This is the essay I wrote for the Honours mid-year Theory Seminars, it's quite long and complex but I really enjoyed writing it and maybe it will actually be read here.
Introduction
The biggest questions facing humanity, and especially ecologists, in the
21st century involve our relationship with our surroundings.
Advances in technology and industry, as well as an exponentially expanding
population, result in an immense pressure that is placed on the ecosystem. For
this reason special attention is required to understand the systems that we
rely on and which are most affected by our actions. Landscape ecology is one
field which offers a host of tools to study ecosystems with a sociological
component. Landscapes exist as a manifestation of ecosystems on a scale that is
specifically and explicitly important to humans. The scale at which humans
interact with the environment is critically relevant in this field of ecology.
A key problem facing ecologists is
the loss of biodiversity and the potential implications for services on which
humans depend. Loss of services includes, among others, decreased water quality
and disrupted pollination systems or nutrient cycles. An understanding of the
fundamental processes which govern these systems seems imperative to ensure a healthy
socio-ecological future. However, the ecosystems which govern these
relationships form extremely complex systems in which interactions at
macro-scales influence processes, trophic-structures, productivity and nutrient
fluxes among components and various scales. The interactions of the components
of the system accordingly feed back into the macro-scale resulting in a hyper
complex system, the understanding of which is vital in resolving the issues
facing mankind today (Levin 1998).
Our current understanding of the systems that govern ecology encompasses
various facets from community compositions to molecular ecology and from the
study of a single plant to the biogeography of biomes. Major jumps in our
understanding of the processes entailed in ecology are rare, and in this sense
the emergence of a new field is particularly noteworthy. One field specifically
studying the relationships between patterns and processes is the relatively
young field of landscape ecology. The development of this field has been aided
by increases in landscape use and change over the last century (Risser and
Iverson 2013) and the field has undergone an interesting evolution from its
founding days as a subject struggling to combine small scale biotic studies
with large scale land use changes to one which is multidisciplined and actively
incorporates a range of scales. The field has grown from one with little
definition into one of the most mathematically intriguing and complex fields in
ecology today. The search for multiscale patterns has extracted landscape
ecology from a simple descriptive field into one that may blur the lines
between mathematics, physics, chemistry and ecology. In addition landscape
ecology has approached a fascinating point in which the human imposed definitions
are likely to be redefined through the application of complex systems theory. The
complex practices entailed in landscape ecology today may be one of our
greatest defences in a rapidly changing and unpredictable world.
The emergence of Landscape Ecology
For
decades, if not centuries, the need to understand our surroundings has both
captivated and haunted biologists and geographers. Those most afflicted with
the desire, dedicated their lives to the understanding of landscapes, and this
work was encouraged by the historic need for planning in increasingly
human-dominated landscapes. Before biologist and botanist Carl Troll coined the
term Landscape Ecology in 1939, the bulk of work on this topic was constrained
to geography, botany and ecology in a land-use framework (Risser et al 1984). At this time there was no canonical
definition of a landscape and the pioneers of landscape ecology had varying
ways in which they approached the topic but it was essentially viewed as
landscape geography. The foundation of the subject has firm rooting in Europe;
however the field had little focus as evident by the many definitions of the
subject and the approaches used. For example Troll defined landscapes as an
objective “organic entity” (Troll 1950), while his contemporary Ernst Neef,
defined landscapes in terms of the uniformity and specificity of their land-use
(Neef 1967). These two definitions are inherently different: Troll’s view,
contrary to Neef’s, leads to the understanding that a landscape is not a mental
construct which can be redefined in an anthropocentric relativistic way. The
view of a landscape as separate from the definitions imposed by human
understanding is paramount to the development of landscape ecology.
