 In this video we'll start the course off with a high level overview to the area of systems ecology. We'll firstly talk about the systems paradigm in general, before looking at some of the main theories and models that constitute systems ecology, including energetics and thermodynamics, emergence, hierarchy and feedback loops, before finally talking a bit about the context and relevance of this domain to contemporary science and sustainability. The term ecology was introduced in the mid 19th century, but the German zoologist Ernst Heckel, and it can be literally translated to mean the study of the household of nature. Heckel intended it to be the study of the relationship between biological organisms, their interaction with each other and their physical environment. Ecology can be understood as the study of ecosystems, which are macroscale systems of interacting biotic and abiotic elements. As such, it is an interdisciplinary science that sits at the intersection of physical and biological sciences, including elements of biology, geography and earth science. Ecology is a relatively young science then, that is formed out of biology, but more specifically botany and population dynamics. A major part of its formation during the 20th century was in the study of energy and nutrient flows through ecosystems. In the latter half of the 20th century, the study was stimulated by the development of new tools and techniques, including radioisotope traces, computer science and applied mathematics that enabled ecologists to label, track and measure the movement of particle nutrients and energy through whole ecosystems. These modern methods enabled a new stage in the development of ecology called systems ecology. Systems ecology is then the study of ecosystems that uses mathematical modelling, computation and as the name implies, is based upon systems theory. As with other areas of system science, the user systems theory as an approach involves the adoption of a holistic paradigm based around synthetic reasoning, meaning that systems ecology seeks a holistic view of the interaction and transactions within and between biological and geological systems on various scales. With this alternative approach, it does not restrict itself simply to the study of natural biophysical processes, but systems ecology now gives equal attention to the human dimension, whereas standard ecology sees human industrial and economic activity as largely outside of its domain. Systems ecology recognises that the function of any ecosystem can be influenced by human economies in a fundamental way, and that human industrial economic activity is a fundamental part of ecosystems around the world today. It has therefore taken an additional transdisciplinary step by including economics in the consideration of coupled socio-ecological systems. As such, systems ecology takes an expansive domain of interest, crossing almost all areas, from physics to biology to economics and social studies truly try and understand the workings of earth systems in all their multi-dimensional complexity. A central part of systems ecology is this holistic paradigm derived from systems theory, which is in contrast to our more traditional approach taken within the natural sciences called analysis. Traditionally, in modern science, when looking at macroscopic features of a given system, scientists have tried to find the origin of these phenomena by looking at the structure and properties of their component parts. By breaking the system down and then describing it as some linear combination of the parts. This process of reasoning is called analysis. Most of the success of modern science has relied on an analytical reductionist approach, in which systems are taken apart to examine the individual components and how they interact together. Historical examples are the isolation and characterization of the elements to the periodic table and the discovery of the particles that make up atoms. In the biological sciences, reductionism has also been very successful. Examples range from the purification of proteins, DNA and RNA, and the study of their structures and activities to the sequencing and analysis of whole genomes. Through analysis, we've developed a large and sophisticated body of knowledge within many specific domains, but this paradigm has also taken modern science down a particular trajectory. This is illustrated by one of the great heroes of nonlinear science, the chemist Iliar Prigge, when he wrote, this is indeed an essential part of the scientific revolution that we're witnessing at the end of the 20th century. Science is a dialogue with nature. In the past, this dialogue has taken many forms. We feel that we're at the end of a period that started with Galileo, Copernicus and Newton and culminated with the discoveries of quantum mechanics and relativity. This was a glorious period, but in spite of all of its marvellous achievements, it has led to an oversimplified picture of nature, a picture which neglects essential features. Classical science emphasizes stability, order and equilibrium. Today, we discover instabilities and fluctuation everywhere. Our view of nature is changing dramatically. In the past, the usual way to study complex phenomena was based on simplifying them through analytical reductionism, describing them as simple systems analogous to machines or by aggregating and averaging through statistical analysis, describing them as unorganized complexity. But complex systems, such as ecosystems, exist at a threshold between order and chaos. Because they are too complex to be treated as machines and too organized to be assumed random and averaged, they are best understood in terms of patterns and processes that emerge as we put the parts together. Simple systems may be governed by a single global rule that can be described in a beautifully compact equation, but complex systems are not governed by a single rule. They are what emerges out of the distributed interaction of many different elements. An ecosystem is what emerges out of the interaction of many different biotic and abiotic elements on different levels. Whereas with analysis, we are breaking physical systems down to their most basic constituent elements. With systems ecology, we're interested in what happens when we put things together, the processes that emerge on different levels as we build them up. Instead of talking about the properties of the parts, we're talking about the connections between them. The fact that the properties of the individual