Evaluating the Impacts of Agricultural Development
on Landscape Scale Ecosystem Stability in a
European Context
by
William Joseph Gillam
A Thesis
presented to
The University of Guelph
In partial fulfilment of requirements
for the degree of
Master of Science
in
Geography
Guelph, Ontario, Canada
© William Gillam, May, 2017
ABSTRACT
EVALUATING THE IMPACTS OF AGRICULTURAL DEVELOPMENT ON LANDSCAPE
SCALE STABILITY IN A EUROPEAN CONTEXT
William Joseph Gillam
University of Guelph, 2017
Advisors:
Dr. Ze’ev Gedalof
Dr. Evan Fraser
In the face of growing human populations, the global area of agricultural lands is
expected to rise by 10 million km2 in the next 50 years, resulting in natural systems that are
highly disrupted. Beyond agriculture, ecosystems provide valuable goods and services to
humanity so it is important to study the landscape dynamics of agriculture. Using the
environmental history of Europe derived from pollen records, this research evaluated the impacts
of agricultural development on landscape scale stability. Rates of change, biodiversity, and
species composition were calculated for 184 pollen records across Europe that contained
evidence of agricultural development. The results indicate that rates of change rose substantially
following agricultural development, indicating a loss in stability. Landscape instability is likely
resultant from two mechanisms: direct changes in landscape composition from agricultural
intensification and abandonment, and ecological changes resulting from altering landscape
composition such as changes in species composition.
ii
ACKNOWLEDGEMENTS
First, I’d like to thank my advisors, Dr. Ze’ev Gedalof and Dr. Evan Fraser. It was a long
three years but you both kept me on track and working hard while I did everything I could to
make this thesis as perilous and slow as possible. Without your wisdom, aid, and advice this
project would never have developed from an idea to the completed thesis written here. I would
also like to thank Dr. Kevin McCann for offering your ecological knowledge as a source of
inspiration throughout this thesis and Dr. Neil Rooney for examine this thesis and offering
insight and ideas that polished the thesis to completion.
This work would not have been possible if not for the pollen data from the European
Pollen Database. To all the contributors and the EPD community I am eternally grateful for your
hard work and dedication to the dissemination of knowledge and information.
I’d also like to thank the other members of the CEDaR Lab, specifically Madison and
Emma. I asked a thousand questions a day, bounced even more ideas off the two of you over our
three years here, yet every new question and idea you willingly gave your advice. Your continual
acceptance of my “quick questions” kept me grounded throughout it all and it’s impossible to
understate how much I appreciated you two during my Masters.
Throughout the three years of this program, I’ve had the honor of meeting so many great
people in the department, from the GSA, ultimate Frisbee, climbing and just from living life. All
of you kept me sane and made Guelph the home it is now and I hope you’ll continue to do so far
into the future. Special shout-out to Team Steve and the many trivia nights we’ve shared
together.
Finally, I’d like to thank my family for being the most loving, caring, and best anyone
can ask for. To my younger brother, Phillip, thank you for always being there when I called you
during the ups and many downs of this process. You deserve so much credit for how this thesis
panned out.
iii
TABLE OF CONTENTS
ABSTRACT .................................................................................................................................... ii
ACKNOWLEDGEMENTS ........................................................................................................... iii
LIST OF FIGURES ....................................................................................................................... vi
LIST OF TABLES ........................................................................................................................ vii
LIST OF EQUATIONS ............................................................................................................... viii
Chapter 1: Introduction ................................................................................................................... 1
1.1
Background ...................................................................................................................... 1
1.2
Problem Statement ........................................................................................................... 3
1.2.1
1.3
Goals and Objectives ................................................................................................ 3
Outline of Thesis .............................................................................................................. 4
Chapter 2: Review of Literature ..................................................................................................... 5
2.1
Ecosystem functioning, stability, and biodiversity .......................................................... 5
2.1.2
Relationships between Biodiversity and Productivity .............................................. 6
2.1.3
Functional Diversity and Productivity ...................................................................... 7
2.1.4
Stability-Diversity ..................................................................................................... 9
2.1.5
Measuring Diversity................................................................................................ 12
2.2
Landscape Ecology ........................................................................................................ 13
2.2.1
Agricultural impacts................................................................................................ 14
2.2.2
Landscape Ecology as a tool for Governance ......................................................... 16
2.3
Ecological and Agricultural History of Europe.............................................................. 18
2.3.1
The Origins of Agriculture...................................................................................... 18
2.3.2
Ecological History of Europe.................................................................................. 19
2.3.3
Agricultural History of Europe ............................................................................... 20
2.4
Pollen Records................................................................................................................ 23
2.4.1
Pollen Record Source .............................................................................................. 24
2.4.2
Indicators of Human Development ......................................................................... 24
2.4.3
Radiocarbon Dating ................................................................................................ 25
2.4.4
Analyzing Pollen Records....................................................................................... 26
Chapter 3: Manuscript................................................................................................................... 28
3.1
Introduction .................................................................................................................... 28
3.2
Methods .......................................................................................................................... 31
3.2.1
Area of Study .......................................................................................................... 31
iv
3.2.2
Pollen Records ........................................................................................................ 31
3.2.3
Transforming/Reformatting the data....................................................................... 32
3.2.4
Calibration............................................................................................................... 32
3.2.5
Agricultural Indicator.............................................................................................. 32
3.2.6
Regime Shift Detection and Application ................................................................ 33
3.2.7
Species Distributions .............................................................................................. 34
3.2.8
Biodiversity ............................................................................................................. 35
3.2.9
Index of Dissimilarity ............................................................................................. 36
3.3
Results ............................................................................................................................ 38
3.3.1
Species Distribution ................................................................................................ 38
3.3.2
Biodiversity ............................................................................................................. 41
3.3.3
Trends in Stability ................................................................................................... 43
3.4
Discussion ...................................................................................................................... 45
3.4.1
Landscape Stability in the Past 10,000 years .......................................................... 45
3.4.2
Impacts of Agricultural Development on Landscape Ecosystem Stability ............ 46
3.4.3
Dynamic Land Use Patterns vs. Ecosystem Structure and Composition................ 47
3.5
Conclusion...................................................................................................................... 52
Chapter 4: Concluding Chapter .................................................................................................... 55
4.1
Overview of Findings ..................................................................................................... 55
4.2
Limitations and Recommendations for Future Research ............................................... 56
Works Cited .................................................................................................................................. 58
Appendix A: List of Pollen References ........................................................................................ 69
v
LIST OF FIGURES
Figure 2.1 Increasing species richness increases average community biomass ............................ 7
Figure 2.2 Dependence of aboveground plant biomass on functional richness ............................. 8
Figure 2.3 A timeline of arable land used in Europe from 3000 years BP to 150 years BP. ....... 22
Figure 3.1 Locations of pollen records analyzed ......................................................................... 38
Figure 3.2 Pollen percent change from 2000 years prior to agricultural development to 2000
years post agricultural development for 6 pollen categories ........................................................ 40
Figure 3.3 A time series representing the distribution of the Shannon index values in 100 year
intervals before and after agricultural development, and in calendar years ................................ 42
Figure 3.4 A time series representing the distribution of the rates of change values in 100 year
intervals before and after agricultural development, and in calendar years ................................. 44
vi
LIST OF TABLES
3.1 Taxa composing the pollen type categories. Poeceae, Cerealia, and Cyperaceae are single
taxa categories ............................................................................................................................... 34
A.1 Site names and citations for pollen records extracted from the European Pollen Database .. 69
vii
LIST OF EQUATIONS
3.1 Square Chord Distance ........................................................................................................... 37
viii
Chapter 1: Introduction
1.1 Background
The human population is predicted to rise to somewhere between 9.6 and 12.3 billion by
the year 2100 (Gerland et al. 2014). The Food and Agricultural Organization of the United
Nations estimated that in 2015 crops and pasture constituted approximately 44 million km2 of
land globally (FAOSTAT 2012). This statistic is expected to rise by 10 million km2 in the next
50 years.
Agriculture is often considered a hallmark of human civilisation. Compared to hunting
and gathering, agriculture provides a stable and productive source of food for expanding
populations. Without agriculture, human population growth would have been substantially
limited and would likely have never reached modern levels. The origins of agriculture have been
traced to three distinct locations: the Far East, Near East, and Mesoamerica (Harlan 1971). These
three centres acted as the cradle of agricultural, where early cultivators experimented with
cultivating, harvesting, and domestication of plants and animals. Sauer (1952) outlines six basic
premises on the origins of agriculture: (1) Agriculture did not originate from famine as a hungry
society would not have the means to undertake the necessary steps to develop agriculture, (2)
Local diversity is necessary for experimentation implying varied ecosystems or climate, (3)
Without the means to drain and irrigate river valleys primitive cultivators originally established
agriculture in hills and mountains, (4) Agriculture originated in woodlands, offering open spaces
and soils suitable for cultivation, (5) Primitive cultivators likely developed skills pre-disposing
themselves to agricultural experimentation, and (6) Given the need to protect crops, the inventors
of agriculture were likely sedentary.
Modern landscapes are socio-ecological systems (Olsson et al. 2004, Folke et al. 2014),
where interactions between social and ecological processes result in highly dynamic landscape
patterns. With changing anthropogenic land use, landscapes are perpetually shifting between
varying proportions of social systems, such as cities and agriculture, and natural systems, such as
forests, grasslands, and deserts. In order to accommodate agricultural development natural
1
systems must be destroyed and disrupted due to the limited availability of land. Occasionally
agricultural land is abandoned due to the discovery of more fertile land, the invention of new
technologies, or a reduction in population due to war, plagues and climate (Hart 1968, Kaplan et
al. 2009). Agricultural abandonment allows natural systems to reclaim land and expand across
the landscape. Agricultural development and abandonment drives many of the dynamics and
interactions associated with socio-ecological systems (Lambin et al. 2001, Lambin and
Meyfroidt 2010).
While society relies heavily on agricultural systems for sustenance, the goods and
services provided by natural systems are often overlooked. These services include, but are not
limited to: provisioning (food, water), regulation (air quality, water quality, biological, climate),
habitat (biodiversity, nurseries) and culture (aesthetic, recreation, heritage, education) (de Groot
et al. 2010). Goods and services are maintained through various ecosystem functions. If an
ecosystem becomes destabilized it can result in reduced ecosystem function and the subsequent
loss of those goods and services (Loreau et al. 2001) . Therefore, with our reliance on both
agricultural and ecological systems it is essential to examine how agricultural landscape
dynamics may impact ecosystem functioning and stability at large.
In the current state of global agricultural development the goods and services provided by
ecosystems may already be at risk. Habitat destruction is one of the primary drivers of global
biodiversity loss and agriculture is a major contributor (Wilson 1989). Landscapes undergoing
extensive habitat destruction can expect a 50% reduction in species diversity (Hooper et al.
2012). Biodiversity is considered integral to maintaining ecosystem functioning (McCann 2000,
Folke et al. 2014). Subsequent losses in biodiversity can result in a destabilized system that may
cause an ecosystem to shift to a less desirable state, one with reduced functioning, providing
fewer goods and services to society (Loreau et al. 2001). Unfortunately, the relationships
between biodiversity and ecosystem functioning are less understood at higher ecological scales
and contemporary studies at the community level may not scale properly to the landscape level
(Schindler 1998, Srivastava and Vellend 2005). This poses a problem given that agricultural
2
lands are expected to continue dominating global landscape and little is known as to how they
impact landscapes as a whole.
1.2 Problem Statement
With agriculture expected to maintain a dominant presence on landscapes worldwide, a
comprehensive understanding of its impacts on ecosystem functioning is required. While
research suggests that agricultural development negatively affects local biodiversity, there is a
lack of research regarding its impacts on ecosystem functioning, particularly landscape scale
stability. The majority of biodiversity, ecosystem functioning, and stability research has focused
primarily on community dynamics on natural or semi-natural systems – while landscape
dynamics and the effects of agricultural modification have been relatively neglected, due to the
large spatial and temporal nature of landscapes. With a rich agricultural history and an extensive
European can provide the opportunity to study stability at a landscape scale before and after
agricultural development. We hypothesize that the development of agriculture will reduce
landscape ecosystem stability. Understanding the role of agriculture in landscape stability can
help develop management strategies to ensure ecosystem goods and services can remain intact
while also developing social systems.