The simultaneous shift in ecological studies from individual organisms,
populations and community structure to ecosystem analysis, from the 1940s to
the 1960s, aided the development of the field of landscape ecology (Risser and
Iverson 2013). In the 1970s “landscape-level” studies attained a “level” of
prominence, especially in Europe where the field was recognized as landscape
geography (Risser et al 1984). In
addition theoretical and technical advancements in ecology such as island
biogeographic theory, patch dynamics and spatial simulation modelling, were
steadily growing and raising interest in the spatially heterogeneous and
complex processes governing patterns. Despite this interest, by the beginning
of the 1980s there was still no generally accepted framework which could
incorporate the diverse ideas of multiple landscape patterns and the movement
of energy and organisms across spatially heterogeneous landscapes. Developments
in the use of mathematical models, especially in population biology (Levin
1968), began to play a role in landscape ecology and allowed for growth in ecosystem
science. Very little attention, however,
was paid to spatial heterogeneity, and very few models incorporated a spatial
component (Risser and Iverson 2013).
In the early 1970s the United States of America’s National Science
Foundation (NSF) ran the International Biome Program (IBP) which conducted
studies on managed and unmanaged geographic ecosystem types; it involved
measurements within ecosystem types as well as ecosystem-level mathematical
models. The studies began to recognize the importance of heterogeneity in the
interactions between sub-units. High levels of heterogeneity complicated
attempts to select and interpret the roles of driving variables. The results of
the IBP led to the NSF’s Long-Term Ecological Research project (running from
the late 1970s and continuing today) which aimed to include landscape
heterogeneity in its design and in its measurement of biodiversity,
productivity and abiotic cycles (Risser and Iverson 2013). In light of these
programmes and the undeniable importance of heterogeneity in landscape
research, the NSF granted funding to the Illinois Natural History Survey –
whose goal had been to develop a large Geographic Information System for
Illinois – to hold a workshop which would strive to outline and contextualise
the directions and approaches of “landscape ecology” as a developing field.
Little did the attendees of the workshop realise that the discussions of those
three days in rainy Allerton Park, Illinois, would define a new paradigm in
ecology, especially in the United States (Risser and Iverson 2013). The
workshop was led by Paul Risser, Richard Forman and James Karr with 25
participating ecologists, including one representative each from Canada and
France, and the remainder from the United States (Risser and Iverson 2013). All
of the attendees were experts in their field and none realised that they were
outlining an essentially new multifaceted field in ecology (Risser 1984,Wu
2013).
There were two essential outcomes of the Allerton Park workshop, as
outlined in Risser and Iverson’s 30 year anniversary review (Risser and Iverson
2013). The first served to define landscape ecology and the second solidified
the importance of the field in the understanding of ecological processes with
respect to heterogeneity. Risser (1984) reported on the workshop and describes
the outline of what was to become the North American vision of landscape
ecology:
“Landscape ecology is not a distinct discipline or simply a branch of
ecology, but rather is the synthetic intersection of many related disciplines…, viewing landscape ecology as a branch of ecology, would…tend to exclude the formal analysis of human cultural processes that
form landscapes…. Understanding landscapes requires that
we deal with human impacts contributing to the landscape phenomenon, without
attempting to draw the traditional distinction between basic and applied
ecological science or ignoring the social sciences”
In the last
three decades of landscape ecology key questions, the basis of which were
raised at the Allerton Park workshop, have been expanded upon and defined. A
review of landscape ecology on the 30th anniversary of the workshop
by Wu (2013) outlines some of the defining questions and topics of landscape
ecology, as demarcated then and as facing the science today. The report
released after the workshop (Risser et al
1984) defined the questions as follows: (1) “How are fluxes of organisms,
materials and energy related to landscape heterogeneity?” (2) “What past and
present processes result in the patterns seen in landscapes?” (3) “How is the
spread of disturbance affected by landscape heterogeneity?” (4) “How can an
understanding of landscape ecology improve resource management practices?”
These four basic questions are no less valid today than they were 30 years ago,
although the science has come a long way in answering parts of them (Wu 2013).
