaceous Angiosperm Trees and LPJ
The Control simulation (Figure 9) yielded 5.8%, 12%, and 81% of land covered by Ferns, NE,
and BE, respectively. Net primary production was approximately 1279 gC/m2yr. The simulated
NPP is higher than that of a modern tropical forest as estimated from field studies: 747-957
gC/m2
yr (Malhi et al. 2004), and as simulated by LPJ: 968 gC/m2
yr (Cowling and Shin 2006).
The higher Cretaceous NPP may be explained by higher atmospheric CO2 levels and the
significant presence of warm-adapted NE, which tend to be high in productivity, and were not
included in the original set of PFTs in LPJ. Ferns occupy a small fraction of land as understory
plants, which is consistent with a Late Cretaceous forest in which ferns played a lesser role than
in Early Cretaceous forests, especially in low latitudes (Golovneva 2000, McElwain et al. 2005,
Upchurch and Doyle 1981).
As in Control, BE were the overwhelmingly dominant group in almost all simulations, indicating
that they are generally more competitive than NE in a wide range of environments. However,
some aspects of early angiosperm trees are not reflected in the simulations. Firstly, the earliest
angiosperm trees were most likely small to medium trees, with low vascular conductivity and
ample soft tissue (Thayn et al. 1985, Upchurch and Wolfe 1993) and they were probably fast-
growing and highly vulnerable to harsh environmental conditions and disturbance (Wolfe and
Upchurch 1987a). The BE - PFT used in the simulations in this study is representative of
angiosperms past their early evolution stages, already being fairly large trees with developed
vascular systems, and having similar heartwood and sapwood characteristics to needle-leaf trees.
However, compared to modern broad-leaf trees, BE are also assigned a lower maintenance
respiration coefficient, which means that fewer resources are directed towards physical
maintenance of the plant and more are directed towards growth, making BE more “weedy” in
character. Also, compared to modern warm-adapted broad-leaf trees, they are assigned a
shallower root system and lower leaf and root turnover rates to indirectly reflect higher
vulnerability and lower efficiency of growth, thus representing a more primitive tree.
Nevertheless, LPJ does not have explicit parameters to represent the characteristics of early
47
angiosperm wood and so the heightened vulnerability of early angiosperm trees compared to
more evolved angiosperm trees is not adequately expressed in the simulations in this study. This
means that BE cover results are likely to be over-estimated, especially in high-stress simulations
with extreme temperatures and/or precipitation rates and seasonal climates.
4.3 Climate Change and Angiosperm Spread
4.3.1 Temperature Effects
A simulation experiment was conducted to determine the vegetation’s sensitivity to temperature,
with NE, BE, and Ferns growing and competing under varying temperatures in the low latitudes,
with all other specifications equivalent to Control.
Figure 9: Fractional land cover occupied by each PFT, and temperature changes.
The portion of land covered by BE was just over 6% in the coldest simulation (-10 °C), and
increased dramatically in the -5 °C simulation, to almost 73%, and was the highest in the +5 °C
simulation at almost 85%. NE cover fluctuated between simulations: starting at 10% in the -10
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Vegetation Composition Response to Temperature Changes
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48
°C simulation, and hitting the highest point of 20% in the +10 °C simulation. Fern cover
followed a declining pattern with increasing temperature, starting off at almost 52% in the
coldest simulation, dropping dramatically to 7.8% in the -5 °C simulation, and then remaining at
around 5-6% in the three warmer simulations. (Figure 9)
The -10 °C simulation climate does not appear to be favorable for tree PFTs and ferns occupy
most of the cover. As the temperatures increase, ferns become the subordinate plant group while
the tree species dominate. Overall, there is a clear temperature threshold for broad-leaf trees
somewhere between -5 and -10 °C of Control values, which corresponds to 19.3 °C and 14.3 °C
annual average temperatures. This is probably because both BE and NE require 15.5 °C as a
minimum cold monthly mean temperature in LPJ parameterization, while Ferns can handle a
minimum cold monthly mean as low as -5 °C.
There is another threshold between +5 and +10 °C of Control values, which corresponds to 29.3
and 34.3 °C annual average, where BE decrease in fractional cover. This threshold does not
likely have to do with BE bioclimatic limit parameters since BE actually have higher temperature
limits for CO2 uptake than NE, making them slightly more adapted to high temperatures than
BE. Therefore, the decrease of BE in the warmest simulation must be a combined response to
temperature and temperature-affected carbon and water cycling and other processes.
It should be noted that bioclimatic temperature limit parameters are only slightly different for BE
and NE, indicating that both plant groups would be successful under similar temperature
conditions. Therefore, the observed pattern indicates that BE are the dominant and competitively
favorable group as the portion of NE cover is inversely proportional to BE cover. NE are given
competitive opportunity only when the temperature and other related conditions becomes less
favorable for BE.
An extreme and unrealistically high NPP result was obtained for -5 and -10 °C simulations,
likely indicative of a problem in the model with carbon balance calculations with these specific
PFTs under these specific conditions. It appears that this error occurs in isolated cases (given a
specific input combination) because carbon balance output values for most other simulations are
within a reasonable range. Under other conditions, including the three warmer simulations:
Control, +5 °C, and +10 °C , the results are realistic and expected, with NPP decreasing from
1278 gC/m2
yr in Control, to 1212 and 871 gC/m2yr in the +5 and +10 °C simulations
49
respectively. The decrease in productivity between the Control and the +5 °C simulation is not
very notable. However, as it gets too warm, the productivity of the forest as a whole decreases.
Keeping in mind that BE dominance in LPJ simulations is very likely to be exaggerated with
respect to the degree of dominance of angiosperm trees in the Cretaceous (see section 4.2.2), the
main focus of the analysis will be on the relative response of BE cover to temperature change
and the direction of the response rather than the absolute fractional cover values. Overall,
simulation results suggest that the annual average temperature optimum for BE is around 29 °C,
although BEs were also almost as successful around 24 °C annual average. Such temperature
conditions would have been effective around the equator and in the subtropical latitudes during
the warmest part of the Cretaceous (from the Albian to the Coniacian) (Hay 2008, Puceat et al.
2003). At the beginning of this time range, the early herbaceous and shrubby angiosperms began
to replace vegetation in environments with high disturbance and penetrate into forest interiors
(Lidgard and Crane 1990, Lupia et al. 1997). Towards the late Cenomanian and Turonian, as the
global temperatures began to decrease (Hay 2008, Puceat et al. 2003), angiosperm trees began to
dominate in many communities (McElwain et al. 2005). The fact that this period of angiosperm
diversification and spread into new environments coincides with the most favorable temperature
conditions for BE shows that it is possible that temperatures facilitated angiosperm spread. Also,
according to some estimates, during the warmest parts of the mid Cretaceous, temperatures may
have reached as high as 42 °C (Bice et al. 2006), which is even higher than those in the warmest
simulation (+10 °C). According to the results in this study, very high temperatures would have
impeded angiosperm growth. Thus, if temperatures were indeed around 40 °C in the mid
Cretaceous, this may explain why angiosperms did not begin to locally dominate in low latitudes
until the cooling period.
The warm conditions during the mid-Cretaceous may have also facilitated the pole-ward
migration of angiosperms. Because the latitudinal gradient was not very large compared to
today, the middle to high latitude regions were fairly warm during the mid-Cretaceous, some
estimates being as high as 30 °C at 40 °N latitude in the late Albian (Huber et al. 2002).
Therefore, during the warm mid-Cretaceous, there would have been a large latitudinal range of
favorable temperature conditions for the angiosperms and it is not surprising that they had spread
all the way to Alaska (Scott and Smiley 1979) and Antarctica (Cantrill and Poole 2002) during
this time and not in a different time during the Cretaceous. The Temperature experiment results
50
therefore support Morley’s (2003) description of angiosperm pole-ward spread as global
warming probably did provide an opportunity for dispersal into high latitude regions, and this
opportunity was likely essential for crossing continental barriers and for migration into
Antarctica (Cantrill and Poole 2002).
Although they were able to establish in parts of high latitude regions, angiosperms never came to
dominate there (Herman 2002, Scott and Smiley 1979). It is possible that despite the small
latitudinal temperature gradient, it may have still been too cold for them (not favorable for
expansion). Some estimates put high-latitude average temperatures around 10 °C in the mid
Cretaceous (Spicer and Parrish 1990), which would have fallen even lower in the late Cretaceous
(Hay 2008, Spicer and Parrish 1990). Most basal angiosperms were also intolerant to frost (Feild
et al. 2009), and this would have been problematic in most high-latitude regions in conditions
outside of the window of maximum temperatures around the Turonian. Overall, the results from
this study demonstrate that temperature is an important factor in plant growth and competition.
