New Models for Ecosystems Dynamics and Restoration

Recently I’ve been working on a review of the latest contribution to The Science and Practice of Ecological Restoration book series, entitled New Models for Ecosystems Dynamics and Restoration (edited by Hobbs and Suding). Here’s an outline of what I’ve been reading and thinking about – the formal review will appear in print in Landscape Ecology sometime in the future.

The Society for Ecological Restoration defines ecological restoration as an “intentional activity that initiates or accelerates the recovery of an ecosystem with respect to its health, integrity and sustainability”. Restoration ecology is a relatively young academic field of study that addresses problems faced by land managers and other restoration practitioners. Young et al. suggest that models of succession, community assembly and state transitions are an important component of ecological restoration, and that seed and recruitment limitation, soil processes and diversity-function relationships are also important.

The ‘new’ models referenced in the title of the book are ‘threshold’ or ‘regime shift’ ecosystem models. These models are ‘new’, the editors argue, in the sense that they contrast gradual continual models and stochastic models. Gradual continuous models are described as those that assume post-disturbance ecosystem recovery follows a continuous, gradual trajectory and are associated with classical, Clementsian theory that assumes steady, uni-directional change towards some single equilibrium state. Stochastic models assume exogenous drivers dominate the behavior of ecosystems to the extent that non-equilibrium and unstable systems states are the norm. Threshold models assume there are multiple (in contrast to the Clementsian view) stable (in contrast to the stochastic view) ecosystem states and represent changes from one relatively distinct system state to another as the result of small changes in environmental (driving) conditions. Thresholds and regime shifts are important to consider in restoration ecology as there may be thresholds in system states beyond which recovery to the previous (healthy) state is not possible.

Two types of threshold model are considered in New Models;

i) state-and-transition (S-T) models that represent multiple (often qualitative) stable states and the potential transitional relationships between those states (including the rates of transition), and

ii) alternative stable state (ASS) models which are a subset of S-T models and generally represent systems with fewer states and faster transitions (flips) between the alternative states.

For example, S-T models are often used to represent vegetation and land cover dynamics (as I did in the LFSM I developed to examine Mediterranean landscape dynamics), whereas ASS models are more frequently used for aquatic systems (e.g. lake ecosystems) and chemical/nutrient dynamics.

New Models focuses on use of these models in ecological restoration and provides an excellent introduction to key concepts and approaches in this field. Two of the six background chapters in this introduction address models and inference, two introduce transition theory and dynamics in lake and terrestrial ecosystems (respectively), and two discuss issues in social-ecological and rangeland systems. These background chapters are clear and concise, providing accessible and cogent introductions to the systems concepts that arise in the later case studies. The case studies present research and practical examples of threshold models in a range of ecosystems types – from arid, grassland, woodland and savanna ecosystems, though forest and wetland ecosystems, to ‘production landscapes’ (e.g. restoration following mining activities). Although the case study chapters are interesting examples of the current state of the use and practice of threshold modeling for ecological restoration, from my perspective there are certain issues that are insufficiently addressed. Notably, there is limited explicit consideration of spatial interactions or feedbacks between social and ecological systems.

For example, in their background chapter King and Whisenant highlight that many previous studies of thresholds in social-ecological systems have investigated an ecological system driven by a social system, ignoring feedbacks to the social components. Explicitly representing the links between social and ecological components in models does remain a daunting task, and many of the case studies continue in the same vein as the ‘uni-directional’ models King and Whisenant hint at (and I’ve discussed previously). The editors themselves highlight that detailed consideration of social systems is beyond the scope of the book and that such issues are addressed elsewhere (including in other volumes of the Ecological Restoration book series – Aronson et al.). However, representing human-environment feedbacks is becoming increasingly vital to ensure appropriate understanding of many environmental systems and their omission here may prove unsatisfactory to some.

A second shortcoming of the book, from the perspective of a landscape ecologist, is the general lack of consideration for spatial pattern and scaling and their influences on the processes considered in the case studies. In their background chapter on resilience theory and rangelands, Bestelmeyer et al. do highlight the importance of a landscape perspective and considering land as being a ‘state mosaic’, but only a single case study really picks up on these concepts in earnest (Cale and Willoughby). Other case studies do indirectly consider spatial feedbacks and landscape context, but explicit representation of relationships between spatial patterns and ecosystems processes is lacking.

However, these criticisms do need to be considered in light of the objectives of New Models. At the outset, the editors state that the book aims to collectively evaluate threshold modeling approaches as applied to ecological restoration – to examine when and where these models have been used, what evidence is used to derive and apply them, and how effective they are for guiding management. In their synthesis chapter the editors highlight that the models presented in the book have been used heuristically with little testing of their assumptions and ask; “Does this indicate an obvious gap between ecological theory and restoration practice?” For example, in their chapter on conceptual models for Australian wetlands, Sim et al. argue that the primary value of threshold models is to provide a conceptual framework of how ecosystems function relative to a variety of controlling variables. The editors’ suggestion is that restoration practitioners are applying models that work rather than “striving to prove particular elements” (of system function or ecological theory), and that maybe this isn’t such a bad approach given pressing environmental problems.