Wu (2013) outlines an updated version of these questions. As the science has
grown, so have the sciences on which landscape ecology can draw, especially
those lending to analytical processing, computing capacity and mathematics of
complex systems, allowing an expansion of the questions that landscape ecology
can ask, and concurrently answer. In 2001 the US Association of Landscape
Ecology held their 16th symposium at which the “Top 10 List for
Landscape Ecology in the 21st Century” was produced through
contributions from 15 leading landscape ecologists. Wu (2013) elaborates on
these 10 key research topics, as follows: (1) ecological flows in heterogeneous
landscapes, (2) processes, causes and consequences of land-use and land cover
change, (3) nonlinear dynamics and landscape complexity, (4) scaling, (5)
methodological development, (6) landscape metrics and related processes, (7)
integrating anthropogenic effects into landscape ecology, (8) optimization of
landscape pattern, (9) landscape sustainability, and (10) data acquisition and
accuracy assessment. The four original questions outlined at Allerton Park are
still prevalent in ecology today, allowing for some modernization while the
introduced questions show the range of interest that has developed in the
field.
Of the 10 key
topics that outline the direction of landscape ecology, the concept of
nonlinear dynamics and complexity has led to a remarkable coalition of the
fields of ecology and mathematics. The need to identify patterns, already in
themselves complex, at various scales of space, time and organizational
complexity, is a cornerstone of landscape ecology. Due to this feature of the
science the adoption of the concept of complex adaptive systems has benefited
the field of landscape ecology. Before the idea of complex adaptive systems was
introduced to landscape ecology by figure heads such as Milne (1998) and Levin
(1998), the main approach to understanding the complexity of landscapes was the
paradigm of hierarchies (Urban et al
1987). By the late 1980s the idea of a landscape as a mosaic of patches
generated by processes operating at various scales was widely accepted (Urban et al 1987). The formation of these
patterns had been in consideration for some years, with Levin’s (1978) work on
planktonic communities as an ideal study for patterns functioning over patches.
Based on this research, patterns were thought to be results of disturbances,
biotic processes and environmental constraints with each of these processes
acting at different scales, both within and across processes (Levin 1978). This
level of complexity in landscape ecology has been approached from two main
perspectives in the last 30 years; that of a set of nested levels of processes
and interactions (the “hierarchy approach”), and that of studying each level
and the translation from one level to the next (the “complex adaptive systems
approach”).Both ideas, which are indeed linked, will be discussed in the
following section of this essay.
Tackling Complexity
The inherent
complexity of landscapes necessitates a multiscale or hierarchical approach in
its analysis. Urban et al (1987)
applies a definition to landscapes which has become somewhat traditional,
specifically that landscapes are seen as a mixture of patches of various size,
of various origins, in various stages of regeneration, approaching
microenvironmental equilibria at various rates. Hierarchy theory views the complexity
of this landscape as patterns organized in a special way, with processes and
patches occurring at characteristic scales. In particular these scales are
positively correlated in space and time (Figure 1). In general, low-level
events occur on a smaller scale with more frequency than do large-scale events.
The characterisation of these scales allows for the complexity to be viewed as
containing a degree of organization which can be broken down into levels. Hierarchy
theory provides guidelines for defining the levels of a system.
Figure 1. The scaling of
vegetation patterns across time and space (Urban et al 1987)
The levels of
the system are nested and when viewed as individual parts, can contribute to an
understanding of the complexities of the landscape. Hierarchy theory thus
allows for explanations of landscape ‘behaviour’ as the interaction among the
parts, and of patterns that translate across the hierarchical scales.
Importantly the levels of a system are related to each other and are functionally
defined. This is a mentally constructed way of viewing a landscape, the primary
benefit of which is the ability to resolve the complexity of dynamics and
spatial patterns at a range of scales into a few variables and a set of
constraints each relative to the reference level.