1.2.1 Goals and Objectives
The overall goal of this research is to evaluate the role of agriculture in landscape ecosystem
functioning; specifically, whether the introduction and development of agriculture in Europe
reduced landscape stability. To achieve this purpose, four objectives were established:
i.
Identify suitable pollen records for analysis
ii.
Identify the timing of agricultural initiation
iii.
Evaluate changes in landscape stability and composition following initiation
iv.
Identify potential mechanism for any observed patterns
3
1.3 Outline of Thesis
The following thesis is divided into three remaining chapters; a literature review, a
manuscript, and a concluding chapter. The literature review contains background information
relevant to this research, including: ecosystem functioning, biodiversity and stability, landscape
ecology, environmental impacts of agriculture, agricultural history of Europe, and pollen records.
Chapter 3 contains a manuscript detailing the methodology of the research, a description of the
results, and the discussion regarding the results. The final chapter includes an overview of the
findings, limitations and a discussion for further research. Following the final chapter are the
works cited and an appendix containing citations for the pollen records analysed in this research.
4
Chapter 2: Review of Literature
The purpose of this chapter is to outline and review relevant literature to provide
background information necessary for this research. Topics include ecosystem functioning,
stability, and biodiversity, landscape ecology, the history of European agriculture, and the use of
pollen records. These topics will be broken down further and expanded upon with relevant
literature.
2.1 Ecosystem functioning, stability, and biodiversity
In simple terms biodiversity describes the variation and diversity found in life, however, it is
far more complicated. Typically, biodiversity is broken down into several components; species,
ecological, genetic, and functional diversity (Delong 1996, Gaston and Spicer 1998). Species
diversity further consists of two primary elements: species evenness and species richness.
Species richness is a measure of the total number of species in a system while species evenness
is a measure of a species’ relative abundance within the system. Ecological diversity describes
the variation in biotic and abiotic factors within an ecosystem including structure, function, and
process. Genetic diversity describes the genetic variation within species. Spatially, biodiversity is
broken down into alpha, gamma and beta diversity. Alpha diversity describes the number of
species at a specific location, gamma diversity describes the number of species that could be at a
location, and beta diversity describes the variation between locations (Whittaker et al. 2001,
Thompson and Starzomski 2006).
Ecosystem functioning is composed of three elements: (1) stocks of energy and materials
(biomass); (2) fluxes in stocks and energy (productivity, decomposition); and (3) stability of
stocks over time (Holling 1973; Srivastava and Vellend 2005). A high functioning ecosystem
contains high levels of biomass and productivity while also maintaining stability. Ecosystem
stability is often described through resistance and resilience. Resistance describes the ability of
an ecosystem to resist change following a perturbation (McCann 2000). Resilience is often
described in several different ways. Pimm (1991) describes resilience through the ability of an
ecosystem to return to its original state following a perturbation; the less time it takes for a
5
system to return to its original state following a disturbance, the more resilient and stable it is
considered. Resilience is also described as the magnitude of perturbation necessary to cause a
regime shift in a system (Holling 1973, Adger 2006).
The picture becomes more complicated when considering the socio-ecological nature of
landscapes (Fraser and Stringer 2009). In the social context of landscapes resilience can involve
the adaptive capacity of the system to respond efficiently to disturbances and to smoothly
transition between states, (Adger 2006). Fraser and Stringer (2009) describe three components
that characterise resilient socio-ecological systems: ecological, socio-economic, and institutional.
Ecological describes a system where resilience is lost when diversity decreases while
connectivity and biomass increases. The socio-economic dimension describes a situation where
losses in the local economy, social networks or access to capital can reduce the adaptive capacity
of the region. The institutional component describes the ability of formal and informal
institutions to help mitigate perturbations.
Nevertheless, reduced resilience and resistance are associated with a loss in ecosystem
stability. An unstable ecosystem is a system in a constant state of change, shifting between
various ecosystem states. Instability may cause an ecosystem to shift to a less desirable state that
has reduced functioning and provides fewer goods and services to society (Loreau et al. 2001).
Consequently, maintaining ecosystem stability is integral to promoting socio-ecological systems
that provide maximum benefits to society while also remaining resilient and resistant to change.
2.1.2 Relationships between Biodiversity and Productivity
Biodiversity is widely considered integral in the promotion of ecosystem functioning
(McCann 2000, Folke et al. 2014). Previous research indicates that more diverse communities
have higher rates of productivity (Tilman 1999a) [Figure 2.1]. Tilman (1999a) hypothesized two
models describing the relationship between productivity and species diversity: sampling effect
and niche differentiation. Sampling effect describes a situation where species are competing for
the same limiting resource in a completely homogenous environment. In this system, increasing
6
the number of species drawn from a common pool increases, on average, the competitive ability
of the best competitor drawn. A better competitor is more a more efficient resource user leading
to greater productivity overall, and lowered levels of unconsumed resources. Therefore, greater
diversity increases the probability of the system containing a superior competitor, leading to
increased productivity. The second model, niche differentiation, describes a heterogeneous
environment with multiple habitat factors, such as soil pH and temperature, which influence
species abundance. In this model, increasing species diversity increases the probability of filling
more niches and having a greater amount of habitat coverage. Increasing habitat coverage
increases the amount of resource exploitation leading to higher levels of system productivity. In
a study on the contribution of biodiversity to the respiration of bacterial cultures it was
determined that increasing bacterial diversity produced a decelerating relationship with
ecosystem functioning (Bell et al. 2005). Higher levels of species richness increased the
likelihood of synergistic interactions, such as complementarity, between bacteria resulting in
greater rates of respiration.
Figure 2.1 Increasing species richness increases average community biomass,
a proxy for productivity (Tilman 1999a)
2.1.3 Functional Diversity and Productivity
A growing body of literature indicates that functional diversity is as important in
establishing ecosystem functioning as species diversity (Diaz and Cabido 2001, Villeger et al.
7
2008). Functional diversity is generally defined as what organisms do within their community
and ecosystem, specifically organisms’ functional traits and their relative importance within the
system (Petchey and Gaston 2006). Often, functional diversity is described through functional
group richness and composition, the number of and relative abundance of various traits (Petchey
et al. 2004). When identifying functional traits, there can be a high degree of uncertainty in
dividing important and unimportant traits due to the amount of research necessary to label traits
confidently (Petchey and Gaston 2006). Fortunately, for plants there exists an extensive literature
on functionally important traits: examples include nitrogen fixing, C3/C4 carbon fixation,
perennial vs annual species life histories, and specific leaf area (Petchey et al. 2004, Hooper et al.
2005, Petchey and Gaston 2006).
Figure 2.2 Dependence of aboveground plant biomass on the number
of functional groups present (Tilman et al. 1997)
In a grassland-savannah field study, plant species were organized into functional groups
based on intrinsic physiological and morphological differences that influence resource use,
growth and life history. The groups include, C3/C4 carbon pathways, legumes, forbs, and woody
plants (Tilman et al. 1997). Following classification, plant species were added to plots with a
varying degree of species and functional diversity. Results from the study demonstrated that
functional diversity was positively correlated with ecosystem processes [Figure 2.2]. For
example, the inclusion C4 plants led to a 40% increase in productivity while the inclusion of
legumes led to a 59% increase in productivity. Furthermore, the results demonstrated that
8
functional diversity was a greater determinant of ecosystem processes (e.g. plant productivity,
plant total N, soil NO3, plant percent N and light penetration) compared to species diversity.
In a similar study of a serpentine grassland ecosystem, increasing functional diversity
was also found to positively correlate with productivity (Hooper and Dukes 2004). Plant species
were classified into four functional groups: early-season annuals, late-season annuals, perennial
bunchgrasses, and nitrogen-fixing legumes. Species were added to plots with 10 different
combinations of functional groups. In two of three study years, plots with a higher functional
diversity also had greater above-ground net primary productivity. The results indicated that
greater functional diversity led to greater productivity due to increased complementation,
facilitation and competitive effects between functional groups.
2.1.4 Stability-Diversity
The relationship between diversity and stability is a widely debated topic in ecology.
Historically it was believed that diversity enhanced ecosystem stability. It was argued that less
diverse communities were more easily disturbed by perturbations (MacArthur 1955). These ideas
were challenged by the mathematical models of Robert May. May’s models predicted that in
randomly constructed systems increasing diversity would destabilize community dynamics (May
1973a). Yodzis (1981), however, postulated that community interactions were not random but
allocated throughout the system based on the nature of the organisms involved. To test this,
Yodzis created community matrices based on 40 real food webs and found that the systems were
more stable when there were plausible interactions as opposed to random constructs. The
relationship between species richness and stability appeared to be more complex than originally
believed. Today, it is generally theorized that increasing diversity will increase stability,
however, diversity is not considered the primary driver of stability (McCann 2000).
Tilman and Downing (1994) demonstrated that increasing species richness increased the
resilience and resistance of a community. In a long term study on grassland drought resistance,
Tilman and Downing discovered that plots with higher species richness were more resistant to
9
the negative impacts of drought. They hypothesized that increased diversity increased the
probability of having drought resistant plants in the community thus increasing overall
community resistance. Furthermore, during the drought increased growth rates of drought
resistant plants compensated for the decline in drought sensitive plants. Tilman and Downing
(1994) also discovered that increasing species richness increased community resilience. The
plots with higher diversity were faster to return to their original biomass following a drought.
Four years following the drought, plots with high species richness had returned to their predrought biomass while the less diverse plots still had considerably less biomass than their
original state.
Similar to species diversity, functional diversity is hypothesized to develop both
resistance and resilience within ecosystems (Hooper et al. 2005, Villeger et al. 2008).
Functionally, diverse systems are more likely to contain redundant species - species that have
functionally similar traits and carry out similar processes within the ecosystem. Redundancy acts
as a buffer where a diverse community could remain relatively stable after losing one or more
redundant species. In coral reef systems, three functional groups are considered integral in
developing and maintaining resilience: scrubbers, grazers and bioeroders (Bellwood et al. 2004).
The loss of just one of these functional groups can severely inhibit the ability of a coral reef to
resist phase shifts, regenerate, and maintain critical processes following disturbance. Coral reefs
worldwide have already begun to shift into a new degraded state unable to recover or regenerate
partially due to the loss of these functional groups from overfishing (Bellwood et al. 2004).
A functionally diverse ecosystem is also more resistant to invasion (Villeger et al. 2008).
A grassland microcosm study found that an exotic species invading a community was less likely
to establish if the community had a native species representing a similar functional group as the
invader (Dukes 2001, Kennedy et al. 2002). If the functional group of the exotic species was
already represented, competition for resources would limit the destabilizing impact of the
invasive species on the community.
10
The stability of an ecosystem can also be determined by its food web structure (McCann
2000). A food web is a network of interactions between consumers and resources in an
ecosystem. Increasing diversity can increase the number of interactions within a food web. This
in turn could drive stability, however, the interactions have to be skewed toward weak interaction
strengths (Polis and Strong 1996). In a community composed of weak interaction strengths the
impact from losing or gaining a species would be dampened because each interaction has a
minor role in maintaining the current ecosystem state. In general, a loss in diversity is
accompanied by an increase in the average interaction strength in the community. Strong
interactions typically drive the current ecosystem state and the removal of a strong interactor
(e.g. a keystone species) can significantly reduce the stability of the system. In tidal communities
the removal of Pisaster ochraceus, a predatory starfish, completely destabilized the system and
led to a reduction in community complexity (Paine 1984). The removal of the starfish allowed
populations of Mytilus californianus, a mussel, to expand and outcompete other species for
resources (Paine 1984). Furthermore, the addition of a strong interacting species can have
profound effects on the stability of an ecosystem, even in communities with high levels of
diversity. Successful invasions typically destabilize the dynamics of the invaded system because
the invader is a strong interactor within the food web. Exotic animals and plants can cause a
cascade of extinctions in the invaded ecosystem by increasing predation and competition, or by
altering habitat and energy budgets (Mack et al. 2000).