The temperature optimums for Cretaceous angiosperms may have been a determinant factor in
their rise to dominance in low latitudes and their pole-ward spread, as well as a potential
deterring factor for expansion and dominance in the high latitudes.
4.3.2 Precipitation Effects
When the growth of Ferns, NE, and BE was simulated under different precipitation conditions in
the low latitudes (with other specifications kept equivalent to the Control simulation), the results
were similar to the Temperature experiment in the sense that broad-leaf trees are by far the
dominant group under all given conditions (with exception of the -10 temperature simulation).
The results also showed a positive relationship between precipitation and fractional cover of BE,
which started off at 70% cover in the 40% precipitation simulation, and increased to 83% in the
160% simulation. The highest increase in BE cover was between 40% and 70% precipitation
simulations (a jump of 10%), from which point it only increased about 1% with each 30%
precipitation step (Figure 10). This indicates a probable precipitation threshold for BE between
40% and 70% precipitation (of Control values), which corresponds to 970 and 1697 mm/yr
annual average, respectively.
51
NE, conversely, experienced a decline with increasing precipitation, their highest land cover
being approximately 17% in the 40% precipitation simulation. NE land cover decreased to about
13% in the 70% simulation, and kept decreasing by 1 to 2% with every 30% increase in
precipitation in the following simulations. Ferns followed a similar pattern, starting off at 12%
in the 40% simulation, jumping down to 6% in the 70% simulation, and then holding steady at
5.8% for the following three simulations.
Figure 10: Fractional land cover occupied by each PFT, and precipitation changes.
Table 9: Net primary production and precipitation changes.
Simulation NPP (gC/m2yr)
40% 1221
70% 1255
Control (100%) 1279
130% 1311
160% 1308
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Vegetation Composition Response to Precipitation Changes
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BE
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Net primary production (Table 9) increases with precipitation for the first four simulations,
beginning at 1221 gC/m2yr in the 40% simulation and going up to 1311 gC/m
2yr in the 130%
simulation. In the 160% simulation, NPP decreases very slightly to 1308 gC/m2yr, which
probably indicates that the conditions become too wet for the forest to keep increasing in
productivity.
Overall, NPP results indicate that productivity of the forest responds positively to higher rainfall
over the prescribed precipitation range, but this response is not as strong as the NPP response to
temperature changes as NPP values are fairly close in all precipitation simulations. This may in
part be due to another structural characteristic of LPJ, which is not too sensitive to precipitation,
particularly to low precipitation; this is demonstrated by the relatively high NPP value in the
40% precipitation simulation. As well, an earlier LPJ study found that substantial declines in
vegetation begin at precipitation values of around 20% of the Control values (Cowling and Shin
2006).
According to the fractional cover results, vegetation composition also tends to respond more to
temperature variations than precipitation variations, and BE once again appear to determine the
success of the other two plant groups under favorable precipitation. Precipitation does not seem
to be an important driver for vegetation change, although the precipitation response will likely be
more pronounced if more extreme precipitation conditions are assigned (i.e. extremely wet or
extremely dry). From these results, it is difficult to support the proposition that angiosperms
were helped by semi-arid climate in their colonization of the mid-latitude regions (Europe) in the
early to mid Cretaceous (Coiffard et al. 2007), given that BE seem to be relatively more
successful in abundant precipitation. Because BE also did reasonably well in low precipitation,
it is equally difficult to support the hypothesis of Field et al. (2009) that angiosperms were
dependent on wet and humid conditions and that their spread was a result of an increase in humid
environments during the break-up of continents and sea level increase during the mid to late
Cretaceous. Although LPJ is known for its characteristically low response to precipitation
(Cowling and Shin 2006), the closeness of the result values in all precipitation simulations
should not be overlooked as it indicates that while precipitation may have still been a minor
factor in Cretaceous vegetation changes, there were probably other, more important factors at
play.
53
4.3.3 Atmospheric CO2 Effects
In the Carbon Dioxide experiment, atmospheric CO2 values were assigned as constants at 200,
1000, 2000 (Control), 3000, and 5000 ppm, while all other conditions were held constant at
Control values. The pattern observed in the vegetation composition response to increasing CO2
is very similar to the precipitation response, but with more pronounced differences between
simulations. BE were once again the dominant group, starting off at 61% cover at 200 ppm,
jumping to 77% cover at 1000 ppm, and continuing to increase by a few fractional with each
1000 ppm step in the following simulations, reaching as high as 86% in the 5000 ppm
simulation. NE start off at 23% in the 200 ppm simulation, and decreased steadily with
increasing CO2 concentrations, finishing at 7% cover in the 5000 ppm simulation. Ferns also
follow a decreasing pattern, with 13% of land occupied in the 200 ppm simulation, holding
around 6% in the 1000, 2000, 3000 , and 5000 ppm simulations. (Figure 11)
Figure 11: Fractional land cover occupied by each PFT, and atmospheric carbon dioxide
concentration changes.
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Vegetation Composition Response to CO2 Changes
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BE
54
Table 10: Net primary production and atmospheric carbon dioxide concentration changes.
Simulation NPP (gC/m2yr)
200 ppm 477
1000 ppm 1113
Control (2000 ppm) 1279
3000 ppm 1345
5000 ppm 1399
Net primary production increases substantially from 477 gC/m2
yr in the 200 ppm simulation to
1113 gC/m2yr in the 1000 ppm simulation (Table 10). NPP kept increasing by 50 to 100
gC/m2yr in each of the following simulations. This is an expected result, as carbon dioxide is
typically correlated with increasing biomass production and decreasing plant water stress
(DeLucia et al. 1999).
It is notable that in the 200 ppm simulation, NE were fairly prominent at 23% cover. Although
BE were still the dominant group at this level, NE were more competitive in lower CO2
conditions. This is probably because BE are slightly less competitive due to higher canopy
conductance, which would result in decreased water use efficiency at low CO2, allowing for
greater water loss than NE. As CO2 rises, BE out-compete NE considerably. Thus, the
simulation results favor the proposition that high CO2 in the early to mid Cretaceous could have
pushed the angiosperms into communities previously dominated by other plants (Jahren et al.
2001). The decrease in CO2 towards the late Cretaceous may have also hindered angiosperm
expansion in high latitudes, which was already impeded by cold temperatures. However, in LPJ,
the stomatal conductance is the principle parameter dictating the response of plants to CO2 levels
and there could be other factors involved. Somewhat contrary to the results of the Carbon
Dioxide experiment, McElwain et al. (2005) demonstrated a negative relationship between CO2
and angiosperm species richness and abundance, while gymnosperms tended to increase with
rising CO2. They argued that features including reticulate venation, xylem vessels, and more
efficient stomatal control mechanisms would have made angiosperms more competitive in lower
CO2 (McElwain et al. 2005, Robinson 1994). These features are not among those represented in
LPJ and so McElwain’s proposition cannot be ruled out by LPJ simulation results. Therefore,
55
based primarily on the relationship between CO2 and canopy conductance, we can say that rising
CO2 may have helped angiosperm expansion. But to thoroughly test this theory, addition of
other key traits including stomatal control mechanisms and vascular features into the LPJ plant
parameterization scheme would be required. Incorporation of these traits would not only be
useful for the angiosperm application in this study, but it would also improve the representation
of plants in general which would have impacts on simulations of present-day vegetation.
4.4 Role of Soils
Three different soil types were used to examine the effect of soil on vegetation composition: fine
non-vertisol soil (used as Control soil type), coarse sandy soil, and organic soil. All other
specifications were kept equivalent to the Control. Since the particularities of Cretaceous soils
are unknown, the aim of the Soil experiment was to analyze the response of plant composition to
soil type based on basic thermal and water characteristics. Fine non-vertisol soil, which
corresponds to the typical soil type found in majority in modern tropical forests (Post and Zobler
2000), functions as the “medium” soil since its descriptive parameters in LPJ are about average
compared to other soil types (Sitch and Smith 2003). Coarse sandy soil has a comparatively low
water-holding capacity and high thermal diffusivity. Organic soil conversely has a high
volumetric water holding capacity and low thermal diffusivity (Sitch et al. 2003).