Hierarchy
theory did not evolve as a consequence of landscape ecology, rather it is a concept
already viewed as classical by Urban et
al (1987), which was merely applied as a paradigm for understanding the
complexity of landscapes. The formal view of hierarchy theory is commonly
thought to have been introduced to landscape ecology by Allen and Starr (1982);
however Urban et al (1987) recognise
the early work by Watt (1947) and Whittaker (1953) in defining the concept.
Hierarchy theory as formally applied
defines a three-tiered nested system in which the levels are relatively
isolated and each level operates at distinct scales; however the boundaries of
the levels may be difficult to define. Concepts of the theory usually apply to
scale-related principles while many of the real world applications involve
organizations defined by human-centric concepts such as populations or biomes, which
may be inappropriate for the description of underlying processes (Hay 2002).
Despite this downfall in hierarchy theory, the idea of levels in a landscape is
exceedingly useful and likewise plays a part in complex theory.
The arrival
of Remote Sensing as a tool to capture the patterns of landscapes allowed for a
new framework to be adopted by landscape ecologist. The plethora of information
available, in addition to the increase in computational power, made the processing
of characteristics of systems with a large number of components interacting in
non-linear fashions and exhibiting adaptive properties possible. This new framework builds on Complex Systems
theory and mathematics from the 1960s and 1970s and has been used in economics,
computer science and more recently, ecology (Hay 2002). Landscapes when viewed
from a complex systems approach have an updated definition from the one
outlined by Urban et al (1987).In
this light, landscapes can be seen as “open systems that extract high quality
energy from the sun, and respond with the spontaneous emergence of organized
behaviour so that their structure and function are maintained” (Kay and
Schneider 1995). An important characteristic of complex systems is that they
lend themselves to a nested hierarchical structure, and complexity theory views
these hierarchies in a slightly different way to that of conventional hierarchy
theory. A fundamental characteristic of Complex Systems is the appearance of
levels and of self-organization.
Complexity as
a science is difficult to define as it entails a vast assembly of contrasting
approaches. A few of the many concepts and approaches include cellular
automata, interacting particle systems, self-organization, non-linear dynamic
systems, fractals, and artificial life (Milne 1998). Complexity in itself can
be approached in two distinct ways, one way embraces the complexity as an
inevitability and studies it by breaking it down into subsets defined and
controlled by a small number of processes. This first view correlates well with
the hierarchies paradigm, however if the complexity of the living system
emerges from a large number of random associations and interacting factors then
the system is appropriately complex to be studied through the framework of
complexity theory, the second way in which to view a complex system. Complexity
theory draws on ideas, concepts and theories from Catastrophe theory, Chaos
theory, Hierarchy theory, Non-Equilibrium Thermodynamics and Self-Organization
theory (Hay 2002). According to complexity theory the levels that emerge are a
result of the systems self-organization.
By applying a
complex systems approach to ecology, the natural levels which emerge in the
complex system can be investigated through the analysis of a multitude of
variables in multifarious models in which the levels and hierarchies are made
obvious by scale thresholds separating
scale domains represented by sharp
transitions in patterns (Meentemeyer 1989). The advances in complexity theory
provide opportunities for landscape ecology as the study of a non-linear system
which includes feedbacks leading to a self-organized behaviour over a wide
range of spatial and temporal scales (Levin 1992). In addition a complex
systems approach enables a synthesis of the understandings developed for each
level in a system and translating that theory from one layer to another (Milne
1998). One aspect that may help to cement the application of complexity theory
to landscape ecology can be seen in the study of scaling relations. Scale, as
mentioned, is one of the key aspects of landscape ecology, the search for
invariant scaling relations and the possible processes that generate them makes
up a large portion of the work done in the field (Wiens and Milne 1989). Scaling
relations can be used to delineate the domain of a hierarchy level (Milne
1998).
The discovery of scaling relationships
that exist in this way would indicate that the system is controlled by first
principle rules that occur across a range of scales (Meakin 1993).