The interactions between biodiversity and ecosystem functions are well researched at the
community scale (Tilman and Downing 1994, Polis and Strong 1996, Tilman et al. 2006).
However, contemporary community-level studies have focused on small spatial and temporal
scales, and therefore these interactions are not properly understood at the landscape scale. A
fundamental problem is that results from community level research may not properly scale up to
the landscape level (Schindler 1998, Srivastava and Vellend 2005). In a spatial context,
community level studies cannot account for the ecosystem heterogeneity found in landscapes. In
a temporal context, community level studies cannot account for slow responding organisms or
for biogeochemical processes. Furthermore, there has been very little research into the role of
biodiversity in landscape level ecosystem functioning.
11
2.1.5 Measuring Diversity
Given the importance of biodiversity in ecosystem functioning, several indices have been
developed to measure and compare diversity across ecosystems. Typically, diversity indices
combine aspects of species richness and evenness as a measure of biodiversity. Two commonly
used indices are the Shannon Index and the Simpson Index (Patil and Taillie 1982, Izsák and
Papp 2000).
The Shannon Index was originally introduced by mathematician Claude Shannon in 1941
as a function to predict the next letter in a message; a common question in information theory
(Spellerberg et al. 2003). The Shannon Index measures the degree of uncertainty in correctly
predicting the succeeding letter. The Shannon index approaches zero when the number of
possible options approaches one, representing a low degree of uncertainty in predicting the letter
(Krebs 1999). Ecologists later applied the Shannon Index as a measure of diversity (Nagendra
2002). Similar to its use in communication theory, in ecology the Shannon Index is the degree of
uncertainty in accurately predicting the species, genera or family of the next individual being
collected. Within an ecosystem a higher degree of uncertainty relates to higher rates of diversity
(Patil and Taillie 1982, Keylock 2005); the greater the species richness and evenness the greater
uncertainty in correctly predicting the species of a randomly selected individual.
The Simpson Index was first introduced in 1949 by Edward Simpson as a measurement
of diversity within a system composed of individuals and groups (Simpson 1949). The original
Simpson index measures the probability that two separate individuals in the system selected at
random belong to the same group; with a lower probability representing a greater level of
diversity (Krebs 1999). The Gini-Simpson Index was modified from the original index such that
it evaluates the probability that two randomly selected individuals belong to different groups.
With the modified index a higher probability therefore represents a higher level of diversity.
A common criticism of the Shannon and Simpson indices is that they both primarily
measure species diversity while disregarding both the genetic and ecological aspects of diversity
12
(Izsák and Papp 2000). Furthermore there is no a priori reason to believe that these indexes,
derived from information theory, have any biological basis (Hurlbert 1971).
While it is generally assumed that species diversity is positively correlated to functional
diversity the relationship is complex and inconsistent (Diaz and Cabido 2001). In various
experiments, functional diversity - including both functional richness and composition - was
found to be more consistently associated with ecosystem function compared to species richness.
These differences have led to the creation of new indices attempting to accommodate ecological,
and partially genetic, diversity into diversity measures (Izsák and Papp 2000). For example a
subset of indices measure diversity by comparing “taxonomic distances”, or functional
relationships between species (Izsák and Papp 2000). Taxonomic distances describe the
connectedness of various species using their evolutionary history. Jizhong et al. (1991)
developed an index that combines structural diversity, defined as the diversity around interaction
relationships, and species diversity as a measure of ecological diversity. While many of these
other indices are more comprehensive compared to the Simpson and Shannon indices, they can
be more difficult to calculate in complex systems requiring more research into the various
functional relationships present or the evolutionary history of each species present.
2.2 Landscape Ecology
Landscape ecology was first introduced in 1939 by the German geographer Carl Troll
(Troll 1939). Troll originally described landscape ecology as a “way of looking,” a field of
thought which combined ecological process with geographical perspective (Antrop et al. 2013).
These concepts developed into an understanding that issues regarding society and the
environment were highly complex and required a multidisciplinary approach. Modern landscape
ecology studies the ecological processes, dynamics, and interactions that cause the spatial
heterogeneity of ecosystems across landscapes (Turner 1989, Forman 2001). Landscape ecology
can be further enhanced with the concept of socio-ecological systems (Olsson et al. 2004, Folke
et al. 2005). Social and ecological systems are highly interconnected and thus cannot be
considered discrete entities within a landscape. The interconnectedness of socio-ecological
systems cause landscape patterns to be highly dynamic and shift between varying compositions
13
of social and ecological systems. In particular, modern landscapes shift between varying levels
of agricultural land use. As such, it is important to study the impacts agriculture may have on the
ecology of the landscape.
2.2.1 Agricultural impacts
Habitat destruction is one of the primary drivers of global biodiversity loss (Wilson
1989). In landscapes with extensive habitat destruction (~90%) there is an expected reduction of
species diversity by 50% (Hooper et al. 2012). In the next 50 years it is estimated that an
additional 10 million km2 of natural land will be converted for agricultural use (Tilman et al.
2001). The resultant loss in habitat is hypothesized to have profound impacts on the biodiversity
of the landscape (Lockwood 1999).
The development of agriculture begins a process of habitat disruption where the original
landscape undergoes perforation, dissection, fragmentation, shrinkage, and attrition (Forman
2001, Wade et al. 2003). Historically, when agriculture is first developed the landscape becomes
perforated with small crops and pastures. With the development of infrastructure such as roads
and railways, the landscape becomes dissected and broken into sections and boundaries. As
agriculture becomes further developed the original landscape ecosystems become fragmented
into patches. These patches then undergo shrinkage and attrition where they start to decrease in
size until they disappear into the agricultural landscape. The resultant ecosystem patches become
more susceptible to further impacts due to edge effects, isolation, and connectivity issues. In the
modern world, agriculture is considered a homogenizing force on the landscape, where any
remaining ecosystem patches are removed, and the entire landscapes are covered in agricultural
systems (Tilman 1999b)
Edge effects are defined as the impacts of one ecosystem on the species composition and
structure of an adjacent distinct ecosystem (Murcia 1995). Edge effects are typically framed as
the influence of a non-forest ecosystem on an adjacent forest ecosystem. Forests are particularly
prone to edge influences due to increased edge creation associated with fragmentation (Harper et
14
al. 2005). Edge effects typically occur due to microclimate variability between the edge and
interior of the patch. Forest edges are typically warmer, receive more wind and light, and are
drier compared to the interior (Chen et al. 1999). Microclimate variability can eventually lead to
changes in forest processes, including increased productivity, evapotranspiration, and nutrient
cycling (Harper et al. 2005). Furthermore, many biotic changes will occur at the edge including
increases in understory cover and sapling density (Chen et al. 1992). At the edge, there is a
proliferation of shade-intolerant species which form a dense thicket acting as a barrier between
the edge and interior of the patch (Laurance and Yensen 1991, Forman 2001). Over time, the
changes in forest processes and structure can result in significant changes in species composition
and richness of the forest patch.
Another issue that affects fragmented landscapes are reductions in patch corridor
connectivity (Forman 2001). Highly connected patches act as a habitat network potentially
allowing organisms to disperse more easily throughout a landscape; these pathways are known as
corridors (Gustafson et al. 1996). An impact of fragmentation is the loss of corridors through
habitat destruction. As the landscape loses habitat connectivity the ecosystem patches become
further isolated (Franklin and Forman 1987). The loss of a patch corridor network can have
profound impacts on several biotic processes (Schumaker 1995). Corridor loss can reduce the
migration, foraging, and mate-finding capacity of mammals. Furthermore, the loss of corridors
can reduce the capacity of plant species to disperse throughout the landscape; any plant growth
on agricultural lands is removed and as such, seeds can only disperse to other patches. The
reduced capacity for migration and dispersion can impact the genetic structure of populations,
particularly plant populations. Isolation can decrease genetic variation found in metapopulations
through inbreeding depression and random genetic drift (Young et al. 1996). Furthermore, the
loss of habitat can result in a general loss of biodiversity in the region (Lockwood 1999).
In the preceding section, it was established that the relationship between diversity and
ecosystem functioning was not well understood at a landscape level. This gap in knowledge is
especially pertinent when considering agriculture, a dominant system on modern landscapes.
15
Unfortunately, there is little consensus regarding the causal relationships behind human driven
environmental change on biodiversity, productivity, and ecosystem stability (Hautier et al. 2015).
2.2.2 Landscape Ecology as a tool for Governance
Until recently there has been little development on theory integrating social and
ecological systems through landscape ecology (Fry 2001). Thus, landscape ecology as a tool for
governance is a relatively new topic in the field of environmental management. Evidence
suggests that the differences lie in scale and organization. In traditional management, governance
would organize ecosystems and society into individual components in a purely spatial context
(Tress et al. 2005). That is, each component occupied a certain physical space and could be
managed separately from other components - unless a direct relationship was present.
Furthermore, each component was typically studied by a single discipline; ecologists and social
scientists rarely interacted to inform decision makers on the relationships between different
landscape components (Opdam et al. 2002, Sepp and Bastian 2007). By contrast, landscape
governance takes a multi-disciplinary approach (Tress et al. 2005, Wu 2006). Therefore,
governance with landscape ecology seeks to integrate both ecological and social knowledge at
different scales and levels of organization to accomplish sustainable landscape management. As
such, governance can use landscape ecology to maintain both the social and ecological integrity
of a landscape or region (Field et al. 2003).
Integrating multiple disciplines for governance can be difficult due to the complexity of
landscape systems; however, Nassauer and Opdam (2008) developed a paradigm that can be
used to guide this process. Their paradigm involves the addition of “design” (the act of deciding
on, and changing, landscape patterns) to the typical pattern-process relationships found in
landscape ecology. They argue that three phases are necessary for the integration of design,
landscape ecology and governance: (1) Process knowledge; (2) Generalizable pattern rule; and
(3) Place specific design. The first phase deals with the accrual of scientific knowledge on the
processes that cause observed landscape patterns (e.g. Causes for biodiversity patterns). Detailed
knowledge at different scales of social and ecological processes are necessary for effective
16
governance. The second phase involves the integration of societal values with sets of pattern
rules: the landscape patterns that society views as most beneficial, both socially and ecologically.
Integration could come through workshops and forums that allow the exchange and creation of
knowledge between scientists, stakeholders, and other involved parties. The third phase deals the
place-specific design. Decision makers will envision patterns that express values and implement
management techniques to produce them on targeted landscapes. In this way, social values act as
goals to be achieved in landscape design. Once implemented, the process can repeat itself;
science can monitor and assess the new landscape and begin the process of re-designing.
Swaffield (2012) further developed the paradigm by stating that the process could begin with the
creation of a design value framework; identifying values within social institutions to create a
framework for a values based approach to landscape governance. Science is then used to inform
strategies and monitor outcomes.
The paradigm developed by Nassauer and Opdam (2008) offers insight into several
components necessary for the integration of landscape ecology into governance: valuation,
integration and scale. Placing social value on the environment can be a difficult and complicated
process (Dietz et al. 2005). The environment may have intrinsic, cultural, or economic value, all
of which can be difficult to quantify and compare between potential landscape designs. One
method of estimating value is by breaking the environment down into ecosystem services; which
are goods and services that directly or indirectly satisfy human needs (Termorshuizen and
Opdam 2009, de Groot et al. 2010). Ecosystem services include a wide variety of both
ecological and social dimensions including air quality regulation, recreation, aesthetics, gene
pool protection and many more. Two broad categories of values can be applied to ecosystem
services: use and non-use. “Use” values describe the direct consumption (e.g. resource
extraction) or non-consumption (e.g. recreation), and indirect use values (e.g. air purification).