units can't always explain the whole has been known from the earliest times of science. In this context, it is often said that the whole is more than the sum of its parts, meaning that the global behavior exhibited by a given system will display different features from those associated to the individual components. A more appropriate statement would be that the whole is something else than the sum of its parts, since in most cases completely different properties arise from the interaction among the components. For example, the properties of water that make the molecules so unique for life can't be explained in terms of the separate properties of hydrogen and oxygen, even though we can understand them in detail from quantum mechanical principles. Some properties, such as memory in the brain, can't be reduced to the understanding of single neurons. Life itself is a good example. Nucleic acids, proteins or lipids are not alive by themselves. It is the cooperation among different sets that actually creates a self-sustaining pattern called life. An example of this new approach to science is systems biology, which recognizes that through the analytical approach, we have gained a very thorough understanding of the component parts to biological systems. But the one of the greatest challenges in biology today is putting it all together. There is a large and rapidly growing body of information about the building blocks of cells, proteins, RNA, DNA, lipids, etc. But how these molecules form organelles and how cells form tissues and organs is far from understood or equally how in developmental biology the genome creates the organism. Self-organization plays a central role in all of these processes and the answers are still largely a mystery. This is obviously not just an issue in biology on the micro level, but also on the macro level in understanding ecosystems and just as importantly coupled socio-ecological systems. So we've talked a bit about the basic paradigm to systems ecology as based upon synthetic reasoning, and we'll now try to give an outline to some of its main principles and theoretical models. Systems ecology studies ecosystems through abstract mathematical models and computation. An ecosystem model is an abstract, typically mathematical representation of an ecological system ranging in scale from an individual population to an ecological community and even the entire biosphere which is studied to gain understanding of the real system. Systems theory is a formal modeling language that is based upon the model of a system. A system is a highly abstract model. In its essence it is simply a set of parts and relations between those parts through which they are interdependent in affecting some joint outcome. This model proves to be very effective in providing a generic language for talking about all kinds of entities from a single cell to the entire Earth's system. Systems ecology often deals with ecosystems on a higher level of abstraction than standard ecology in order to be able to remove the details and derive formal models. These formal models of systems theory go hand in hand with computational methods and enable the interpretation of large amounts of data. This approach of using abstract models and computation allows us to approach very complex ecological systems such as the whole ecology of the Earth in a formal fashion. Systems ecology is one of the few theoretical tools that can simultaneously examine a system from the level of an individual all the way up to the level of ecosystems dynamics. It is an especially valuable approach for investigating systems so large and complex that experimentations are impossible or even observations of the entire system are impractical such as when talking about the entire biosphere. A second fundamental set of ideas within systems ecology is that of energetics interpreting ecosystems in terms of the flow of energy through networks. Systems ecology studies the flow of energy and materials through networks of biotic and abiotic elements within an ecosystem. It seeks to understand the properties which govern the stock and flow of material and energy and how they are processed through the system. An ecosystem is characterized by flows, flows of nutrients and energy, flows of material and flows of information. It is such flows that provide the interconnections between the parts and transform the community from a random collection of species into an integrated whole. An ecosystem in which biotic and abiotic parts are interdependent and fit together. The analysis of how ecosystems function is determined by how these processes and components cycle, retain, transform and exchange energy and nutrients. Systems ecology typically involves the application of computer models that track the flow of energy and materials and predict the responses of the system to perturbations. Ecosystems and biological systems in general challenge us because they are constantly consuming energy and are therefore far from thermal equilibrium. Thus classical thermodynamics which has been so successful in deriving an understanding of physical and chemical properties such as temperature and pressure does not apply to these systems. Instead of self-assembling into lower energy states such as a crystal these energy dissipating components self-organize into highly dynamic structures through which there is a constant flux of energy and materials and this is in many ways the defining feature to life. Within chemistry this is called a dissipative system and the theory of dissipative systems goes a long way to helping us understand how biological systems self-organize and evolve over time into more complex organizations. The idea of hierarchy and integrative levels of organization is another major organizing theme within systems ecology. Integrative levels is an extension of the idea of emergence that addresses the biological organization of life that self-organizes into layers of emergent whole systems that function according to non-reducible properties. This means that higher order patterns of whole functional systems such as an ecosystem can't be predicted or understood by a simple summation of the parts. These hierarchical structures have a nested pattern and the smaller subunits are nested within larger subsystems and so on. Emergence gives ecosystems a distinctive omnipresent hierarchical structure and this scale hierarchy is a primary organizational principle from biological cell to individual to community to ecosystem to the biosphere. The