The simulation results showed that soil type was not a very important factor in determining
vegetation composition and PFT dominance. BE do only very slightly worse in coarse sandy soil
and in organic soil than in fine non-vertisol soil, with 62% and 61% cover in coarse sandy soil
and organic soil, respectively, as compared to 64% in fine non-vertisol soil. NE on the other
hand, do slightly better in coarse sandy soil and organic soil, with 30.5% and 32% cover,
respectively, compared to 29% in fine non-vertisol soil. Fractional cover of Ferns stayed
constant at 6% in all three soil types. (Figure 12)
NPP response to soil type was also very slight, as values differed only by a few gC/m2yr. Fine
non-vertisol soil yielded the highest NPP, at 673 gC/m2
yr. Organic soil had the lowest result,
666 gC/m2yr, while coarse sandy soil had an NPP value of 670 gC/m
2 yr.
56
Figure 12: Fractional land cover occupied by each PFT, and soil type.
Overall, soil changes did not cause a substantial difference in vegetation as differences in NPP
were almost negligible, and differences in fractional cover of each PFT were also very small.
Fine non-vertisol soil is slightly more favorable for BE. The slightness of the effect may be
partially explained by the fact that soils are not very extensively parameterized in LPJ. Soil
types are primarily defined by their temperature and water-related characteristics such as
diffusivity, percolation and water-holding capacity. Other aspects such as the structure of the
soil profile and nutrient and mineral content are not defined in LPJ, and there is no reason to
suggest that such aspects would not have played an important role in determining plant
composition during the Cretaceous. In fact, a study by Berendse and Scheffer (2009), which was
the inspiration for the Soil experiment, looked at the possibility of a feedback between
angiosperm leaf litter that was comparatively easy to decompose, and the resulting increase in
available soil nutrients which would have then primarily benefited the fast-growing angiosperms
rather than slow-growing gymnosperms. Unfortunately, such a feedback could not be studied
within the LPJ framework since LPJ does not simulate extensive soil processes such as various
rates of decomposition and the resulting nutrient content. Further development of the soil
0
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57
parameterization scheme and processes would be another great advancement for LPJ, which
would make LPJ a more versatile tool. For now, we can say that in terms of energy and moisture
characteristics, the soil types were roughly the same for the assigned PFTs in the Soil
experiment. Although we cannot conclude from these results that soil was not a factor at all for
plant composition in the Cretaceous and further study would be required to determine its effect.
4.5 Light Regime
4.5.1 Light as an Isolated Factor
The effect of light was initially tested separately by altering the latitude (equatorial, around 30
°N, around 60 °N, around 80 °N) with all other factors kept the same as Control, which uses
warm, non-seasonal temperature values.
The vegetation composition response to light was very strong. While the fractional cover of
ferns stayed around 6% in all three simulations, BE and NE cover varied substantially at
different latitudes. BE were still the dominant group in all simulations, but interestingly, BE
cover fell to 51% around 30 °N while NE rose to almost 42%. In the high latitudes, BE cover
increased back up to 67% while NE decreased to 26%. It thus appears that the optimal light
regime for NE in terms of competition with BE is not in the high latitudes, but in middle
latitudes (Figure 13). This may be linked to the higher canopy conductance of BE compared to
NE, making BE more susceptible to water loss. Thus, BE would be more competitive in low
latitudes where plant productivity is supported by abundant light which compensates for the
water loss, but the compensation would be less apparent in middle latitudes.
NPP decreased substantially with increasing latitude, starting off at 1279 gC/m2yr in the low
latitudes, eventually moving down to 488 gC/m2yr around 80 °N latitude (Table 11). This is a
reasonable and expected result since sufficient light is required for photosynthesis to take place
and the amount of hours of light decreases with latitude. Furthermore, in the higher latitudes
(especially in the arctic) light is available in very low quantities for half of the year and it is
mostly diffuse light. Most plants do not grow at all during the winter under these light
conditions, so the growing season is restricted to half (or less than half) of the year. Therefore,
NPP tends to be lower at high latitudes in model simulations.
58
Figure 13: Fractional land cover occupied by each PFT, and light regime changes (expressed
through latitude). Temperature and precipitation conditions are equivalent to Control.
Table 11: Net primary production and light regime changes. Temperature and precipitation
conditions are equivalent to Control.
Simulation NPP (gC/m2yr)
Control (3 °N to 7.5 °S) 1279
24.5 to 35 °N 1044
54 to 64.5 °N 673
74.5 to 85 °N 488
The results from the Light experiment are very noteworthy as they show the effect of light
regime in isolation from other factors (i.e. temperature and precipitation). Light regime is
observed to be a very important factor in determining vegetation composition, and this is an
important consideration for many theories regarding angiosperm emergence and spread. Firstly,
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Control (3 °N -7.5 °S)
24.5 - 35 °N 54 - 64.5 °N 74.5 - 85 °N
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Latitude
Vegetation Composition Response to Light Regime (Latitude) Changes
Ferns
NE
BE
59
it has been previously questioned why angiosperms emerged from equatorial regions as opposed
to mid-latitudes and/or high latitudes, especially since Cretaceous climate would have made for
favorable temperature and moisture conditions for angiosperms in higher latitudes (Feild et al.
2009). Light regime may be the explanation for this phenomenon as the simulation results in this
study indicate that angiosperms thrived primarily in low-latitude light. Around 30 °N, BE and
NE fractional cover was almost level. If BE dominance is exaggerated in LPJ due to the lack of
representation for primitive features of early broad-leaf trees (see section 4.2.2), the needle-leaf
gymnosperm trees in the Cretaceous would have probably dominated in the middle latitudes,
possibly overwhelmingly. Thus, not only would this not have been a favorable environment for
newly-emerging angiosperms, but it is not surprising that it took considerable time for
angiosperms to gain local dominance in these regions after they began to spread across the globe
(Drinnan and Crane 1990).
Another unexplained trend is that despite the probable temperature and CO2 increase events in
the mid Cretaceous along with the increase in precipitation belts (Feild et al. 2009), which should
have helped to propel the angiosperm pole-ward migration, the pole-ward movement was
relatively slow as it took some millions of years for angiosperms to reach the high latitudes
(Hickey and Doyle 1977, Retallack and Dilcher 1986). The light regime was likely a factor that
slowed the spread since angiosperms are less competitive in high-latitude light. Moreover, the
mid-latitude belt, in which angiosperms are the least competitive, may have served as a “barrier”
that took awhile for the angiosperms to overcome. The Aptian-Albian was a time of rapid
taxonomic radiation for angiosperms (Lidgard and Crane 1990, Lupia et al. 1999), which
probably played a large part in their increase in abundance in the mid-latitudes around this time
(McElwain et al. 2005). It is possible that the taxonomic radiation, which would have given
some angiosperm species new niches and adaptations, was needed in order to cross the mid-
latitude belt, and by the mid-late Albian the first angiosperms began to appear in the high
latitudes (Crabtree 1987, Scott and Smiley 1979).
60
4.5.2 Light – Temperature Interaction
To simulate more realistic high-latitude conditions, four climate scenarios were assigned to high
latitude regions, with colder temperatures, and varying precipitation and
temperature/precipitation seasonality (Table 12).
Table 12: High latitude scenario specifications.
Simulation Latitude Climate Anomalies
HL1 54 to 64.5 °N Temperature: -14 °C
Precipitation: 70%
HL2 74.5 to 85 °N Temperature: -14 °C
Precipitation: Control
HL3 74.5 to 85 °N Temperature: -7 °C
Precipitation: Control
HL4 74.5 to 85 °N Temperature: -5 °C in summer/fall,
-10 °C in winter/spring
Precipitation: Control
Control 3 °N to 7.5 °S Temperature: 24.3 °C annual average
Precipitation: 2424 mm/yr annual average
HL1 scenario, representing a cold climate (10 °C annual average) all year round and medium-dry
precipitation conditions (1697 mm/yr), yielded a very low NPP of 26 gC/m2yr (Table 13). Only
Ferns were present, occupying 10% of the land cover. HL2 scenario had a similar cold climate
but with higher precipitation (2424 mm/yr, equivalent to Control), and it was at a higher latitude.
The NPP output for this scenario was even lower than for HL1; only 10 gC/m2yr. Once again,
Ferns were the only plant group present, occupying 5% of the land surface. The temperatures in
HL1 and HL2 must have been lower than the bioclimatic limit temperatures for both BE and NE.
Although Ferns are more tolerant to cold than trees, they did not take up the entire land cover in
the absence of trees, probably because temperatures were mostly below their optimal
temperature ranges for CO2 uptake and photosynthesis. Latitude and precipitation differences
likely account for the change in NPP and fern cover between HL1 and HL2.
61
Figure 14: Fractional land cover occupied by each PFT in high latitude scenarios. Climate
conditions for each scenario are outlined in detail in Table 3 in Methods and in brief in Table 12
(above).
Table 13: Net primary production and high latitude scenarios.