“Non-use” values describe the intrinsic value of the landscape. In order to quantify these values
two different approaches can be used: economic and non-economic (de Groot et al. 2010). Once
societal values are quantified into ecosystem services they could then be incorporated into the
landscape design to maximize benefits.
17
Another component essential for governance is the integration of the varying disciplines
and bodies of knowledge associated with landscape ecology (Cash et al. 2003). It has been
argued that scientists can no longer act as bystanders to governance; instead, they need to
actively participate in the governance process (Nassauer and Opdam 2008). Natural and social
science disciplines are only a few of the many bodies of knowledge that need to be integrated
into design. Traditional and local knowledge systems should also be considered and incorporated
into the governance process (Folke 2009).
A final component necessary for effective landscape governance is the identification of
ecological, social, and political scales (Görg 2007, Termorshuizen and Opdam 2009).
Landscapes are expansive, covering a large spatial area and as such encompass many
organizational levels at different scales. Identifying the scales helps in the labelling of
stakeholders, actors, and decision makers in the governance process. Landscape governance can
fall into the domain of municipal, provincial/territorial, and federal levels. The role of each level
needs to be properly identified and outlined for efficient and effective governance. Furthermore,
landscapes cross international borders and encompass a variety of social components. The
public, private land owners, NGOs, indigenous communities, and scientists are all potential
actors, decision makers, and stakeholders that need to be identified and reached out to. There is
much to be considered in developing a theory of landscape ecology in ecological governance,
and that this process needs to occur at multiple levels of analysis including societal values,
integration strategies, and the various scales that influence landscape design.
2.3 Ecological and Agricultural History of Europe
2.3.1 The Origins of Agriculture
The origins of agriculture have been traced to three distinct locations: the Far East, Near
East, and Mesoamerica (Harlan 1971). These three centres acted as the cradle of agricultural,
where early cultivators experimented with cultivating, harvesting, and domestication of plants
and animals. Sauer (1952) outlines six basic premises on the origins of agriculture: (1)
18
Agriculture did not originate from famine as a hungry society would not have the means to
undertake the necessary steps to develop agriculture, (2) Local diversity is necessary for
experimentation implying varied ecosystems or climate, (3) Without the means to drain and
irrigate river valleys primitive cultivators originally established agriculture in hills and
mountains, (4) Agriculture originated in woodlands, offering open spaces and soils suitable for
cultivation, (5) Primitive cultivators likely developed skills pre-disposing themselves to
agricultural experimentation, and (6) Given the need to protect crops, the inventors of agriculture
were likely sedentary. These conditions gave the opportunity for early societies to experiment
with early proto-agricultural activities eventually leading to the development of agriculture. Once
developed, agriculture would begin the process of spreading across landscapes worldwide.
2.3.2 Ecological History of Europe
Prior to the advent of agricultural development the majority of Europe was largely
covered by forest. It is difficult to determine the exact composition and structure of the primeval
forests, however, the paleoecological records and ancient forest stands offer insight into the
forests that once covered Europe (Bengtsson et al. 2000).
In Northern Europe a boreal forest covered the majority of Fennoscandia. The boreal
forest developed 18,000-21,000 years ago following the termination of the last glacial period.
Disturbances such as fire, wind, and snow maintained high levels of diversity and heterogeneity
across the landscape (Kuuluvainen and Ankala 2011, Halme et al. 2013). Pioneer species such as
Pinus sylvestria and Betula spp. became dominant while other genera such as Quercus, Alnus,
Sorbus and Ulmus were slower at developing in the forest ecosystem. Over time a shift in
climate and fire disturbance levels began altering forest composition allowing Picea abies to
become the dominant species forming the boreal forest seen today (Bjune et al. 2009).
Throughout Western and Central Europe deciduous forest dominated landscapes
(Bengtsson et al. 2000). The ancient deciduous forest was dominated by communities of Fagus
and Quercus species, however, the deciduous forest was highly diverse with at least 132 species
19
(Hermy et al. 1999). It is believed that the composition of these forests were highly influenced by
the presence of now extinct mega-herbivores that once roamed Europe (Mitchell 2005).
2.3.3 Agricultural History of Europe
The exact location where agriculture was first introduced in Europe is unknown,
however, it is estimated that it was introduced approximately 10000 years ago from Northern
Mesopotamia. Once introduced, agriculture dispersed through the Balkans and along the
Mediterranean coast, until it reached North-West Europe 3000 years after its introduction
(Pinhasi et al. 2005). Along with agriculture, the domestication of cattle, sheep and goats also
spread throughout Europe (Beja-Pereira et al. 2006). Initially, agriculture was small in scale as
communities shifted from hunter gathering to harvesting small amounts of cereal grains and
maintaining small herds of livestock; however, around 5400 BCE agriculture development began
to intensify.
In 5400 BCE, a culture known as the Linear Pottery Culture (Linearbandkeramik or
LBK) began to spread throughout Europe (Bogucki 1984, Behre 2007) . The LBK marked the
first major transition from hunter gatherer societies to communities built upon an agrarian
economy. Utilizing slash and burn agriculture these communities harvested a variety of crop
plants including wheat, barley, peas, and lentils. Livestock populations began to grow as
communities began managing herds of cattle for meat and milk. Networks were formed between
communities to exchange food, ceramics and tools. (Zvelebil and Dolukhanov 1991, Thorpe
1999). While agricultural development had accelerated during the LBK it was still small in scale
and European forest remained dominant on European landscapes.
Agriculture continued to develop slowly, maintaining a minimal impact on the landscape
until approximately 1000 BCE when the European population began to rapidly expand. Entering
into the Classical Era (~600BCE), population densities throughout Europe began exceeding
levels sustainable via hunter-gathering or preindustrial agriculture. In order to accommodate the
growing population there was widespread deforestation to convert large tracts of land into crops
20
and pastures [Figure 2.3] (Kaplan et al. 2009). Over the next millennia the development of new
techniques and technologies allowed the Greek and Roman cultures to industrialize agriculture
(White 2014). As the Roman Empire (~100 BCE) expanded across Europe small scale farms
were replaced with large estates covering hundreds of acres (Wolf 1987). The forested landscape
was transitioning to a landscape of crops and pasture. Rates of deforestation continued to rise
along with population until the collapse of the Roman Empire around 400 CE. A period known
as the Migration Period (400 CE-800CE) followed the collapse. This period was characterized by
large scale barbarian invasions across Europe, a slight shift to a colder drier climate, and plagues
(Wanner et al. 2008, Büntgen et al. 2011). These factors combined to form a period of population
stagnation allowing for a short period of stable forest cover and afforestation over the period
from ca. 400 CE to approximately ca. 1000 CE (Kaplan et al. 2009). As feudal societies formed
and the climate ameliorated in the Early Middle Ages (ca 1000 CE), the European population
began to stabilize and grow once again. This led to an increase in deforestation as agricultural
land expanded. Deforestation occurred gradually until 1347 CE when the Black Death ravaged
the European population. It is estimated that 30% of the European population died from the
plague (Williams 2000, Rabbinge and van Diepen 2000). Furthermore, a climatic period known
as the Little Ice Age reduced global temperatures likely reducing agricultural yield and causing
widespread famine throughout Central Europe (Büntgen et al. 2011). Due to declining
populations, large tracts of agricultural land were abandoned allowing for an expansion in forest
cover across landscapes. Following the Black Death, rates of deforestation increased as European
populations stabilized and continued to grow. It is estimated that in the late middle ages 40% of
total land use was farmland compared to <5% in the 6th century (Williams 2000). Around the
17th century, new technologies and methods increased the efficiency of agricultural lands and
resulted in a short expansion of forest cover. However, this was short lived and in the 18th and
19th centuries the current level of agricultural production was unable to sustain the growing
European population accelerating the conversion of forests to agricultural land (Rabbinge and
van Diepen 2000, Kaplan et al. 2009) The agricultural history Europe provides the context to
which the impacts agriculture may have on ecosystem functioning can be studied.
21
Figure 2.3 A timeline of arable land under cultivation in Europe from 3000 years BP to 150 years
BP. Periods of growth represent agricultural expansion and periods of decline represent agricultural
abandonment (adapted from Kaplan et al. 2009).
The modern European landscape is very different from the vast forests that once
dominated the continent. Today, the landscape is a mosaic of urban, agricultural and natural
systems. Current estimates place forest cover at 34% of total land use, cultivated land at 45%,
and pastures at 15% (Mucher et al. 2010, FAOSTAT 2012). Furthermore, 50-80% of the current
forest cover is estimated to have developed on lands that were once used for agricultural
purposes, implying those forests may be influenced by the legacy effects of agricultural soils
(Dambrine et al. 2007). It is also estimated that only 0.2% of the forests in Central Europe are in
their natural state, relatively unaffected by anthropogenic developments (Mucher et al. 2010).
Even though European populations are on the rise, in the last few decades forest cover
has increased in some countries including: France, Italy, Portugal, Denmark, and Switzerland
(Mather et al. 1998). New innovations in technologies and methods allow for a greater
productivity per acre; sustaining the growing population densities while also negating the need
for agricultural expansion (Rabbinge and van Diepen 2000). Even though agricultural land use is
22
receding in some countries it is still a dominant land use throughout Europe and its impacts on
landscape ecology need to be addressed.
2.4 Pollen Records
Pollen records are developed through the analysis of lake sediment cores (Simpson 2012).
After plant species release pollen into the atmosphere, a proportion of the pollen grains will land
on nearby bodies of water (lake, pond, bog, etc.) and begin to accumulate in the sediment. In
anaerobic environments, pollen grains remain well preserved. Over time sediment and pollen
continue to accumulate on the lake bed preserving earlier points in the ecological history of the
region at greater depths within the sediment. Through dating different depths of a sediment core
and counting the number and type of pollen grains at each depth measurement, the relative
species abundance in the region can be estimated (Birks et al. 2016).
The size of the pollen source area is positively correlated with the size of lake in the basin;
for example 35-40% of the pollen loading in a small lake (radius of 50m) is from plants within
300-400m away compared to a medium lake (radius of 250m) where 35-40% of pollen loading is
from plants within 600-800m (Sugita 1994). Generally, 90% of the pollen in a sediment core is
from an area within 10 km2 of sediment core. Covering such a large area allows pollen records to
represent landscape scale vegetative dynamics which can then be used to study stability.
When collecting sediment cores not all lakes are created equal in their suitability for
developing pollen records (Jacobson and Bradshaw 1981). The presence of inflowing streams
can substantially alter the size pollen source area with the streams transporting pollen into the
lake. Lake morphology and depth can impact rates of sedimentation and pollen redeposition. In
shallow dimictic lakes, pollen and sediment is re-suspended at irregular intervals due to seasonal
circulation and high winds. Deep lakes with a small littoral zone are more likely to produced
varved sediments indicating little resuspension of pollen and sediments. Changes in the local
hydrogeology can alter deposition rates over time or even alter the size of the pollen base area
through the formation or loss of inbound streams. Glacial till containing pollen from an earlier
23
age can possibly be transported into the lake skewing the records. Ideally, when selecting a lake
for pollen research a lake should be deep, have a small littoral zone, and have few inbound
streams.
2.4.1 Pollen Record Source
The European Pollen Database (EPD) is the primary source for European pollen data. The
EPD is an online, open source database hosted by the Mediterranean Institute of Marine and
Terrestrial Biodiversity and Ecology developed for the scientific community. Data submitted to
the database must adhere to a strict set of guidelines:
1. Data must consist of original counts, not percentage or digitized data.
2. The database must contain the original taxonomic identifications, with exceptions of
exact nomenclatural synonymy. As primary entries, taxa will not be lumped into higher
taxonomic groups in the database. For practical reasons, higher-level hierarchies will
exist within the database in two ways; the first will be according to pollen morphology,
the second according to plant taxonomy.