Potentially, this is a lesson that if landscape ecologists are to provide ecosystem managers and stewards with timely advice they may need to need to scale-back (i.e., reduce the complexity of) their modeling aims and objectives. Alternatively, we could view this situation as an opportunity for landscape ecologists to usefully contribute to advance the field of ecological restoration. Most likely it is indicative that where practical knowledge is needed quickly, simple models using established ecological theory and modelling tools are most useful. But in time, as our theoretical understanding and representation of spatial and human-environment interactions advances, these aspects will be integrated more readily into practical applications of modelling for ecological restoration.

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A Companion to Environmental Geography: Brief Review

A couple of weeks ago I received my copy of ‘A Companion to Environmental Geography‘ to review for Progress in Physical Geography. I’m still working my way through the edited volume’s diverse material, and on the review, but I thought I’d post a brief outline here along with a few thoughts.

The diversity of issues and approaches demonstrated in the Companion is a result of both the editors’ objectives to demonstrate the size, breadth and multiplicity of geographical work at the people-environment interface, and definition of environmental geography; “any form of geographical inquiry which considers formally some element of society or nature relative to each other” (p.6). The chapters address issues ranging from ‘Complexity, Chaos and Emergence’ and ‘Uncertainty and Risk’, through ‘Landscape, Culture and Regional Studies’ and ‘Ecosystem Prediction and Management’ to ‘Marxist Political Economy and Environment’ and ‘Environmental Discourse and Representation’.

The editors’ broad definition of Environmental Geography is, in part, a response to the increasing specialisation of science in general and geography specifically. Their definition is also a result of the perceived need to think more clearly about the relationships between the sub-discplines of geography rather than just the simple human/physical dichotomy, as I have discussed previously. Increasing research specialisation has resulted in a growing irrelevance of (and difficulty of achieving) the traditional view of ‘symmetric’ Environmental Geography in which both humans and their environment receive equal attention and treatment. Research in contemporary Environmental Geography is largely asymmetrical (i.e., research focus is generally more on either the human or environmental dimension) as demonstrated by the many of the chapters in the Companion.

Such a broad definition also allows the emphasis of what is seen as a traditional strength of Geography – the possibility of multiple diverse approaches to examine human-environment interactions. Indeed, editors Castree, Demeritt, Liverman and Rhoads suggest that “Environmental Geography’s plurality can make it a player in such grand endeavours [as addressing global environmental chage and sustainability] yet without sacrificing its capability to offer multiple insights and perspectives on human-environment relations” (p.12). A player it may be, but other human-environment researchers are now arguing that their more systematic approaches move beyond Environmental Geography and, as Billie L. Turner’s chapter highlights, the geography is no longer necessarily the primary domain of the study of coupled human-environment systems; “the immediate future appears to be one in which geographic practitioners of land systems are drawn increasingly into integrative science programmes, while geograghic pedagogy, more so than at any other time in the past, opens to practitioners from beyond the formal discipline” (p.174).

The Companion, is certainly more than a Dictionary – each of the 32 chapters following the introduction from the editors provides an introduction to key ideas, methods and debates that will be accessible to advanced undergraduates and beyond. The chapters are divided into four sections – Concepts, Approaches, Practices, and Topics – some tackling questions at the cutting edge (e.g., what are the interlinked social and environmental implications of commodifying nature, and of commodification more generally?), some calling for advances or changes in perspective (e.g., current consideration of uncertainty and risk is a facade on deterministic approaches) and others providing more benign, yet no less stimulating, introductions to the issues. Such is the diversity of human-environment issues covered that not all chapters will be of interest to all readers. However, the book will be a useful reference for all scholars of human-environment interactions, whether to provide inspiration for potential research approaches or as a teaching tool to introduce students to the breadth of topics in Environmental Geography.

I’ll post again with a link to the final review once it’s published.

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Engaging the Future

The book review I wrote for Environment and Planning A appears in the latest issue (39:11). View the pdf here or read on below…

Engaging the Future: Forecasts, Scenarios, Plans, and Projects (2007) edited by Hopkins, L.D. and Zapata, M.A.

The future is inherently uncertain. In accepting this we should not be fatalistic suggest the authors of Engaging the Future. Rather, as the title of the book suggests, scholars, planners, public officials, and citizens alike should endeavour to engage the future, creating and shaping it via a continuing process of regional and urban planning. The tools available for us to advance this process are forecasts, scenarios, plans, and projects.