Simulation NPP (gC/m2yr)
Control (3 °N to 7.5 °S) 1279
HL1 27
HL2 10
HL3 530
HL4 522
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HL3 scenario was warmer than HL1 and HL2, with temperatures averaging 17.3 °C annually.
There is a notable increase in forest productivity in this simulation from the colder simulations,
with an NPP result of 530 gC/m2yr. Ferns represented 10% of the vegetative cover, while NE
and BE represented 25% and 54% of the cover, respectively. It should be noted that the
fractional cover of BE in HL3 (54% cover) is substantially lower than BE cover in the 74.5 to 84
°N simulation from the Light experiment (67% cover) in section 4.5.1, which had the same
latitude but higher temperature values (equivalent to Control). But the difference between NE
cover values between HL3 and the 74.5 to 84 °N Light simulation is very small, 25% and 26%
respectively. Thus, temperature decrease seems to have a greater effect on BE than on NE at
high latitude. (Figure 14)
HL4 scenario is a seasonal climate in terms of temperature, with annual average temperature
being close to that in HL3. Interestingly, NPP in HL4 (522 gC/m2yr) is just slightly lower than
that in HL3 (530 gC/m2yr). However, BE cover dropped substantially from 54% in HL3 to 31%
in HL4. Needle-leaf tree cover also dropped to 23%, which is only a difference of 2% from
HL3. Ferns, on the other hand, increase dramatically to 25% cover in HL4. The HL4 result may
be showing a response to temperature decrease because although the annual average temperature
is similar in HL3 and HL4, in HL4 the temperatures are 3 degrees lower than in HL3 in the
winter/spring half of the year. The lower winter temperatures can be impeding the growth of BE.
NE are also impacted as not only do they not take the opportunity to become dominant, but they
also decrease in fractional cover, though slightly. Ferns, on the other hand, do not appear to be
impeded by decrease in temperature and they expand substantially to occupy space taken up by
trees in higher temperature conditions.
To further test the effect of light (as opposed to temperature), two more simulations were
conducted at equatorial latitudes: LL3 and LL4, which had the same climate specifications as
HL3 and HL4, respectively (Figure 15). Interestingly, the differences in vegetation composition
between the HL simulations and their corresponding LL simulations were not very substantial.
NE cover actually went up by 3% while BE went down by 2% in LL3 (compared to HL3),
indicating that NE become slightly more competitive with increasing light in a uniformly cool
climate. However, under an even cooler seasonal climate BE increase slightly (by 2%) in
competitiveness while NE decrease by about 1.5% with in response to increase in light.
63
Figure 15: Fractional land cover occupied by each PFT in high latitude and low latitude scenarios.
LL3 and LL4 have the same climate specifications as HL3 and HL4, respectively. LL3 and LL4
were conducted at equatorial latitudes.
High Latitude Scenario experiment results show that there is a definite interaction between light
and temperature, which affects vegetation composition, however further investigation is needed
to better understand it. There are a few of prominent patterns in the simulation results, which
can provide direction for the next steps in the investigation.
Firstly, light seems to have a considerable impact in uniformly warm temperatures (as seen in the
Light experiment in section (4.5.1), and not as large in low temperatures. This is a particularly
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Ferns
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BE
64
important result for the Cretaceous period, most of which was characterized by uniformly warm
temperatures and a small latitudinal temperature gradient (Hay 2008, Huber et al. 2002, Sloan
and Barron 1990). The period of active latitudinal spread of angiosperms was among the
warmest during the Cretaceous (Cantrill and Poole 2002, Morley 2003), and according to the
High Latitude Scenario results, the influence of light would have been heightened during this
period. This strengthens the proposition that latitudinal angiosperm spread may have been
slowed in the middle latitudes by the increased competitiveness of gymnosperms in these areas
(discussed in section 4.5.1).
Secondly, temperature seems to be a substantial factor in high latitude environments, but it has
more impact on BE than NE; while BE cover fluctuates with changing temperature regimes, NE
remain fairly stable, between 25% and 30% cover, independent of the BE cover. And when
competitive opportunity opened with declining BE, it is taken by ferns rather than NE. This can
explain why gymnosperms and ferns remained prominent in the high latitudes throughout the
entire Cretaceous period (Herman 2002, Smiley 1969a), during which many climatic changes
took place (Huber et al. 1995, Puceat et al. 2003). BE vulnerability to low temperature in low
light can also explain why angiosperms were not successful in becoming dominant in the high
latitudes, particularly with declining temperatures during the late Cretaceous (Puceat et al. 2003,
Spicer and Chapman 1990, Wolfe and Upchurch 1987b).
Thirdly, there is a slight difference in response of vegetation to uniform versus seasonal
temperature climates, as seen in Figure 15. Temperature and precipitation seasonality is
investigated further in the following section.
4.6 Temperature and Precipitation Seasonality
Temperature and precipitation seasonality was tested as an independent factor in tropical
latitudes. Six variations of seasonal climate were assigned: three with hot dry summers and
warm wet winters, and three with cool wet summers and warm dry winters. Climate
specifications are briefly summarized in Table 14 below and in Table 4 in the Methods section.
NPP results for two of the seasonal simulations (CW/WD1 and 3), and possibly in HD/WW1,
clearly contained errors similar to those in the cold Temperature simulation (section 4.3.1 for
65
further discussion). The HD/WW2 and 3 simulations have a higher NPP (1491 and 1470
gC/m2yr) compared to Control (1279 gC/m
2yr). This shows that seasonality may slightly
increase NPP, although it is difficult to separate the pure effect of temperature and/or
precipitation from this trend.
Table 14: Seasonality simulations specifications.
Simulation Temperature Anomalies Precipitation Anomalies
HD/WW1 +5 / -5 °C of Control values in
(summer, autumn)/(winter, spring)
80% / 120% of Control in (summer,
autumn)/(winter, spring)
HD/WW2 +10 / -5 °C of Control in (summer,
autumn)/(winter, spring)
80% / 120% of Control in (summer,
autumn)/(winter, spring)
HD/WW3 +10 / -5 °C of Control in (summer,
autumn)/(winter, spring)
60% / 140% of Control in (summer,
autumn)/(winter, spring)
CW/WD1 0 / -5 °C of Control in (summer,
autumn)/(winter, spring)
120% / 80% of Control in (summer,
autumn)/(winter, spring)
CW/WD2 +5 / -5 °C of Control in (summer,
autumn)/(winter, spring)
120% / 80% of Control in (summer,
autumn)/(winter, spring)
CW/WD3 0 / -5 °C of Control in (summer,
autumn)/(winter, spring)
140% / 60% of Control in (summer,
autumn)/(winter, spring)
66
Figure 16: Fractional land cover occupied by each PFT in seasonal conditions: hot dry summer,
warm wet winter.
Figure 17: Fractional land cover occupied by each PFT in seasonal conditions: cool wet summer,
warm dry winter.
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Vegetation Composition Response to Change in Seasonality - Hot Dry Summer (HD); Warm Wet Winter (WW)
Ferns
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BE
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Vegetation Composition Response to Change in Seasonality - Cool Wet Summer (CW); Warm Dry Winter (WD)
Ferns
NE
BE
67
In all six cases, the fractional cover values varied very little from the Control values (Figure 16).
However, a slight seasonality effect is observed in NE in the HD/WW type of seasonality, while
BE mildly responded to the CW/WD type of seasonality. NE cover dropped from 12% in the
Control simulation to around 8% in the HD/WW simulations, while BE remained relatively
unaffected by the HD/WW seasonality (remained around 81% cover). Of the three HD/WW
simulations, the first one, which had the coolest temperatures and the smallest seasonal variation
in both temperature and precipitation, had the highest fractional cover of NE, at almost 9%
(compared to just under 8% in the other two HD/WW simulations). This is likely because NE
tend to do slightly worse under warmer temperatures, as observed in the Temperature experiment
(section 4.3.1).
BE decreased from 81% cover in Control to around 75 – 76% in the CW/WD simulations, while
NE rose slightly from 12% in Control to around 1-15% in CW/WD simulations (Figure 17).
This, once again, may largely be a result of a slight overall temperature decrease in the CW/WD
simulations, which tends to slightly lower BE cover (section 4.3.1). One notable result is that
CW/WD3, which was the simulation with the highest precipitation seasonality, had the smallest
fractional cover of BE (74.5%) and the greatest fractional cover of NE (almost 15%), showing
that BE prefer more uniform precipitation, while NE are more tolerant to precipitation
seasonality. However, this precipitation seasonality effect is not great.