Traditionally biological sciences operate in a framework that emphasizes
analysis of variance in a somewhat descriptive sense. Descriptions tend to be
based on interest levels which exist in the frame of the viewer, as such
processes and patterns tend to be anthropocentrically defined. The search for
invariant scaling relations is in contrast with these ideas, complexity theory
searches for invariant system properties which may transcend the boundaries of
the human conceptual frameworks. The current state of the application of complex
theory is working to objectively reveal domains of length and time scales over
which processes operate (Wiens and Milne 1989).
Complex
Adaptive Systems
The ecosystem
is a complex system which presents itself in a different way to other complex
systems as it involves adaptability and sustained diversity. These two points
define the biosphere as a special type of complex system known as a complex
adaptive system (CAS). Descriptions of other CAS’s can be found in Arthur et al (1997) especially as applied to
any economy. Examples of CAS’s are found in everything from cells to
meta-communities and the idea was applied to ecosystems as recently as 15 years
ago. Levin (1998) championed the usefulness of a CAS approach to ecosystems, in
particular as a tool to investigate the relationship between the organization
of biodiversity and the functioning of systems.
Not all
complex, self-organising systems are adaptive and Levin (2003) outlines the
point that in order to be considered adaptive some form of selection must take
place, and in these situations it is crucial to distinguish at what levels
selection is taking place. This task is non-trivial, and may be impossible as
selection may be occurring on multiple interacting scales. In general Levin
(2003) defines CAS’s according to three properties: (1) Diverse range of
individual components, (2) localized interactions among components, and (3) a
self-directed process that uses feedback systems and the outcomes of the
interactions in order to enhance the state of the system. Complex adaptive
systems are thought to contain a level of self-organization which emerges in
the form of patterns at higher levels and are a result of localized
interactions and selection processes at lower levels (Levin 1998).
The basic
properties of a CAS have been proposed in many different ways (Levin 1998), one
slightly different way was outlined by Holland (1995) in identifying four
properties of any CAS:
Aggregation – Basic elements of a system are
inhomogeneously organized, as such patterns of hierarchies are a natural
consequence of self-organization and an essential element in the development of
a system. It is important that the hierarchies and assemblages are not imposed
on the system by any human definition; rather they emerge as a feature of
CAS’s.
Non-linearity – Local rules
of interactions in a system change as the system develops. This is seen in the
systems dependence on the chance events which occur at the system’s inception.
The events at the beginning set the system on a path which it is in some way
restricted to. This feature manifests as resilience in some systems.
Diversity – The generation and maintenance of
diversity is fundamental to adaptive evolution and it is therefore necessary in
adaptive systems. Diversity within functional groups allows for buffering and
homeostasis for critical ecosystem processes.
Flows – Fluxes of energy, material and information characterise any system
and allow for interconnectedness of the components of an adaptive system,
without which individuals would be existing in random collections. Flows are
responsible for the way in which the self-organization emerges.
The ecosystem
displays these properties (Levin 1998) and therefore the application of complex
theory to ecosystems is opening up a new way in which to view the systems
landscape ecologists have been attempting to understand for centuries.
Applications of Complex Systems Theory
In
the case of CAS’s, and complex theory in general, a major deterrent to its
entrance into the main stream is the host of non-trivial mathematics that
accompany the theories. Although mathematics and complexity are daunting
subjects, they are inherently objective and can therefore enlighten our
understanding of systems if understood and appropriately applied. The field of
complex systems is relatively new and applications and empirical evidence is
not something that can easily be found in landscape ecology. However there are
a few interesting studies which are applying the concept of complex theory to
ecosystems and in this way revealing a host of promising and interesting ways
in which landscapes and ecosystems can be understood.