2.4.2 Indicators of Human Development
Pollen records contain a variety of proxies that can be used as indicators of past human
developments (Behre 2007). One such indicator is the presence of charcoal, typically indicating
the development and long term settlement of human populations – especially when they are
associated with vegetation types that don’t often burn naturally (Tinner et al. 1998, Finsinger et
al. 2005). Other indicators can narrow-down the specific type of human land use occurring in the
pollen source area, such as agriculture. The presence of agricultural development can be
identified using several different pollen types including Plantago lanceolata, Artemisia, Rumex,
Ambrosia, Poaceae, and Cerealia-type (Behre 2007). The primary indicator of agricultural
development is Cerealia-type pollen. Cerealia-type pollen is a subset of Poaceae, representing
cereal species that were developed for cultivation (Bottema 1992). Cerealia-type pollen is
separated from wild Poaceae through analysing the diameter of each pollen grain. Cerealia-type
24
species usually have pollen grains greater than 40 µm in diameter while wild Poaceae species
have grains smaller than 40 µm (Leroi-Gourhan 1969, Behre 2007). The distinction between
cultivated and wild Poaceae pollen makes Cerealia-type pollen well suited as the primary
indicator for the presence or absence of agricultural development in the surrounding region of the
pollen record (Leroi-Gourhan 1969). Compared to Cerealia-type pollen the majority of other
indicator species are apophytes; plant species that thrive in disturbed ecosystems (Behre 2007).
While trends in apophyte pollen can be indicative of anthropogenic activity, the exact type of
land use cannot be accurately distinguished in the record and as such apophyte species should
primarily function as a secondary indicator of agricultural development.
2.4.3 Radiocarbon Dating
A common method to date pollen records is through radiocarbon dating techniques
(Brown et al. 1989). Carbon-14 (C14) naturally occurs in the atmosphere and is taken up by
plants in the form of radioactive CO2 (Blaauw and Christen 2005). Through cellular respiration
and other internal processes, 14C from the CO2 is incorporated into plant tissues, including
pollen. Following the release of pollen into the atmosphere, C14 is no longer incorporated into
the pollen and it begins the process of radioactive decay. Radiocarbon dating involves measuring
the ratio of C14 to C12 in an organic compound, such as pollen, and using the half-life of C14
(~5730 yrs) to estimate the date in which that compound stopped incorporating C14.
A primary assumption of radiocarbon dating is that the concentration of 14C in the
atmosphere has been constant, however, given that 14C concentrations have varied over time
radiocarbon measurements have to be calibrated to determine accurate calendar year
measurements (Blaauw and Christen 2005, Blaauw 2010). The common technique for calibrating
sediment cores, such as pollen records, are using age-depth models (Blaauw 2010). Age-depth
modelling combines calibrated 14C dates at various depths with the sediment accumulation rates
to produce a distribution curve that can estimate calibrated 14C at specific depths.
25
2.4.4 Analyzing Pollen Records
Stability in pollen records can be analyzed through measuring rates of change between
pollen spectra. Differences between pollen spectra are generally measured in terms of
dissimilarity (Smol et al. 2001). Dissimilarity is calculated using dissimilarity coefficients. There
are three primary types of dissimilarity coefficients: unweighted, equal weight and signal-tonoise (Prentice 1980, Overpeck et al. 1985, Jacobson et al. 1987). Unweighted coefficients have
values that are predominantly influenced by common pollen types with large ranges, with rarer
pollen types having relatively little influence on the coefficient. Equal weight coefficients upweigh rarer pollen types and down-weigh common pollen types, however, when there are more
than 15 pollen types the rarer pollen types can be weighed excessively. The third coefficient
type, signal-to-noise, is designed to maximize the differences between pollen spectra while
reducing the impact of random variation affecting pollen production, dispersion and deposition.
Similar to equal weight coefficients signal-to-noise coefficients increase the sensitivity of the
analysis to rarer pollen types, however, to a lesser degree. A signal-to-noise coefficient, the
square chord distance, has been identified as the preferred coefficient to use in pollen record
analyses given its simplicity, and its tendency to yield similar results to other signal-to-noise
coefficients (Overpeck et al. 1985).
Pollen dissimilarity data are occasionally converted into rates of change in order to
measure trends in pollen over time (Simpson 2012). Recently, studies have begun to smooth
pollen data prior to calculating rates of change to give the data constant time intervals (Bennett
and Humphry 1995). Compared to unsmoothed values smoothing tends to reduce the rate of
change values by approximately half, however the overall trends remain the same and smoothing
has little affect when using simple dissimilarity coefficients (Bennett and Humphry 1995).
When analysing pollen record both the Shannon and Simpson indices are commonly used
as indicators of local vegetative diversity (Moore 1973, Morley 2008). A rarefaction analysis is
another commonly used technique to measure pollen diversity (Birks et al. 2016). Diversity
indices fail to accommodate for variable pollen production and dispersion between taxa that can
26
bias their numerical representation in the record (Birks and Line 1992). A rarefaction analysis
resamples the total pollen counts in a record to a standard level to remove those biases and
produces an expected number of species as a measure of diversity (Birks and Line 1992).
27
Chapter 3: Manuscript
3.1 Introduction
Agriculture is often considered a hallmark of human civilisation. Compared to hunting
and gathering, agriculture provides a stable and productive source of food for expanding
populations. Without agriculture, human population growth would have been substantially
limited and would likely have never reached current levels. Historians believe that agriculture
likely originated from three primary centres: the Far East, Near East, and Mesoamerica (Harlan
1971). Not all societies were created equally in their ability to experiment with agricultural
development. Certain conditions were more suitable for experimenting with cultivation, therefore
early cultivators were likely sedentary, had plentiful resources, and lived in hills and forests with
high levels of diversity and a varied climate (Sauer 1952).
Statistics on national land uses compiled by the Food and Agricultural Organization of
United Nations estimate that by 2015 approximately 44 million km2 of land had been converted
into crops and pastures (FAOSTAT 2012). This figure is expected to rise by 10 million km2
within the next 50 years (Tilman et al. 2001). With the global population projected to reach
between 9.6 and 12.3 billion by the year 2100, landscape patterns are predicted to be further
dominated by agricultural development (Lotze-Campen et al. 2010, Gerland et al. 2014).
While society relies on agricultural systems for sustenance, the goods and services
provided by natural systems are often overlooked. These services include, but are not limited to:
provisioning (food, water), regulation (air quality, water quality, biological, climate), habitat
(biodiversity, nurseries), and culture (aesthetic, recreation, heritage, education) (de Groot et al.
2010). Agricultural development is often accompanied with the destruction, disruption, and
fragmentation of natural systems, possibly putting these goods and services at risk (Wade et al.
2003, de Groot et al. 2010).
28
With agriculture expected to maintain a dominant presence on global landscapes the
primary goal of this paper is to evaluate the role of agricultural development on landscape scale
stability. To achieve this purpose, several objectives were established: (1) Identify suitable
pollen records for analysis, (2) Identify the timing of agricultural initiation, (3) Evaluate changes
in landscape stability and composition following initiation, and (4) Identify potential
mechanisms for any observed patterns.
Given habitat destruction is considered a primary driver of global biodiversity loss, there
is a general consensus that agriculture is harmful to biodiversity (Wilson 1989, Lockwood 1999).
Research indicates that biodiversity is integral to the promotion of ecosystem functioning, where
more diverse communities have increased productivity and stability (Tilman 1999a, Folke et al.
2014). Consequently, losing biodiversity can result in the loss of productivity and stability. In
particular, losing ecosystem stability can have profound effects on the structure and function of
an ecosystem. Ecosystem stability is typically divided into three components: resistance,
resilience, and temporal variability (Pimm 1991, Tilman 1999a, McCann 2000). Resilience
describes the ability of an ecosystem to return to its original state following a perturbation.
Resistance describes the ability of an ecosystem to resist change following a perturbation.
Temporal variability is a degree of constancy, measured as the variance in a time series of
abundances scaled by the mean (i.e. the coefficient of variation) (Tilman 1999a). The coefficient
of variation measures a ratio between the mean abundance and the temporal standard deviation
where a lower value represents greater stability (CV = 100*σ/µ). Ecosystem instability is
associated with a loss in either resilience or resistance or both. Following a loss in stability,
perturbations may cause an ecosystem to shift to a less desirable state that provides less goods
and services to society (Loreau et al. 2001). Consequently, maintaining ecosystem stability is
integral to promoting socio-ecological systems that provide maximum benefits to society while
also remaining resilient and resistant to change.
The concept of stability becomes more complicated when considering the social aspect of
ecosystems and landscapes (Fraser and Stringer 2009). Similar to ecosystems, social systems
maintain contain levels of adaptive capacity; the capability of the system to respond to
29
perturbations, and smoothly transition between states (Adger 2006). In a social context, Fraser
and Stringer (2009) suggest two types of social resilience: socio-economic and institutional. The
socio-economic component describes a situation where losses in the local economy, social
networks or access to capital can reduce the adaptive capacity of the region. The institutional
component describes the ability of formal and informal institutions to help mitigate
perturbations.
The interactions between biodiversity and ecosystem stability are well researched at the
community scale (Tilman and Downing 1994, Polis and Strong 1996, Tilman et al. 2006).
However, contemporary community-level studies have focused on small spatial and temporal
scales, and therefore these interactions are not well understood at the landscape scale. A
fundamental problem is that results from community level research may not scale up to the
landscape level (Schindler 1998, Srivastava and Vellend 2005). In a spatial context, community
level studies cannot account for the ecosystem heterogeneity found in landscapes. In a temporal
context, community level studies cannot account for slow responding organisms or for
biogeochemical processes. Furthermore, there has been very little research into the role of
biodiversity in landscape level ecosystem stability. These issues also apply to research regarding
agricultural development, as there has been little research regarding the impacts of agricultural
development on landscape scale stability. Therefore, with our reliance on both agricultural and
ecological systems, it is essential to examine how agricultural landscape dynamics may impact
ecosystem stability at local to regional scales.
Conventional definitions of stability tend to focus on local, small scale dynamics associated
with single ecosystems and may not be suitable when studying large scale, multi-system
landscapes dynamics. For this study, we define landscape ecosystem stability similar to
conventional definitions, however, applied at the gamma scale. From an ecological perspective
the gamma scale is derived from gamma diversity, which represents the total biodiversity
between ecosystems that compose a landscape. Landscapes are typically composed of different
ecological and social patch types, each with a different level of stability. Therefore, at the gamma
30
scale, ecosystem stability represents a landscape where the patch-type composition can remain
resistant and resilient to change through both ecological and social mechanisms.
3.2 Methods
3.2.1 Area of Study
The rich agricultural history of Europe provides an ideal test case to study the impacts of
agricultural development on landscape scale stability. Agriculture was first introduced in Europe
approximately 10,000 years BP likely from Northern Mesopotamia (Pinhasi et al. 2005). Once
introduced, agriculture spread across Europe remaining relatively small scale until approximately
3000 years BP when European populations began to rapidly expand. From the inception of
agricultural development, agriculture and population growth have been highly coupled; rising
populations result in agricultural expansion while high mortality rates result in agricultural
recession (Kaplan et al. 2009). Current estimates place forest cover at 34% of total land use,
cultivated land at 45% and pastures at 15% (Mucher et al. 2010, FAOSTAT 2012).
3.2.2 Pollen Records
Pollen records were collected from the European Pollen Database (EPD). The EPD is an
online, open source database hosted by the Mediterranean Institute of Marine and Terrestrial
Biodiversity and Ecology. The purpose of the database is to provide free access to European
pollen data to the scientific community. Researchers who collect and develop pollen records in
Europe are encouraged to upload their data to the database. Criteria to upload data to the
database include: data in original counts, data contains original taxonomic identification, data are
labelled as unrestricted or restricted, and unrestricted data are available for open use.
31
For this project, pollen records were selected on the following criteria:
1) The record contains data within the Holocene that overlapped with agricultural
development
2) Time step resolution was sufficient to calculate relevant statistics
For this project, pollen records were filtered on the following criteria:
3) The records proved to contain evidence of agricultural development
4) The records further contain both a period of pre and post-agricultural development.