Overall, the above results indicate that NE tend to be more competitive in CW/DD seasonal
conditions compared to no seasonality (Control) and HD/WW seasonality. BE prefer uniform
climates and ferns tend to take the opportunity given by the decline of either broad-leaf or
needle-leaf trees. In all, the seasonality effect, though present, is not very strong. Thus, my
simulation results do not show that seasonality played an important role in angiosperm spread
although it may have been a minor co-factor within more important processes (McElwain et al.
2005, Mutterlose et al. 2003). If seasonality did decrease on a global scale in the mid to late
Cretaceous, as suggested by Donnadieu et al. (2006) and Steuber et al. (2005), this may have
been of some benefit for the angiosperms and contributed to their spread, as in my simulations
BE tend to prefer a uniform climate. There is no indication, however, that seasonal aridity would
have increased the favorability of angiosperms as previously suggested by Axelrod (1970) and
68
Mutterlose et al. (2003). In fact, the decrease of BE in high precipitation seasonality indicates
the opposite; that angiosperms would not have been favored under seasonally dry conditions.
4.7 Deciduousness
Deciduousness was tested in higher latitude conditions (54 to 64.5 °N), since this is where it was
the most relevant during the Cretaceous. To test the effect of deciduousness individually, the
climate was set to Control in EvNT and DecidNT simulations. In ND, all parameters were kept
the same as in NE, except the parameter defining deciduousness.
When needle-leaf trees were set to deciduous (in DecidNT) they completely disappeared, unable
to compete with BE. BE took 93% cover in DecidNT, while ferns stayed around 6% in both
EvNT and DecidNT. (Figure 18) Thus, deciduousness gives the needle-leaf trees a substantial
competitive disadvantage in a warm, uniform climate.
The DecidNTcold simulation was similar to DecidNT, except temperatures were decreased by 10
°C relative to Control values to better represent a high-latitude climate. Interestingly, the
temperature was too cold for any trees to emerge, even though when the same temperature was
set at low latitude (see the -10 °C simulation in the Temperature experiment) some trees were
able to grow. This is not likely to be an effect of deciduousness since the BE did not emerge
either. Rather, this is probably a combined effect of temperature and light regime; trees tend to
be more tolerant to cold at high light levels.
69
Figure 18: Fractional land cover occupied by each PFT; evergreen and deciduous needle-leaf trees.
NPP changed only very slightly when needle-leaf trees were converted to deciduous, on account
of the fact that the tree cover stayed about the same in EvNT and DecidNT. NPP fell to 27
gC/m2yr in DecidNTcold, when no trees were present.
Because deciduousness is usually an adaptation to variable climate, three simulations were
conducted under temperature seasonality, with -5 °C from Control values in the summer and -10
°C from Control values in the winter. Precipitation was kept equivalent to Control. The first
simulation (ND/BE) tested competition between ND and BE. In the second simulation (ND/BD)
all trees were set to deciduous. The third simulation only had needle-leaf trees: ND and NE.
Table 15: Net primary production; alternating deciduousness in seasonal temperature climate.
Simulation NPP (gC/m2yr)
ND/BE 945
ND/BD 906
ND/NE 850
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Evergreen and Deciduous Needle-leaf Trees, 54 to 64.5 deg N latitude
Ferns
NE/ND
BE
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NPP results for the three Deciduous Scenario simulations were not surprising and realistic (Table
15). The ND/BE simulation yielded 945 gC/m2yr NPP. When broad-leaf trees were converted
to deciduous in the second simulation, NPP went down slightly to 906 gC/m2yr. This is probably
because of the resulting decrease in highly productive broad-leaf trees. In the needle-leaf
(evergreen) versus needle-leaf (deciduous) simulation, NPP was even lower, at 850 gC/m2yr,
which can also be explained by the lack of productive broad-leaf trees.
In a seasonal climate, BE out-competed ND with 44% cover versus 10% cover while Ferns took
up almost 30% (Figure 19). When ND were simulated alongside BD, their fractional cover went
up to 15%, while BD trees took 38%. In the all-needle- leaf (ND/NE) simulation, the deciduous
trees were outcompeted substantially, only taking up 10% of the cover while evergreen trees
dominated with 44% cover.
The ND/NE simulation clearly shows deciduousness as a competitive disadvantage in needle-
leaf trees. There is also an apparent disadvantage of deciduousness in broad-leaf trees as their
dominance decreased by over 6% when they were converted to deciduous (ND/BD simulation).
Interestingly, in the ND/BD simulation, needle-leaf trees, though also deciduous, were able to
take advantage of the decreased competitiveness of broad-leaf trees and their cover went up by
5% (compared to the ND/BE simulation). Therefore, deciduousness tends to “level the playing
field” in terms of competition between broad-leaf and needle-leaf trees. However, the “leveling”
effect is only present in a seasonal climate. In fact, a substantial quantity of ND emerged only in
cool and seasonal temperature conditions (as in ND/BE simulation), while in a warm uniform
climate with equivalent precipitation and light levels (as in DecidNT simulation), ND could not
compete at all. Thus, the results indicate that the seasonality of temperatures may have also been
a factor in the emergence of deciduous trees. Therefore, if seasonality decreased towards the late
Cretaceous (Donnadieu et al. 2006b), the success of the previously dominant deciduous
gymnosperms in the high northern latitudes may have been compromised, which would have
given angiosperms a chance to colonize some areas in the high latitudes.
71
Figure 19: Fractional land cover occupied by each PFT; alternating deciduousness in seasonal
temperature climate.
The results of the Deciduous experiment and interactive Deciduous Scenarios indicate that the
effect of deciduousness is very complex and it may have been a very influential aspect in
competition between broad-leaf and needle-leaf trees in high latitudes during the Cretaceous.
Unlike today, the Northern high latitude forests in the Cretaceous were dominated by deciduous
gymnosperms (Falcon-Lang et al. 2004, Wolfe and Upchurch 1987b), and deciduousness was,
until recently, typically regarded as an adaptation to decreased light in warm conditions, since by
maintaining their leaves in insufficient light in the warm winter, trees would have incurred large
carbon loss through respiration (Axelrod 1966, Herman and Spicer 1999, Hickey 1984, Wolfe
1985). Recent experiments and modeling studies, however, found that deciduousness is
probably not an optimal strategy for conserving resources in low-light warm winters as leaf-
shedding actually results in more annual carbon loss than keeping the leaves all year round
(Osborne and Beerling 2003, Royer 2005, Royer et al. 2003, 2005). My results also do not
support the proposition that deciduousness was an effective strategy for surviving the winter
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Deciduous Scenarios, 54 to 64.5 deg N latitude, seasonal temperatures
Ferns
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72
light conditions since deciduous trees generally competed poorly with evergreen trees. To
further confirm that light regime is not the main factor in promoting deciduousness, more
interactive simulations with a 5-PFT assemblage were conducted under cool seasonal
temperatures in variable latitudes. (Simulation specifications summarized in Table 16)
Table 16: Simulation specifications for 5-PFT scenarios.
Simulation Latitude Temperature Anomalies
D80 74.5 to 84 ºN Summer/autumn: -5 ºC
Winter/spring: -10 ºC
D60 54 to 64.5 °N Same as D80
D30 24.5 to 35 ºN Same as D80
D0 3 °S to 7.5 °N Same as D80
D0warm 3 °S to 7.5 °N Non-seasonal temperatures, 24.3 °C annual average
The 5-PFT scenarios (Figure 20) which included both, deciduous and evergreen, types of broad-
leaf and needle-leaf trees, once again showed the competitive superiority of evergreen trees. In
the four cool seasonal temperature scenarios (D80, D60, D30, and D0), NE and BE were the
dominant tree types, each occupying 16% to 24% cover while the deciduous trees, ND and BD,
only occupied between 4% and 11% cover each. BE remained fairly constant at around 17% in
the four simulations while NE fractional cover stayed between 21% and 24%, showing little
response to latitude. Deciduous trees maintained a secondary role at all latitudes, which shows
that light was probably not a major factor in their competitiveness. Interestingly, in the 5-PFT
assemblage (in cool seasonal temperatures), NE had the greatest fractional cover of the tree PFTs
in all four latitudes and unlike in 3-PFT assemblages, BE did not overwhelmingly dominate.
Instead, it seems that BE are also not given a chance to dominate in a more diverse plant group
assemblage, regardless of the latitude. This is indicative of the importance of the competition
dynamics since a more extensive plant assemblage would result in higher light and nutrient stress
for BE. The 5-PFT assemblage would therefore be representative of a more mature and diverse
forest, which would have taken some time for early angiosperms to penetrate given that they
were weedy and not typically stress-tolerant (Hickey and Doyle 1977, Royer et al. 2010, Taylor
73
and Hickey 1996, Wing and Boucher 1998). Stress conditions tended to be more conducive to
conifers (Wolfe 1987) and this is reflected in the relative success of NE in the 5-PFT scenarios.