One
such example of this is in the prediction of critical transitions in systems
(Scheffer et al 2009). Complex
systems have been shown to have tipping points in which a sudden shift to a
contrasting regime may occur, this is apparent in many systems including
medicine and economics but has also been seen in ecosystems (Scheffer et al 2001). By adopting the complex
systems approach these points may be predicted (Scheffer et al 2009). So far work on basic models has produced remarkable
accuracy in predicting transition points, while application of the method to
more complex systems is showing promise. Examples in landscape ecology have
been found, such as self-organization in vegetation patterns possibly
indicating imminent vegetation loss (Rietkerk et al 2004), and, in systems ruled by local disturbance, scaling
laws which govern the structure of patterns have been found to vanish as a
critical transition approaches (Kéfi et al 2007).
The
technique of renormalization group analysis is one that was developed in
physics (Creswick et al 1992) and can
be applied to landscape ecology in a complex systems approach. The technique
allows for thresholds and domains to be detected as critical phenomena (Milne
1998). The renormalization strategy aims to determine processes at the level of
the individual and aggregate the system to coarse scales in order to make
predictions of scaling exponents for macroscopic patterns (Creswick et al 1992). An example of the
application of this concept to a landscape occupied by trees is provided in
detail in Milne (1998). Scaling and renormalization approaches contrast with
traditional approaches to ecology which tend to focus on empiricism and
analysis of variance, this approach searches for invariant system properties
(Milne 1998) and can be thought of as a more “first principles” approach to
modelling systems.
The
acceptance of landscapes as complex systems has facilitated collaboration
between ecology and computer science, especially in the form of remote sensing
and spatial analysis. Growth in technology of data collection at large scales
and extents with high resolution has provided an excess of multi-spatial,
multi-spectral, and multi-temporal resolution data (Hay 2002). This ability to
observe landscapes at a large scale, along with complex data analysis tools
such as Scale-Space theory (Lindeberg 1994), creates the possibility of a framework which may
assist in automatically defining critical landscape thresholds, domains of
scale, ecotone boundaries, and the grain and extent at which scale-dependent ecological
models could be developed (Hay 2002, Blaschke and Hay 2001).
A Better Approach
The use of a complex systems approach to landscape ecology works to
fundamentally remove any bias that could be applied to ecosystems by the human
scale that we perceive the world to function at. By fully embracing the complex
systems approach it seems it may be possible to allow landscape ecology to
express its own laws and define its own boundaries. Although many of the
boundaries and patterns that we observe may be rightly attributed to processes,
especially as some may coincide with the scale at which we perceive the
environment, however the possibility of revealing unknown processes, scales or levels
is too exciting to ignore.
Given the necessity to understand the processes that govern the ecosystem
that we depend on a solid framework for viewing the landscapes is imperative.
Ernst Neef may have been introducing the bias of human construct to landscapes
when he classed them as large scale spatial arenas in which humans interact
with the environment, but he was not wrong. Our landscapes, although separate
entities which have their own processes and patterns, are the most direct link
that humans have to the ecosystem. For this reason, I believe that an
understanding of the complexity of the processes that govern the patterns of
the ecosystem in which we exist, is of the utmost relevance. There is no doubt
that by understanding the mechanisms behind biodiversity, species assemblages,
vegetation patterns or energy flows through a system, we can optimize our
management of these systems and thus better utilise and conserve them.
The
growth of landscape ecology over the last 30 years is a remarkable story on its
own. Adding in the revolutionary approach of conceptualising systems as complex
entities which create patterns as a result of finite local processes results in
an exciting new age for landscape ecology and ecology as a whole. Landscape
ecology seems to be rushing forward towards a multidisciplinary field in which
the tools to answer questions as well as discover questions never asked may be
possible. Perhaps it may at least be possible to separate the knowable unknown
from the truly unknown, as Levin (2002) eloquently said in reference to the
development of statistical mechanisms. The daunting introduction of complex
mathematical notions to practitioners more comfortable in the physical world
should be mollified by the exciting possibilities that the coalition of fields
creates.
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