3.2.3 Transforming/Reformatting the data
The pollen records were first transformed and reformatted using the statistical software R
(R v.3.3.3). Following reformatting, all non-pollen and unrelated pollen data were removed,
including algae, charcoal, added pollen (i.e. lycopodium tablets), stomata, and analytical
components (e.g. sample volume) (Kaland and Stabell 1981, Finsinger et al. 2005, Bjune 2005).
Prior to conducting the analyses, the pollen data in each record were converted from total counts
into proportions.
3.2.4 Calibration
54 records contained uncalibrated 14C dates. These records were calibrated to calendar
years using clam, a code written for R that produces non-Bayesian, classical age-depth modelling
for calibrating and estimating 14C dates (Blaauw and Christen 2005, Blaauw 2010).
3.2.5 Agricultural Indicator
Cerealia-type pollen was used as the sole indicator of agricultural development. Cerealiatype pollen is a subset of Poaceae (true grasses), representing cereal species such as barley and
wheat, that were developed for cultivation (Bottema 1992). Cerealia-type pollen is separated
from wild Poaceae through analysing the diameter of each pollen grain. Cerealia-type species
32
normally have pollen grains greater than 40 µm in diameter while wild Poaceae species normally
have grains smaller than 40 µm (Leroi-Gourhan 1969, Behre 2007). The distinction between
cultivated and wild Poaceae pollen makes Cerealia-type pollen well suited as an accurate
indicator of the presence or absence of agricultural development in the region surrounding the
pollen basin (Leroi-Gourhan 1969). Other families/genera used in agriculture, such as Fabaceae
and Cannabis/Humulus, are not differentiated between wild and cultivated strains in the pollen
record making them unreliable indicators of agricultural development (Behre 2007). Indicators
such as charcoal, Plantago, or Ambrosia provide evidence of general human activities in a region
and are not purely indicative of agriculture. As such, they were not used as an indication of
agricultural development (Behre 1981)
3.2.6 Regime Shift Detection and Application
To estimate the inception of agricultural development in each pollen basin, a regime
detection tool, Shift Detection v. 3.2, was applied to the Cerealia-type proportion data in each
record. Shift Detection v. 3.2 detects a regime shift by sequentially conducting a Student’s t-test
on each new observation of a dataset to determine if it significantly deviates from the mean value
of the current regime (cut off = 10, α=0.05) (Rodionov 2004, 2006, Rodionov and Overland
2005). The regime detection tool fulfills two criteria: (1) It can determine if there is a period of
pre and post agricultural development within each record, and (2) It can estimate the initial year
of agricultural development in the record. The period of pre-agricultural regime was determined
through analyzing the first regime. If the first regime had a mean of approximately 0 it implies
that agriculture had not been developed in the region given that Cerealia was not present or
abundant. Assuming the first regime was a period of pre-agricultural development, the initial
year of agriculture was identified as the time step of the first regime shift. The period following
the first regime shift represented post-agricultural development.
If a record met these two criteria the initial year of agricultural development was
deducted from all time step values in record in order to set the initial year of agricultural
development as 0. Additionally, the time step data was converted such that the period of pre33
agricultural development corresponded to negative values and the period of post-agricultural
development corresponded to positive values.
3.2.7 Species Distributions
Table 3.1 Taxa composing the pollen type categories. Poeceae, Cerealia, and Cyperaceae are single taxa categories
Pollen Categories
Tree Pollen Types
Cultivated Pollen Types
Other Herbaceous Pollen Types
Taxa
Abies
Acer
Alnus
Betula
Carpinus
Cornus
Corylus
Fagus
Frangula
Fraxinus
Hedera
Ilex
Juglans
Juniperus
Myrica
Picea
Pinus
Pyrus
Quercus
Salix
Tilia
Ulmus
Cruciferae
Humulus
Leguminosae
Rubiaceae
Umbelliferae
Plantago
Compositae
Asteraceae
Artemisia
Rumex
Chenopodiaceae
Carophyllaceae
To measure changes in species distributions between the pre and post agriculture periods,
37 pollen types of plants were organized into 6 categories [Table 3.1]. The 37 pollen types
represent common families, genera or species of plant found throughout historical European
34
ecosystems (Hicks 1971, Chorley 1981, Bottema 1992, Mack et al. 2000, Behre 2007, Broström
et al. 2008) . The 6 categories include: tree pollen types, Cerealia, Poaceae, Cyperaceae,
cultivated pollen types, and other herbaceous plants. Cultivated pollen types represent common
historically cultivated species that are also found naturally in European ecosystems (Chorley
1981, Behre 2007). Other herbaceous plants represent pollen types of species that remained uncultivated or relatively un-cultivated but were common and prominent in European ecosystems.
Using R, the data for each pollen type was extracted from each record and placed into the
respective categories. The data were placed into 100 year intervals corresponding to the time step
data in each record. The extracted data from each record were then combined into one dataset.
Changes in species distributions were estimated through calculating the mean percent
change from the initial proportion of each category ranging from 2000 years pre-agricultural
development to 2000 years post-agricultural development.
3.2.8 Biodiversity
The Shannon-Wiener Index was calculated within the records adjusted for agricultural
development and in calendar years (Izsák and Papp 2000). The index data within each record
was placed into 100 year intervals ranging from 10000 years BP to 0 years BP for the calendar
years data. For the data adjusted to agricultural development, the period of pre-agricultural
development was set as 10,000 years leading to agricultural development, and for the period of
post-agricultural development the timeframe was set from agricultural year zero to the value of
the earliest agricultural start date. Following this, the biodiversity data was aggregated,
respective to the two time frames, across all records to calculate the distribution of biodiversity
over time. We chose to calculate the Shannon-Wiener Index as opposed to conducting a
rarefaction analysis because the rarefaction analysis requires that the pollen records being
compared share similar vegetative patterns. Given the scope and scale of this research, it is
unlikely that this assumption would have been met.
35
3.2.9 Index of Dissimilarity
In order to evaluate rates of change associated with agricultural development, the time axes
of the pollen records were shifted so that values represented years relative to the initiation of
agriculture. Thus, landscape stability is calculated as a function of time before or since
agriculture, rather than as a function of chronological year. Trends in landscape stability were
estimated by calculating rates of change using the square chord distance between subsequent
time steps for each pollen record (Bennett and Humphry 1995). A stable ecosystem would be
associated with low rates of change due to the system’s ability to resist change and return to the
original state following a disturbance, effectively reducing dissimilarity over time. In contrast, an
unstable ecosystem would be associated with increasing rates of change as it would demonstrate
a system with low resistance and resilience that is continuously shifting between variable
ecosystem states.
The squared chord distance is a signal-to-noise dissimilarity coefficient; a metric that
measures the degree of dissimilarity between pollen spectra (Overpeck et al. 1985). Compared to
unweighted and weighted dissimilarity coefficients, signal-to-noise coefficients are designed to
maximize the differences between pollen spectra while reducing the impact of random variation
affecting pollen production, dispersion, and deposition (Prentice 1980, Jacobson et al. 1987).
Signal-to-noise coefficients also increase the sensitivity of the analysis to rarer pollen types. The
square chord distance coefficient ranges between 0 and 2, where a larger value represents a
greater degree of dissimilarity (Prentice 1980). The squared-chord distance has been identified as
a preferred dissimilarity coefficient when comparing pollen spectra for its simplistic nature and
for yielding similar results as other signal-to-noise coefficients (Overpeck et al. 1985).
36
Using R version (R v3.3.3), the squared chord distance was calculated as:
𝑑𝑖𝑗 = ∑
𝑘
1
2
(𝑝𝑖𝑘
−
1
2 2
𝑝𝑗𝑘
)
[3.1]
Where k is the proportion of an individual pollen species at timestep (i) and timestep (j)
(Birks et al. 1977, Overpeck et al. 1985, Bennett and Humphry 1995, Gavin et al. 2003). For
each pollen record, and for each time step in the data, the squared chord distance between sample
t[-1] and t[0] was calculated. To accommodate for the variability in interval size between
adjacent time steps, the square chord distance values were converted to a rate of change by
dividing the square chord distance by the interval size between time steps i and j (Bennett and
Humphry 1995).
The associated year for each rate of change value was calculated as the average year
between time steps i and j. Working in rates of change it is unnecessary to resample each pollen
record to a common time step (Bennett and Humphry 1995). Rates of change were calculated for
two time sets: calendar years and the time steps adjusted for agricultural development.
3.2.9.1
Combining Data
The rate of change data in each record were placed into 100 year intervals respective to
both calendar years and the time steps adjusted to agricultural development. For calendar years,
the range was set between 10000 years BP and 0 years BP. For the data adjusted to agricultural
development, the period of pre-agricultural development was set as 10,000 years leading to
agricultural development, and for the period of post-agricultural development the timeframe was
set from agricultural year zero to the value of the earliest agricultural start date. Results were
aggregated by time relative to the onset of agriculture, across all available records, to calculate
the distribution of rates of change (Jacobson et al. 1987, Overpeck et al. 1991).
37
3.3 Results
Following the criteria outlined previously, 180 pollen records spanning 23 different
countries were found to be suitable for analysis [Figure 3.1]. The regime detection analysis
estimated the earliest starting year of agricultural development to be 6836 YBP and the latest at
151 YBP. The mean and median starting years of agricultural development were 1826 YBP and
1500 YBP respectively. With an earliest agricultural start date of 6836 YBP, the timeframe in the
data adjusted for agricultural development was adjusted to 6200 years post-agricultural
development. Intervals after 6200 years post-agricultural development contained only one data
point and were removed from analysis.
Figure 3.1 Locations of pollen records analyzed
3.3.1 Species Distribution
Between 2000 years and 1400 years pre-agricultural development, species distribution
remained stable with all 6 categories fluctuating around 0% change [Figure 3.2]. Commencing at
800 years pre-agricultural development, the relative abundance of tree species began to decline,
38
reaching a percent change of -8.0% by agricultural year zero. Following agricultural
development the relative abundance of tree species began declining more rapidly, falling to 38.5% change after 400 years. Between 400 and 2000 years post-agricultural development, the
proportion of tree species underwent periods of growth and decline, however, throughout this
period percent change remained below -22.9%, reaching a maximum loss of 44% after 2000
years.
39
Figure 3.2 Pollen percent change from 2000 years prior to agricultural development to 2000 years post agricultural
development for (A) Tree Pollen Types (B) Cerealia (C) Cyperaceae (D) Poeceae (E) Other Cultivated Pollen Types
(F) Other Herbaceous Pollen Types
40
At 800 years prior to agricultural development, other herbaceous species underwent a
period of growth reaching +83.4% change at agricultural year zero. The proportion of other herbs
species continued to trend upward for approximately 400 years reaching a growth of +293%.
Following 400 years of agricultural development the proportion of other herb species underwent
periods of growth and decline, however, maintain levels between +175% and +293%.
Cyperaceae, Poaceae, Cerealia and other cultivated species continued to trend around zero
percent change leading into agricultural development. Following agricultural development the
proportion of all four categories began increasing substantially. Cerealia had the most dramatic
increase reaching +3861% after 400 years of development. Following a short period of decline, it
continued to trend upward reaching +4486% change after 1700 years of development. After
1700s years of development the proportion of Cerealia decreased substantially falling to +1240%
change over the next 300 years. Cyperaceae and Poaceae continued to rise, on average, following
agricultural development reaching maximum percent changes of +121% and +202% after 2000
years of agricultural development. Similarly, other cultivated species also rose substantially
following development, however, reaching a maximum of +540% change after 900 years.
Following this point, the proportion of cultivated species declined, fluctuating between +74%
and +140% from 900 years to 2000 years post agricultural development.