Therefore, the results of the 5-PFT scenarios primarily show that competition was likely an
important element that suppressed the angiosperms, especially in mature forests.
An interesting result is seen in the D0warm simulation (low latitude), which is around where the
temperatures are warm and relatively uniform throughout the year (Control temperatures). The
fractional cover of all trees increased substantially in D0warm compared to the cooler 5-PFT
simulations while the fractional cover of ferns decreased dramatically to 6%. The increase in
overall tree cover is likely an effect of increased temperature. Notable, the broad leaf trees (both
BE and BD) increase substantially in fractional cover relative to the D0 (with cool seasonal
temperatures) simulation and BE very slightly outcompete NE (35% versus 34% cover,
respectively). This once again shows that broad-leaf trees are more competitive in warm
conditions (see Temperature experiment). However even under warm conditions, they are
unable to dominate in a 5-PFT assemblage due to increased competition.
Also, in the 5-PFT scenarios, deciduous broad-leaf trees did slightly better in the high latitudes
than in the low latitudes, while deciduous needle-leaf trees did slightly worse in high latitudes.
This may be due to the lower canopy conductance parameter of ND (than BD), which would
make it less competitive during the time that it has leaves since it would not be able to take the
same advantage of the limited light hours in the high latitudes as BD. BD, on the other hand,
would be subjected to more competitive pressure in lower latitudes, where there is more light.
74
Figure 20: Fractional land cover occupied by each PFT; competition between deciduous and
evergreen trees at various latitudes. Temperatures are cool and seasonal, with exception of
D0warm, which has a warm uniform climate.
Another notable result is that ferns had a very strong presence in high latitude regions with cool
seasonal climates (Figures19 and 20), keeping around 30% cover in all Deciduousness Scenario
simulations except D0warm. This result is fairly consistent with the fossil record from the early-
mid Cretaceous, which shows that ferns were a prominent plant group in the high latitudes
(Drinnan and Crane 1990, Herman 2002, Scott and Smiley 1979, Smiley 1969a). The resultant
30% Fern cover in the cool seasonal simulations is a substantial fractional cover, comparable to
that of trees in the same simulations, especially if the broad-leaf tree cover is exaggerated due to
modeling limitations (section 4.2.2). This indicates that less favorable environments for trees
provide an important establishment opportunity for understory plants. Ferns are the only
understory PFT in the Deciduousness simulations, so it is reasonable to generalize their success
to understory plant success. The same opportunity could have been taken by other understory
plants similar to ferns, including herbaceous or shrubby angiosperms. Many researchers suspect
that early herbaceous angiosperms displayed weedy behavior (Hickey and Doyle 1977, Royer et
0
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75
al. 2010, Taylor and Hickey 1996, Wing and Boucher 1998), and the thinner canopy due to fewer
trees and the dropping of the deciduous leaves may have given a special opportunity to weedy,
fast-growing ruderals. Although this is not illustrated in simulation results, from a weed’s
perspective, the dropping of canopy leaves could be considered as a sort of “disturbance” that
opens up light and nutrient resources for a short period. The decomposition of the resulting leaf
litter would contribute nutrients into the soil while the open canopy would allow more light to
reach the understory. Thus, if deciduous trees were predominant in Cretaceous forests (Falcon-
Lang et al. 2004, Wolfe 1987, Wolfe and Upchurch 1987a), this could explain how the early
understory angiosperms initially penetrated the forest interior, however, further study is required
to confirm this. Furthermore, such an environment, with seasonally available light for
understory plants, may have also provided a favorable setting for diversification of understory
plants. This opens up a series of questions regarding the role of deciduousness in the
diversification of angiosperm in the Cretaceous, as well as the role of ferns. Interestingly,
outside of angiosperms, the most diverse plant group at the time was ruderal ferns, while the
stress-tolerant plant groups including conifers and cycads had low species diversity even though
they were dominant in many regions (Grime 1979, Wing and Boucher 1998). In fact, recent
evidence suggests that ferns may have undergone a large diversification during the Cretaceous
along with the angiosperms (Schneider et al. 2004). On the other hand, in polar latitudes, where
the angle of light incidence is relatively low, less light would have been able to penetrate through
the canopy which would have impeded understory growth (Wolfe 1985). This is consistent with
the fact that most Cretaceous angiosperm fossils in the high latitudes were found near coastal
margins, where more light would have been available to them (Golovneva 1994, Herman 1993,
Herman and Spicer 1997, Spicer and Parrish 1990). However, this is difficult to test in LPJ
because LPJ does not simulate such intricacies as leaf size and leaf angle with respect to incident
light, which may have been important adaptive plant features in high latitude environments
(Wolfe 1987).
The results from the Deciduousness experiment and the high-latitude scenarios open up some
new direction for research. However, the exact effect of deciduousness is complex and difficult
to determine from this experiment. Overall, deciduousness tends to pose a competitive
disadvantage for trees under the climate conditions given in the simulations. Today, broad-
leaved deciduous forests are usually present in humid to mesic regions with large temperature
76
seasonality in the Northern Hemisphere. The optimal geographical range for modern deciduous
forests is where the cold-month mean is less than 1 °C and the warm month mean is greater than
20 °C (Wolfe 1987). However, in the late Cretaceous, the polar regions were covered in
deciduous forests with many trees having large leaf sizes, which was probably a result of
relatively warm temperatures and polar light regime (Wolfe 1987). Therefore it seems that the
high-latitude deciduous forest required specific climate conditions, which were met during the
equable Cretaceous period as well as in parts of the Paleocene (~ 65 to 56 Ma) and Eocene (~ 56
to 34 Ma), but not during colder periods. In future studies it would be beneficial to conduct
simulations with higher temperature seasonality to better understand the dynamics of the
deciduous polar forest. It would also be useful to add more leaf trait parameters (such as leaf
size) to the PFT parameterization scheme to better represent high-latitude deciduous vegetation
and understand the competition dynamics between polar trees in the Cretaceous.
Another interesting recent proposition is that deciduousness may have been an adaptation to
frequent disturbance, rather than light (Brentnall et al. 2005), particularly since Cretaceous trees
in Antarctica (which experienced less frequent fire disturbance compared to Northern high
latitudes) were predominantly evergreen (Brentnall et al. 2005, Falcon-Lang and Cantrill 2000,
2001). This can be effectively tested in LPJ with an improved disturbance module.
4.8 Success of LPJ in the Angiosperm Paleo-Application
4.8.1 Strengths of LPJ as a Paleo-Research Tool
In this project, LPJ was successfully used to determine the general effects of various factors on
the potential vegetation composition in the Cretaceous period. Through the comparison of plant
responses under varying climate, soil, and light regime, as well as varying plant structure (i.e.
tree versus grass) and phenology (deciduous versus evergreen), it was possible to infer some of
the processes that may have taken place in the Cretaceous and propose explanations for some of
the phenomena observed in the fossil record (for example, that latitudinal differences in light
regime could have hindered angiosperm spread). In this sense, LPJ has proven itself as a very
useful and practical tool for testing the broad range of existing theories on the topic in a dynamic
and interactive way, even without major changes being made to the model. In a medium-scale
77
(regional) type of experiment such as this one, LPJ can be used to answer, or at least provide
insight into, a variety of paleo-questions for not only the Cretaceous period, but any time period
for which enough fossil information is available for validation of modeling results. The
flexibility of the PFT plant classification scheme is one of the features that broaden LPJ’s
applicability to different time periods, particularly since PFT parameters can be modified to
better represent the characteristics of ancient plants (Cowling and Gousseva 2010). For instance,
in this study, the PFTs that were used to represent the general gymnosperm and angiosperm tree
groups (NE and BE) were modified from original broad-leaf and needle-leaf trees in LPJ to
better represent the characteristics of Cretaceous trees. In future work, other parameter
modifications can be made to incorporate new emerging fossil information or even to test the
role of plant characteristics in plant competition and response to changing climate. Overall, LPJ
has much potential as a tool in paleo-ecological studies and its applicability has been
demonstrated in the angiosperm experiments in this work.
4.8.2 Problems and Limitations of LPJ
Several problems and limitations of LPJ have been brought to light through the simulations in
this study that should be addressed in the future. Firstly, it appears that there are some technical
glitches within LPJ’s program code, which cause errors in carbon balance output. This problem
arises only in certain cases, when a specific set of input parameters is used. It is unclear what
causes this glitch in individual cases since it does not appear in most simulations. Another glitch
is that LPJ does not work with a set of 4 PFTs (although it works with any number of PFTs
below or above 4). These are probably very minor coding errors that are difficult to spot due to
the length and complexity of the program code.