study of ecosystems can cover 10 orders of magnitude from microbes to the surface layer of rocks to the surface of the whole planet. In this hierarchy there are both processes and patterns that are universal having a scale invariant fractal property as they re-occur on all levels but also unique processes emerge on the different levels. But this idea of synergies and self-organization leading to emergence and the formation of new levels in a hierarchical fashion is a central model to understanding the complex multi-dimensional characteristic of ecosystems. Another major modeling approach adopted from cybernetics and systems theory is that of feedback loops which is central to understanding the dynamics of macroscale complex systems as they evolve over time and also to understanding processes of regulation and control within ecosystems and economy. On the micro level, feedback is well understood in the process of homeostasis which means maintaining things at a steady state Negative feedback homeostasis is used in biochemical processes to regulate cells, individual organs and organisms but macro processes of change such as ecosystem succession are also regulated by feedback loops. As we go up from the organism to the community to the whole biosphere there is no homeostatic centralized control system but now instead distribute of feedback loops the work to stabilize the macro system into an oscillatory flow bound within some upper and lower limits. This process is called homeoresis a term derived from the word meaning same and flow as it refers to a stabilized flow. Feedback loops tell us a lot about the dynamics to ecosystems and biological systems in general. For example, negative feedback regulates the human body as it grows starting with what is called the R stage of growth where most of the resources are used for development and little for maintenance a period of high growth rate and positive feedback before at some stage reaching a mature state where negative feedback starts to limit the growth as the system enters what is called a K stage of development now investing in other activities with negative feedback setting in as the system becomes more stable feedback loops are an example of nonlinear models that can be used to understand complex behavior within both ecosystems, economies and the interaction between them and we'll be discussing them throughout the course. Finally we'll say a bit about the current context to this subject today there is a recognized need for sciences that cut across domains in particular between the natural and social sciences our traditional scientific approach has provided us with great insight into many specific domains but the 21st century context requires us putting this domain specific knowledge together to understand some of the most complex systems that involve the coupled interaction between society and ecosystem and increasingly on the level of the whole biosphere this requires complex systems based models that are able to integrate the large amount of data now available to us and generate meaningful insight and analysis that is relevant to the contemporary challenges of sustainability Bob Bishop the president of the International Center for Earth Simulation describes the situation as such today we have hundreds of satellites in space earth observation satellites high performance computing and on the ground all kinds of radar we have data now coming in from all directions on our planet about our planet we have so much data we can't use it all we're not using it all perhaps we're using only 20% of this data so far and the other problem is that the data is specialized so we do have a problem in ingesting so much data we have a problem in analyzing it and we have a problem in connecting it across all the subsystems that are being measured integrate horizontally all this data from the hard sciences the solid earth the oceans the poles the atmosphere on the one hand and even the social sciences that drive our social economics on the other hand and the whole biosphere in between so we have so much specialized data we have so much specialized science that it's time to integrate it horizontally and understand what the entire planet is doing the whole planet and have a holistic view of the planet not just a series of specialized opinions now I think the vertically specialized data and vision will always be there and I don't think that will ever go away but I'm saying that it's time to supplement and compliment that with a full horizontal viewpoint and the public is asking for it I think society is saying that specialization is fine but give me the whole picture help me understand how these different specializations talk to each other are interconnected, are coupled this is a science that we don't have and this is a science that we need to put in place in this module we've been giving a high level view to the domain of systems ecology describing it as the application of systems theory to the study of ecology as it studies the interaction between organisms and their abiotic environment through systems models we talked about its holistic nature as it crosses both physical and biological sciences but also economics and the social sciences to understand both ecosystem and also coupled socio-ecological systems in an integrated fashion we talked about how it's based upon the process of reasoning called synthesis that is focused primarily on the interaction between systems components and the patterns that emerge out of this instead of the properties of the parts themselves we briefly discussed some of the basic principles and theories within this area including systems theory that provides the basic abstract generic models energetics that helps to study the flow of energy and materials through networks of biotic and abiotic elements within an ecosystem and tries to understand the universal thermodynamic laws that govern these processes we talked about the idea of hierarchy and integrative levels of organization that is used to structure our understanding of ecosystems in terms of emergent levels with their own internal patterns and processes we discussed feedback loops as another set of models central to understanding regulatory processes and the dynamics of macro-scale complex systems as they evolve over time finally we tried to offer a little context to the subject as it relates to the current need for an integrated understanding of socio-ecological systems given the contemporary societal challenge of sustainability