3.3.2 Biodiversity
For the data adjusted to agricultural development, median biodiversity levels increased
approaching agricultural year zero, rising by approximately 47% [Figure 3.3a]. Following
agricultural development, levels of biodiversity continued to trend upward, however, after 2900
years of development a new trend formed where biodiversity levels fluctuated. In calendar years
rates of biodiversity steadily increased by 41% from 10000 years BP to present time. [Figure
3.3b].
41
A
A
B
B
Figure 3.3 (A) A time series representing the distribution of the Shannon index values in 100 year
intervals before and after agricultural development across Europe. Negative values represent before
agricultural development. Positive values represent post-agricultural development. (B) A time series
representing the distribution of Shannon index values in 100 year intervals from 10,000 ca. years BP to
present (1950 A.D)
42
3.3.3 Trends in Stability
3.3.1.1
Adjusted for Agricultural Development
Figure 3.4a demonstrates the overall trends in rates of changes during the 10,000 year
period pre-agricultural development and the 6200 year period post-agricultural development.
Between 10,000 years and approximately 1500 years before agricultural development, median
rates of change were low and relatively consistent over the centuries. From 1500 years preagricultural development leading into agricultural development median rates of change began to
trend upward, almost doubling from the average of the previous 8500 years. Following
agricultural development, trends in median rates of change shifted occasionally, however,
generally increased over time. At approximately 5400 years post-agricultural development,
median rates of change began to increase sharply reaching a maximum value of 42 times greater
than the lowest median rate of change over the entire 16,200 year time frame.
43
A
B
B
B
Figure 3.4 (A) A time series representing the distribution of the rates of change values in 100 year
intervals before and after agricultural development across Europe. Negative values represent before
agricultural development. Positive values represent post-agricultural development. (B) A time series
representing the distribution of rates of change values in 100 year intervals from 10,000 ca. years BP
to present (1950 A.D)
44
3.3.1.2
Calendar Years
Between 10,000 YBP and approximately 2500 years BP, trends in median rates of change
were unstable, shifting between variable rates of decline and growth, however, throughout this
period rates of change trended upward overall [Figure 3.4b]. Following 2500 years BP, trends in
rates of change stabilized, marked by a consistent and accelerated upward trend leading to the
present.
3.4 Discussion
In calendar years, rates of change steadily rose from 10,000 years BP to present
indicating that stability likely decreased over time. From this perspective it is difficult to
distinguish the mechanisms behind the decline in stability, however, once the timeframe is
adjusted for agricultural development, it becomes clear agricultural activities are a large
contributor to landscape scale instability. Prior to agricultural development, rates of change
remained consistently and relatively low; once agriculture started to develop rates of change rose
substantially, maintaining high levels of instability over time. Given the scope of this research it
is difficult to isolate the specific mechanisms causing the instability, however, it is likely due to
shifting landscape dynamics resulting from social factors, and changing ecological factors, such
as reduced functional diversity, resulting from changes in the species composition of the
landscape.
3.4.1 Landscape Stability in the Past 10,000 years
From 10,000 ca. years BP to present, rates of change continued to rise, however, from
this perspective it can be difficult to distinguish between possible factors that are driving
instability. While the earliest agricultural date is at 6836 ca. years BP, the distribution of
agricultural starts dates lean towards the present with a median start date of 1500 ca. years BP
suggesting a lack of agricultural activities earlier in the records. Given this, it is possible that the
rates of change early in the records may be more influenced by the highly variable climate of the
Holocene as opposed to agricultural activities (Tinner and Lotter 2001, Cubizolle et al. 2003). As
45
time progresses and records begin to show evidence of agricultural development it becomes
likely the trend in rates of change become a result of agricultural activities on the landscape.
Once compared to the trends in rates of change with time adjusted for agricultural development,
it becomes evident that agricultural activities are a prominent destabilizing force across European
landscapes.
3.4.2 Impacts of Agricultural Development on Landscape Ecosystem Stability
Given the trends in rates of change pre- and post-agricultural development, it is evident that
the presence of agricultural development substantially reduced the stability of the landscape.
Prior to agricultural development, rates of change across the pollen records remained
consistently low implying that landscapes ecosystems were relatively stable and slow changing.
Approximately 1500 years prior to agricultural development landscape stability began decreasing
while rates of change began increasing. Following agricultural development, rates of change
continued to rise demonstrating that across landscapes the presence of agricultural development
is associated with increasingly reduced stability in those landscapes.
Prior to agricultural development, natural ecosystems were not immune to human activities.
This research indicates that rates of change began rising approximately 1500 years prior to
agricultural development, possibly caused by the activities of hunter-gathering societies. Hunting
gathering societies were known to modify ecosystems through deforestation, predator removal,
ground preparation, opportunistic planting, and burnings (Zvelebil and Dolukhanov 1991).
Large-scale anthropogenic landscape modification in Europe may have occurred as early as the
last glacial maximum (21000 years BP) where paleolithic societies used burning to facilitate
foraging, hunting, and travel (Kaplan et al. 2016). In Britain, palynological evidence suggests
that pre-agricultural societies may have cleared forests in order to facilitate hunting, and for the
cultivation of wild nuts and grasses (Dennell 1985, Brown 1997). Additionally, archeological
evidence in Britain suggests that the construction of large wooden structures began in the midlate Neolithic period and would have required large amounts of timber (Brown 1997). Along the
agricultural frontier in Europe, hunter gathering societies may have adopted farming techniques,
46
however, their land was most likely colonized by members of agrarian societies (Zvelebil and
Dolukhanov 1991). Over time, regions previously inhabited by hunter-gatherers became
dominated by agrarian societies as agriculture became the prominent source of food production
in Europe. In this scenario, the presence of hunter-gather societies may act as a precursor to
agricultural development, demonstrating increased rates of change prior to agricultural
development due to their activities.
Agriculture may have also occurred in the records prior to when the regime detection tool
first determined the initial year of agricultural development. An examination of 30 randomly
selected records found that Cerealia pollen was present episodically, and in small amounts on
average 300 years before the regime detection algorithm determined a significant and sustained
increase, suggesting that agricultural may have been present sporadically or locally prior to its
assignment in this analysis. These findings suggest that either population growth or agricultural
adaptation were relatively slow, but were nevertheless substantial enough to influence landscape
stability centuries before it was pervasive on the landscape.
3.4.3 Dynamic Land Use Patterns vs. Ecosystem Structure and Composition
Evidence suggests that following agricultural development landscape level stability was
reduced substantially. This instability likely stems from two mechanisms: direct and indirect
impacts of agricultural development. Direct impacts are those where the composition of a
landscape is directly altered through dynamic land use patterns (e.g. agricultural expansion or
abandonment). In this context, direct impacts are related more to the social aspects of landscape
stability, where changes in the socio-economic and institutional strength of a region can result in
a decreased capacity to mitigate perturbations or increase the capacity for agricultural activities
to occur (Fraser and Stringer 2009). Indirect impacts represent changes in the ecological
structure and function resulting from direct impacts (e.g. changes in local species composition).
Ecological stability is highly dependent on various forms of biodiversity, including species and
functional diversity (McCann 2000, Diaz and Cabido 2001). These results indicate that landscape
species composition changed substantially following agricultural development; these changes in
47
the ecological structure, and possible functional diversity, of the landscape may contribute to
instability. Either of these two mechanisms could result in increased rates of change following
agricultural development. While the two are not mutually exclusive it is important to
differentiate how each process can represent the landscape instability established with the
records.
3.4.3.1
Dynamic Land Use Patterns
Historically, agricultural land use patterns throughout Europe have been highly dynamic
and unstable, fluctuating between periods of intensification and abandonment. Agricultural
intensification and abandonment can facilitate substantial change across a landscape.
Agricultural intensification is defined as higher levels of inputs and increased output of
cultivated products per unit area and time (Lambin et al. 2001). We also consider the expansion
of agricultural land as a form of intensification. Throughout European agricultural history, a
primary driver of agricultural intensification would have been population growth (Kaplan et al.
2009). The development of agricultural practices significantly increased local food output
allowing population densities to exceed those capable by hunting and gathering (Hillman 1973,
Clark 1991). As populations continued to rise agricultural output would as well, to accommodate
the growing population. European cultures would have followed two primary processes to
increase agriculture output: cultivating more land and developing new technologies and
techniques (Kaplan et al. 2009). Entering the Classical Era (~2600 YBP) European populations
began to rise more rapidly requiring increased agricultural output. The Greek and Roman
cultures responded through industrializing agriculture by using new techniques such as crop
rotations, fallowing fields, and fertilization (White 2014). Additionally, throughout their
territory, which at its height covered a substantial portion of Western and Southern Europe, the
Romans further industrialized agriculture through increasing the size of their farms from small
acreage farms to large scale estates covering hundreds of acres (Wolf 1987). Further expanding
cultivated lands would continue to alter landscapes due to the large degree of land clearance and
deforestation necessary for expansion (Kaplan et al. 2016). This trend would continue throughout
European history; during periods of population growth, agricultural land use would expand in
response and rates of deforestation would rise. The decline of tree species following agricultural
48
development suggest agricultural expansion likely occurred. Additionally, while Cerealia
increased dramatically after agricultural development the proportion of other crops species also
increased, though to a lesser degree, suggesting further intensification as European societies
expanded the variety of cultivated crops.
Opposite to intensification, agricultural abandonment is a process where cultivated land is
abandoned due to the discovery of more fertile land, the invention of new technologies, or a
reduction in population due to war, plagues, or climate (Hart 1968, Kaplan et al. 2009). During a
period of abandonment, cultivated land is either converted into another anthropogenic land use
or, if completely abandoned, the surrounding natural systems succeed onto the abandoned land
(Hart 1968). Due to the chemical, physical, and biological changes imposed on agricultural soils,
succeeding systems are often different compared to their neighbouring ecosystems (Foster et al.
2003, Flinn and Vellend 2005). Research across Europe and the United States indicates that
changes in species diversity and plant performance between post-agricultural ecosystems is
inconsistent (Flinn and Vellend 2005). For example, forests in France that succeeded over
abandoned Roman farming communities were found to have significantly higher levels of
species diversity closer to the centre of the abandoned communities (Dambrine et al. 2007).
Agricultural abandonment, given time, would eventually alter the structure of the landscape and
during this process would undergo increased rates of change. Periods of agricultural
abandonment may represent inadequate social resiliency of a landscape. In preindustrial
societies, national and international institutions were not capable of easing tensions surrounding
food shortages, resulting in war, population decline, and subsequently, agricultural abandonment
(Zhang et al. 2007).
Throughout European agricultural history there have been several periods where large
scale agricultural abandonment may have occurred. Following the collapse of the Roman Empire
was a period known as the migration period. Occurring between 400 CE (1550 years BP) and
800 CE (1150 years BP) the migration period was characterized by large scale barbarian
invasions, political turmoil, plagues, and a slight shift to a colder, drier climate (Wanner et al.
2008, Büntgen et al. 2011). These factors combined to form a period of population stagnation
49
allowing for a short period of stable forest cover and afforestation (Kaplan et al. 2009). Another
period of agricultural abandonment occurred following the Black Death in 1347 CE (603 years
BP). Estimates predict that 30% of the European population died from the plague and that
between a fifth and a quarter of all settlements were abandoned (Williams 2000, Rabbinge and
van Diepen 2000). Furthermore, a climatic period between 1350 CE (600 years BP) and 1850 CE
(100 years BP) known as the Little Ice Age reduced global temperatures resulting in reduced
agricultural yields and subsequently widespread famines throughout Central Europe (Büntgen et
al. 2011). The ensuing losses in population from the plague and famine resulted in the
abandonment of large tracts of agricultural land, leading to afforestation across Europe. Given
this history it is possible agricultural abandonment contributed to the instability revealed in the
pollen records.
3.4.3.2
Changes in Ecosystem Structure
In contrast to changing land use patterns, instability in the pollen records may also be the
result of purely ecological factors including changes in biodiversity and in species composition.