One of the limitations of LPJ, in its current state, is the limited number of PFT parameters. This
greatly restricts the descriptiveness of individual PFTs, especially since some key plant features
cannot be expressed in the scheme. This issue is highlighted in section 4.2.2 as the available set
of PFT parameters could not be used to represent key features of early angiosperms (such as
wood characteristics and height). Furthermore, LPJ does not account for the different types of
reproductive strategies, seed type, pollination and dispersal mechanisms. Due to this problem,
Cretaceous angiosperms could only be examined in terms of their carbon and water cycling traits
78
in relation to their environment and competing vegetation, and nothing could be said about their
reproductive innovations, which may have played a key role in angiosperm spread and rise to
dominance (Burger 1981, Crepet 1983, Doyle and Donoghue 1986, Midgley and Bond 1991,
Regal 1977, Stebbins 1981). Also due to the restrictiveness of PFT parameters, only 3 PFTs
were used in most experimental simulations. In reality there were many other plant groups
present in the Cretaceous including but not limited to pteridophytes, sphenophytes, and cycads
(Scott and Smiley 1979, Smiley 1969a, Spicer et al. 2002). However, currently those plants are
only able to be described as another tree or grass with moderately differing carbon and water
related characteristics (and a few characteristics related to structure and growth) and bioclimatic
limits. Although including these PFTs can potentially be useful in further examining Cretaceous
plant competition in terms of carbon and water cycling, it would be of most benefit to first
expand the PFT parameterization scheme to include distinguishing features of these plants (for
example, reproduction mechanisms and wood structure characteristics).
Another limitation of LPJ is that, among many DGVMs, LPJ does not have extensive modules
for simulating soil processes or disturbance. The restrictiveness of the soil parameterization
scheme is discussed in section 4.4. LPJ’s simulation of disturbance is restricted to fire, and the
fire module is very basic and not versatile (Sitch et al. 2003). It is also not able to simulate other
types of disturbance such as windstorms and herbivory, which can be important parts of the
ecosystem. For example, it has recently been hypothesized that disturbance played a crucial role
in determining high-latitude plant composition in the Cretaceous (Brentnall et al. 2005)(see
section 4.7).
Vegetation modeling in general, in its current state, cannot be used to test the role of
biodiversity, taxonomic diversification and speciation rates, such peculiar features such as weedy
behavior and parasitism, and fine-scale processes of roots and leaves. Some of these aspects
may have been important for early angiosperms. For example, according to Wing and Boucher
(1998), one possible explanation for the angiosperms’ rise to dominance is that the replacement
of older lineages took place gradually as a result of angiosperms’ higher speciation rate which
would have given angiosperms a greater chance to replace extinct competitors and stress-
tolerating taxa. Thus, angiosperms would have gradually increased in dominance, following
their increase in diversity. However, being able to simulate such processes would require
significant advancements in the vegetation modeling field.
79
80
Conclusion
5
5.1 Angiosperms in the Cretaceous
Several main patterns were identified through LPJ simulations with regard to angiosperm spread
during the Cretaceous. Firstly, broad-leaf trees are typically more successful under warm
conditions suggesting that the relatively warm temperatures during the Cretaceous period likely
facilitated the spread of angiosperms into forest interiors as well as into higher latitudes. This
proposition is supported in the fossil record, which shows that angiosperms spread to high
latitudes during the warmest period of the Cretaceous (Cantrill and Poole 2002, Morley 2003,
Scott and Smiley 1979). However, it may have still been too cold for angiosperms to dominate
in the polar regions (Herman 2002, Scott and Smiley 1979). Furthermore, angiosperms may
have been relatively more sensitive to decreases in temperature at high latitudes, which would
have hampered their spread in the high latitudes particularly during the cooling period in the late
Cretaceous (Spicer and Chapman 1990, Wolfe and Upchurch 1987a).
Neither precipitation nor atmospheric CO2 appear to be major factors for angiosperm spread.
However, needle-leaf trees tend to be less competitive in higher CO2, probably due to lower
canopy conductance, which may have given early angiosperms opportunity to spread into new
communities under high CO2 conditions in the early to mid Cretaceous (Jahren et al. 2001).
Temperature and precipitation seasonality also did not seem to play a key role in angiosperm
spread, although the simulated evergreen broad-leaf trees tended to prefer uniform climates and
thus the more equable climates during the Cretaceous could have benefitted angiosperms
(McElwain et al. 2005).
The effect of light on plant composition was found to be very substantial in LPJ simulations in
this study. Broad-leaf trees were less competitive in high-latitude light regime, and interestingly,
considerably less competitive in middle latitudes. This can explain why angiosperms took
millions of years to spread pole-ward despite favorable temperature and CO2 conditions (Hickey
and Doyle 1977, Retallack and Dilcher 1986). This may also be why it took time for
angiosperms to gain local dominance even after they spread around the globe (Drinnan and
Crane 1990).
81
Finally, deciduousness, particularly in higher latitudes, was shown to be a deterring factor for
broad-leaf and needle-leaf trees as both were largely outcompeted by evergreen trees in all cases.
It is unclear from the simulations how this would have affected angiosperm spread, although one
hypothesis is that annual leaf shed could have created an opportunity for understory growth
(including understory angiosperms). Most of the observations from the Deciduousness
simulations go beyond of the focus of this thesis, which is the angiosperm/ gymnosperm
competition (translating into needle-leaf/broad-leaf tree competition in LPJ), and open up many
topics for further research.
5.2 Future directions: Paleo-vegetation research and development of LPJ
The Deciduousness scenarios reveal many questions about the dynamics of the high-latitude
Cretaceous forests. For instance, the fact that deciduousness was shown to be a disadvantage to
trees under the given simulation conditions poses the question of why deciduousness was so
common in Cretaceous high-latitudes (Falcon-Lang et al. 2004, Wolfe 1987, Wolfe and
Upchurch 1987a). Another observation is that broad-leaf deciduous trees did better in high-
latitudes than needle-leaf deciduous trees, probably because the broad-leaf PFT had a higher
canopy conductance, which may have allowed it to take more advantage of the available light
hours. This is interesting given the fact that some high-latitude Cretaceous conifers were broad-
leaved (Wolfe 1987), however, further investigation is required to understand the competition
between broad-leaf and needle-leaf deciduous trees and how it would have affected vegetation
dynamics in polar regions. The questions can be extended to other hot-house climate periods
such as the Paleocene (~65 – 56 Ma) and Eocene (~56 – 34 Ma), during which deciduousness
became more wide-spread, especially in mid-latitudes (Wolfe 1987). Future modeling studies
can explore the disappearance of some deciduous needle-leaf taxa, such as Metasqeuoia, from
high latitudes during the late Eocene cooling period (Liu et al. 2007, Williams et al. 2003), as
well as the origins of the modern Boreal Forest biome (Taggart and Cross 2009).
Although this study shed light on many key trends of Cretaceous vegetation, a deeper
investigation of the questions addressed in this work will require modification of LPJ. Extending
the parameterization of soils in LPJ can be of substantial benefit for paleo-vegetation as well as
modern vegetation studies, especially since soil structure has been shown to have a strong impact
82
on vegetation composition and carbon cycle modeling (Ostle et al. 2009, Shellito et al. 2005).
For studying the possible plant-soil interactions and feedbacks, it would be useful to include in
LPJ the characteristics of the soil profile (i.e. soil depth and substrate), soil nutrient and mineral
content, and soil processes such as various rates of decomposition and the resulting nutrient
content. Furthermore, developing the disturbance module would also improve simulation results
and extend the applicability of LPJ in addressing such hypotheses as deciduousness being a
potential adaptation to frequent disturbance rather than climate seasonality (Brentnall et al.
2005).
Another hypotheses that remains to be explored is the possibility of early angiosperm affinity to
seasonally arid environments and drought, which has been previously suggested as the primary
mechanism for the success of early angiosperms (Axelrod 1966, 1970, Brenner 1963, Coiffard et
al. 2007, Mutterlose et al. 2003). This question can be addressed at least in part with LPJ in its
current state, by simulating more intense precipitation extremes as well as introducing periodic
droughts. However, given that currently LPJ is generally not very sensitive to precipitation
(Cowling and Shin 2006), modification is needed to obtain more realistic results. Expansion of
the PFT parameterization scheme may be the key to resolving this issue, as well as improving the
flexibility of the model. Addition of plant traits related to water-cycling such as wood
characteristics (i.e. amount of soft tissue, vascular conductivity) and leaf structure (i.e. leaf size
and venation, stomatal control mechanisms) would not only improve water-sensitivity, but also
help to better define the plant functional types, thus forming a better framework for creating
more specific PFTs that would have been found in the deep past (such as primitive trees and tree
ferns). Introducing shrubs as a vegetation type as well as including seed and dispersal
characteristics in the PFT parameterization would also help to better define PFTs and extend
LPJ’s applicability to paleo-vegetation studies.