It is evident from the pollen records that biodiversity levels in Europe have generally
trended upward throughout the Holocene. A similar trend was seen in the 10000 years preceding
agricultural development. Given that the majority of agricultural start dates fall within the
subatlantic period of the Holocene it’s possible that the trend in biodiversity preceding
agriculture would follow similarly to the trend in biodiversity over the past 10000 years. These
trends may be due to shifts in climate following the last glacial period (~12000 years BP)
(Cubizolle et al. 2003, Wanner et al. 2008).
Approximately 8200 years before present time, a short climatic period that was cool and
dry triggered a shift in the structure of European ecosystems (Tinner and Lotter 2001). The
populations of Corylus, the dominant species at the time, collapsed while species such as Tilia,
Picea, Betula, Fraxinus, Ulmus, and Alnus proliferated, including rarer species such as Abies and
Fagus. Over the next few millennia European ecosystems would continue to shift slowly until
they reached the Fagus dominated ecosystems of modern time. Entering into the Atlantic period
50
(8000 to 5000 years BP) of the Holocene the European climate became warmer and wetter
(Cubizolle et al. 2003). The climatic conditions of the Atlantic period lead to increased formation
of mires across France. Mires are known to contribute substantially to local biodiversity levels.
It is also possible that human activates on the landscape result in greater levels of
landscape biodiversity. In Europe, human activities have been significant on landscapes for at
least 6000 years (Williams 2000). These activities including burnings, clearings, harvesting,
predator removal and agriculture would serve to facilitate hunting, travel and, harvesting and
cultivation (Kaplan et al. 2016). In an ecological context, early landscape modification would
serve to increase the heterogeneity of landscape ecosystems. Increasing landscape heterogeneity
would likely increase landscape biodiversity levels due to the propagation of the different species
associated with the different land types. Human landscape activities would likely intensify
approaching agricultural development. As such, the intensification of human activities on
European landscapes may contribute to the upward trend of biodiversity levels seen in the 10,000
years preceding agricultural development.
Biodiversity is intrinsically linked to ecosystem stability; providing the necessary
structure for various stabilizing mechanisms to occur (McCann 2000). Species diversity and
functional diversity are both considered to have an integral role in maintaining stability (Tilman
and Downing 1994, Hooper et al. 2005). Functional diversity and, to a lesser degree, species
diversity, play into a mechanism referred to as the portfolio effect where increasing diversity
increases the probability of the system containing populations with differential responses to a
disturbance (Tilman et al. 1998). Variability in a community’s response to a disturbance
produces an averaging effect over time resulting in stable community dynamics (McCann 2000).
The pollen records indicate that a high level of biodiversity was maintained following
agricultural development, however, the Shannon Index is indicative of species diversity, not
functional diversity (Izsák and Papp 2000). While species diversity can be a suitable surrogate
for functional diversity the relationship is complex and at times inconsistent where changes in
species diversity may not result in a proportionate change in functional diversity (Diaz and
51
Cabido 2001). Given this, it is possible that while species diversity was maintained functional
diversity may have been lost following agricultural development. If so, this loss may have
contributed to the unstable landscape dynamics revealed in the pollen records following
agricultural development.
In Europe, changes in the species composition of life history traits may be a source of lost
functional diversity and the subsequent loss of stability following agricultural development. Prior
to agricultural development, slow growing tree species dominated European landscapes. After
agricultural development there was a substantial decline in the proportion of tree species while
grasses, sedges and other herbaceous species flourished [Figure 3.2]. Research indicates that
slow growing species are typically more resistant and less resilient compared to fast growing
species (Lepš et al. 1982, MacGillivray and Grime 1995). Additionally, high growth rates are
also associated with less stable population dynamics (May 1973b, Gellner and McCann 2016).
Consequently, at the landscape scale, one would expect that increasing the proportion of fast
growing species increases landscape resilience but reduces landscape resistance. By nature, a
loss in resistance would make a landscape more susceptible to greater change following a
disturbance or stressor compared to a more resistant system under the same conditions. Given
that fast growing species thrived in the presence of agricultural development while slow growing
species declined there was likely an associated loss in landscape resistance. Consequently, this
would result in greater rates of change in the pollen records as the landscape would respond more
strongly to disturbances and stressors such as changing climatic conditions or intensified human
landscape modification.
3.5
Conclusion
While this research suggests that agricultural development resulted in a substantial
decline of landscape stability, specific mechanisms remain unclear. In particular, two
mechanisms likely have a role in landscape instability: direct and indirect impacts of agricultural
development. The act of agricultural expansion or abandonment alters landscape composition by
directly changing the composition of the landscape. Indirectly, agricultural development also
changes the ecological structure (i.e species composition) of the landscape which may also result
52
in instability. Due to the large spatial nature of pollen records it is difficult to distinguish
between instability resulting from either direct or indirect impacts of agricultural development.
As a result the relative magnitude of each contribution is left unknown. We suggest future
research conduct case studies on regions where detailed land use records are available. Land
records can help identify periods of dynamic and static agricultural land use so that the
mechanisms behind landscape instability can be more clearly defined.
In a broader context this research relays the importance in developing techniques to
monitor and manage landscapes that are becoming increasingly destabilized under agricultural
activities. This becomes especially pertinent given agricultural activities are expected to intensify
over the next 50 years (FAOSTAT 2012). Although this research demonstrates biodiversity
levels remained high following agricultural development, agricultural is considered a
homogenizing force in ecosystems (Tilman 1999b). As agricultural lands continue to expand and
develop, more natural ecosystems will be replaced with cultivated fields and pastures resulting in
landscapes that are composed entirely of simplified agricultural systems, typically monocultures.
Monoculture systems are typically more vulnerable to disease, pests, and invasion, all of which
can further destabilize the system (Tilman et al. 1997, Tilman 1999b). Additionally, the overuse
of agricultural regions can result in increased rates of desertification, particularly in semi-arid or
arid regions, due to soil erosion, soil and water salinization and biodiversity loss (Portnov and
Safriel 2004).
Agricultural activities are likely not the only destabilizing force on landscape systems.
Throughout the Holocene, changes in the European climate resulted in shifting ecosystem
structures (Tinner and Lotter 2001, Cubizolle et al. 2003). In modern times, we are currently in a
period of abrupt climate change that has had profound ecological implications around the globe
(Walther et al. 2002). These implications include: changes in the physiology and phenology of
organisms, changes in the species distributions, changes in the community interactions and
altered ecosystem processes. Climate change is also expected to exacerbate the rates of
desertification caused by human activities on landscapes (Schlesinger et al. 1990). In an era of
globalization the rate and magnitude of species invasions have accelerated around the world
53
(Hulme 2009). Invasive species, if left unchecked, can substantially reduce ecosystem stability
through increasing predation and competition, or by altering habitat and energy budgets (Mack et
al. 2000). The implications of agriculture, climate change, and globalization on the world’s
remaining natural ecosystems further demonstrate the necessity for future research to develop
methods to monitor, manage, and conserve the goods and services these systems provide. One
solution is to study agroecosystems found in many developing nations (Altieri 2004). Many
indigenous populations have developed farming strategies that combine agriculture and
ecological systems resulting in sustainable food systems that also protect agrobiodiversity.
Incorporating the ecological theory behind agroecosystems into industrialized agricultural
techniques may aid in the developing landscapes that are more resilient, resistant, and diverse.
54
Chapter 4: Concluding Chapter
4.1 Overview of Findings
The primary objective of this research was to measure the possible effects of agricultural
development on landscape stability. To accomplish this, 180 pollen records from 23 European
countries were analyzed using an index of dissimilarity, the square chord distances, to measure
rates of change (as a proxy for stability) of landscape-scale vegetation before and after
agricultural development. To determine the initial year of agricultural development, a regime
detection tool was applied to the proportion of Cerealia pollen in each record. The analysis
indicated that median rates of change began rising 1500 years prior to agricultural development
and continued to rise substantially following agricultural development. These results suggest that
agricultural development reduced the overall stability of the landscape.
The secondary objective was to suggest possible mechanisms explaining changes in
stability following agricultural development. Through a review of the literature two possible
mechanisms were identified: dynamic land use patterns and changes in ecological functioning.
Land use patterns throughout European agricultural history were highly dynamic with
agricultural development undergoing periods of intensification and abandonment. Occurring
during periods of population growth agricultural intensification typically involves high levels of
land conversion from natural systems to cultivated fields and pastures. In contrast, agricultural
abandonment generally occurs during periods of population decline or stagnation due to disease,
war, or famine. During abandonment, succession occurs and previously cultivated fields become
overgrown with new vegetation types. Both intensification and abandonment can result in
substantial changes to the structure and composition of landscapes, and thus these changes likely
contribute to the lost stability following agricultural development.
Changes in ecosystem structure may have also contributed to the loss in stability
following agricultural development. In ecological theory, species diversity and functional
55
diversity have integral roles in maintaining stability; providing the structure for stabilizing
mechanisms to occur. Given this relationship, biodiversity levels in the pollen records were
calculated using the Shannon-Wiener Index. Additionally, a species composition analysis was
conducted for a 4000 year period (+/- 2000 years from agricultural year zero). A total of 32
species, genera, and families were placed into 6 categories: trees species, Poaceae, Cyperaceae,
Cerealia, other cultivated species and other herbaceous species. The Shannon Index found that
biodiversity levels increased by 47% over the 10000 years leading into agricultural development
and that these high levels were maintained following development. Although the diversity
indices indicated high levels of biodiversity following agriculture both the diversity indices
represent aspects of species diversity and compared to functional diversity, species diversity is
often considered less important in maintaining stability. As such, while species diversity
increased functional diversity may have been lost during development. One possible source of
lost functional diversity is changes in the species composition of the landscape. The results of the
species composition analysis found that the proportion of Poaceae, Cyperaceae, Cerealia, other
crops species, and other herbaceous species increased following agricultural development while
the proportion of tree species declined by 43% after 2000 years of agriculture. In terms of
functional diversity, this represents a substantial change from a landscape dominated by slowgrowing species to one dominated by fast-growing species. Fast-growing species are less
resistant yet often more resilient compared to slow-growing species and thus would be more
susceptible to change following a disturbance. As a result, the changes in species composition
from slow-growing species to fast-growing species likely contributed to the loss in stability
identified following agricultural development.
4.2 Limitations and Recommendations for Future Research
While pollen records are indicative of the landscape as a whole their large spatial and
temporal nature make it difficult to distinguish between changes in the individual ecosystems
that compose the landscape, including the degree of human landscape modification. As such,
when using rates of change as a proxy for landscape stability it becomes difficult to discriminate
between change caused directly by human land use changes (i.e agricultural expansion or
abandonment) and those caused indirectly (changes in ecosystem functioning, biodiversity).
56
While both likely contribute to instability the relative magnitude of that contribution is unknown.
One solution is to conduct case studies using detailed land use records, if available, for
individual pollen records to determine periods of changing land use and periods of static land
use. Once these periods are identified it should be possible to estimate the magnitude of change
attributed to changing land use and to ecological factors. Additionally, case studies allow for a
more in-depth analysis of the functional traits composing the landscape, furthering our
understanding of the role that functional diversity plays in landscape scale stability. If land use
records are unavailable ecological models can used to simulate landscapes with varying degrees
of agricultural land use. Providing detailed outputs, models should accurately predict the
magnitude of both the direct and indirect impacts of agricultural development (Smith et al. 2001,
2014, Prentice et al. 2004).
Global croplands are expected to rise by 10 million km2 in the next 50 years and if society
wishes to conserve and protect the ecosystem functioning of our landscapes it is imperative for
management strategies to incorporate aspects of landscape ecology into governance (Tilman et
al. 2001, Opdam et al. 2002). This research acts as a foundation for future work to continue
developing the body of knowledge surrounding agricultural development and its impacts on
landscape structure, function, and stability. Once this body of knowledge is further developed
and incorporated into landscape ecology it can be used to guide decision makers when
establishing strategies around agricultural development and resulting landscape structure.
57
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Appendix A: List of Pollen References
Table A.1 Site names and citations for pollen records extracted from the European Pollen Database
Site
ADAJA
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