One argument against extending LPJ modules and parameterization may be that adding
complexity would take away from LPJ’s usability as a regional to global scale model by adding
features that may not be reflected on a large scale while significantly increasing computation
time (Hickler et al. 2006, Hughes et al. 2006). Moreover, other models such as LPJ-Guess
(Smith et al. 2001) and SEIB-DGVM (Sato et al. 2007) have been developed to incorporate more
individual plant features and local plant community dynamics. However, a case can be made
that it is not so much necessary to simulate competition between individuals in LPJ (as in LPJ-
83
Guess), but to increase the ability of LPJ to better represent and distinguish between PFTs and
increase the flexibility of the model (by perhaps adding optional features that can be turned off if
desired), which can be key in paleo-studies and also improve modern-day simulations. A more
extensive PFT scheme can also open up an interesting possibility of studying patterns of
individual species with LPJ. For instance, if one could represent a specific plant (Metasequoia,
for example) within an assemblage of more general plant groups, it would not necessarily result
in a substantial increase in computation time, but it would open up an opportunity to explore
numerous ecological questions with vegetation modeling.
Overall, in this study, LPJ has proven to be a useful tool in exploring Cretaceous angiosperm
spread. With its flexible framework, it has also shown great potential for modification and future
applications in the field of paleo-ecology.
84
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Appendix 1
Original LPJ PFTs as per Sitch et al. (2003)
Tropical broad-leaved evergreen trees
Tropical broad-leaved raingreen trees
Temperate needle-leaved evergreen trees
Temperate broad-leaved evergreen trees
Temperate broad-leaved summergreen trees
Boreal needle-leaved evergreen trees
Boreal needle-leaved summergreen trees
Boreal broad-leaved summergreen trees
Temperate herbaceous (C3 grass)
Tropical herbaceous (C4 grass)
Complete set of LPJ PFTs as per Cowling and Gousseva (2010) and abbreviations
PFT Abbreviation 1 Bryophytes Bry
2 Arborescent Lycopods Lyc
3 Ferns Ferns
4 Giant Horsetails HT
5 Tree Ferns TF
6 Cordaites Cordaite
7 Tropical needle-leaved evergreen trees TrNEv
8 Tropical broad-leaved evergreen trees TrBEv
9 Tropical broad-leaved raingreen trees TrBRn
10 Temperate needle-leaved evergreen trees TmNEv
11 Temperate broad-leaved evergreen trees TmBEv
12 Temperate broad-leaved summergreen trees TmBSum
13 Boreal needle-leaved evergreen trees BorNEv
14 Boreal needle-leaved summergreen trees BorNSum
15 Boreal broad-leaved summergreen trees BorBSum
16 Temperate herbaceous (C3 grass) C3 gr.
17 Tropical herbaceous (C4 grass) C4 gr.
99
Appendix 2
LPJ PFT parameters as per Cowling and Gousseva (2010)
PFT 1 2 3
Parameter Bry Lyc Ferns
1 Fraction of roots in upper soil layer 1 0.98 0.9
2 C4 (1) C3 (0) 0 0 0
3 Water scalar, when leaves shed by decid 0 0 0
4 Canopy conductance 0.2 0.7 0.5
5 Maintenance respiration coefficient 2.5 1 1.8
6 Moisture of extinction 0.1 0.7 0.2
7 Maximum foliar N composition 20 20 35
8 Fire resistance index 1 0.1 0.12
9 Leaf turnover (years) 2.5 1 0.3
10 Leaf longevity (years) 2.5 1 0.3
11 Sapw to heartw (in years) 1 5 1
12 Root turnover (years) 4 1 2
13 Leaf C:N 30 25 40
14 Sapwood C:N 0 80 0
15 Root C:N 30 25 40
16 Leaf type: broad(1), needle(2), grass(3) 3 1 3
17 Phenology: evergreen(1), summergreen(2),raingreen(3), any type (4) 1 1 1
18 Leaf to root ratio (not water stressed) 1 1 1
19 GDD requirement to grow full leaf canopy 30 40 75
20 Max tree crown area 0 2 2
21 Initialization: grass or saplings LAI 0.001 0.2 0.05
22 (Sapwood+heartwood)/sapwood 1.2 1.05 1.2
23 Boreal (1), non-boreal (0) 0 0 0
24 Low T limit for CO2 uptake 2 10 10
25 Low range, Pn Topt 15 20 20
26 High range, Pn Topt 30 35 35
27 High T limit for CO2 uptake 35 45 55
28 Min cold monthly mean -1000 -5 -5
29 Max cold monthly mean 1000 100 1000
30 Min GDD 0 300 0
31 Upper T-limit warmest month 50 50 50
32 Lower limit growth efficiency (gm-2) 0 0 0
33 GDD base 5 5 5
34 20-year mean (min warmest – coldest month) (T-range) -1000 -1000 -1000
100
4 5 6 7 8 9 10 11 12
HT TF Cordaite TrNEv TrBEv TrBRn TmNEv TmBEv TmBSum
1 0.9 0.9 0.9 0.7 0.95 0.7 0.7 0.7 0.8
2 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0.35 0 0 0
4 0.5 0.5 0.4 0.4 0.7 0.5 3 0.5 0.5
5 1.8 1.8 1.8 0.5 0.5 0.2 1.2 1.2 1.2
6 0.5 0.4 0.3 0.5 0.3 0.3 0.3 0.3 0.3
7 30 30 25 20 100 100 100 100 120
8 0.12 0.1 0.12 0.12 0.12 0.5 0.12 0.5 0.12
9 1.5 1 2 1.5 2 1 2 1 1
10 1.5 1 2 1.5 2 0.5 2 1 0.5
11 5 10 10 20 20 20 20 20 20
12 4 4 3 3 2 1 2 1 1
13 25 45 40 40 29 29 29 29 29
14 80 115 80 80 330 330 330 330 330
15 25 45 40 40 29 29 29 29 29
16 3 1 2 2 1 1 2 1 1
17 1 1 1 1 1 3 1 1 2
18 0.8 1 1 1 1 1 1 1 1
19 75 125 200 300 1000 1000 1000 1000 200
20 5 8 5 15 15 15 15 15 15
21 0.05 0.2 1 1 1.5 1.5 1.5 1.5 1.5
22 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2
23 0 0 0 0 0 0 0 0 0
24 10 5 5 5 2 2 -4 -4 -4
25 20 20 20 20 25 25 20 20 20
26 35 27 30 30 30 30 30 30 25
27 45 40 50 50 55 55 42 42 38
28 -5 -5 -5 15.5 15.5 15.5 -2 3 -17
29 1000 1000 1000 1000 1000 1000 22 18.8 15.5
30 0 300 1000 1000 1000 0 900 1200 1200
31 50 1000 50 50 50 1000 1000 1000 1000
32 0 0 0 0 0 0 0 0 0
33 5 5 5 5 5 5 5 5 5
34 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000
101
13 14 15 16 17
BorNEv BorNSum BorBSum C3 gr. C4 gr.
1 0.9 0.9 0.9 0.9 0.9
2 0 0 0 0 1
3 0 0 0 0.35 0.35
4 0.2 0.5 0.3 0.5 0.5
5 1.2 1.2 1.2 1.2 1.2
6 0.3 0.3 0.3 0.2 0.2
7 20 100 100 100 100
8 0.12 0.12 0.12 1 1
9 2.5 1 1 1 1
10 2.5 0.5 0.5 1 1
11 20 20 20 1 1
12 3 1 1 2 2
13 40 29 29 29 29
14 330 330 330 0 0
15 40 29 29 29 29
16 2 2 1 3 3
17 1 2 2 4 4
18 1 1 1 1.5 0.75
19 300 100 200 100 100
20 15 15 15 0 0
21 1.5 1.5 1.5 0.001 0.001
22 1.2 1.2 1.2 1.2 1.2
23 1 1 1 0 0
24 -3 -4 -4 -4 6
25 10 15 15 10 20
26 25 25 25 30 45
27 38 38 38 45 55
28 -32.5 -1000 -1000 -1000 15.5
29 -2 -2 -2 15.5 1000
30 600 350 350 0 0
31 23 23 23 50 1000
32 0 0 0 0 0
33 5 5 5 5 5
34 -1000 -1000 -1000 -1000 -1000