The opening chapter by the editors Hopkins and Zapata sets the tone for the volume, highlighting that these tools are ways of representing, manipulating, and assessing ideas about the future. They allow us not simply to think about the future but also to influence it. Predictions, however, are conspicuous by their absence from Hopkins and Zapata’s putative toolbox. This, as Moore discusses in chapter 2, is because of an all too frequent over-reliance on quantitative output from models. Moore complains that the emphasis on using numerical predictions about populations, transport demands, and other regional trends can inhibit creativity, stifle debate, and limit policy alternatives, when predicted futures are regarded as inevitable ones. Thus, numerical predictions can suppress uncertainty rather than engaging and dealing with it effectively.

The alternative approach, developed and explored throughout the remaining chapters, is one that is increasingly reflexive, collaborative, democratic, and consensual. Both the tools that will facilitate this approach and their use in (predominantly American) case studies are presented and discussed. In chapter 3 Grant discusses the use of visioning to improve participation in the planning process, highlighting both the advantages (democratic inclusion) and drawbacks (potential munipulation) of such an approach. Myers (chapter 4) introduces the idea of narratives to examine how individual choices will influence future communities, and stresses that, if quantitative data about the future are to be used, they must be embedded within a story that describes community transformations through time. Narratives are also discussed as tools by which to engage and generate ‘reflective conversations’ between diverse parts of the public (Cummings, chapter 12) and to highlight multiple views and expectations about the future rather than suppressing them (Zapata, chapter 13).

Chapters 5 (Smith), 6 (Avin), 7 (Harwood), and 11 (Deal and Pallathucheril) all focus on the use of scenarios in planning in business, industrial, regional, and local community contexts. In these contexts, scenarios differ from forecasts as they do not assign any probability or likelihood estimates to their feasibility, and so better able to explore nonstationary processes and their normative implications. By generating scenarios using the input from local stakeholders these authors suggest community concerns, perceptions, and values can be integrated into a formal description of possible futures, helping to build the capacity of a community to plan via education, dialogue, and empowerment.

Isserman, Klosterman, and Hopkins (chapters 9, 10, and 14, respectively) continue the emphasis on the continued need for a shift away from a ‘technocratic, mystified’ approach toward an ‘open, participatory’ one. Such a philosophy is consistent with the attitude of the need to ‘democratise science’ that has been forwarded recently in the United Kingdom, particularly by organisations such as the think tank DEMOS. Echoing those debates about experts and the politics of expertise, Klosterman argues that, despite their technical skills, planners cannot claim any special knowledge about the desirability of given futures, or arguably even their probability of occurring, than ordinary citizens with their lived ‘experience expertise’ about the changing nature of the region. In turn, Hopkins suggests plans should become `living documents’ that are negotiated and support continued deliberation by multiple

This broad message of the book – to accept uncertainty and embrace participatory approaches – resonates with contemporary attitudes across other areas of environmental science and management. Adaptive resource management, for example, is a process of ‘learning by experimenting’, updating policies and management strategies as more is learnt about the system in hand. Likewise, Funtowicz and Ravetz (1993) have argued that a new form of `postnormal’ science that embraces uncertainty, individuals’ personal values, and dialogue amongst multiple stakeholders is required to solve the environmental problems arising from applications of ‘normal’, reductionist science.

However, uncertainty is politically undesirable and participation is not a panacea. Accepting uncertainty is disquieting – embracing it is even more of a challenge. Policy makers are often loathe to accept advice based on uncertainty, and where uncertainty is accepted it is often used to delay (tough) decision making. A pertinent example is political unwillingness to address the suggested causes of potential anthropogenic climate change in certain quarters because of the scientific uncertainty in those processes. Participatory approaches demand both the will and the skill to engage with non-planners. Making the planning process more inclusive is likely to slow it, potentially leading to unforeseen (and unwanted) demands on the planning process and remit. Participatory approaches will demand that planners expand their skill set to learn how to incorporate a variety of perspectives and views into their planning process.

The case studies presented in each chapter show how this might be done, offering practical ways to engage this multiplicity of demands and perspectives. In this light, Engaging the Future will be most useful for, or have most impact upon, students and junior planners. Given the emphasis of the book on wider participation in the planning process it should be read by more than just planners and students however. Well-produced with uncomplicated language, useful figures, and a glossary of planning terms, this book will be accessible and valuable both to the policy makers calling upon the services of planners and to the citizens and stakeholders who will be influenced by the outcomes of their actions.

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The Role for Landscape Ecology in Poverty Relief

In the latest issue of Landscape Ecology, Louis Iverson suggests landscape ecologists have a role in poverty relief. Reviewing SachsThe End of Poverty: Economic Possibilities for our Time, Iverson believes the book ‘should motivate additional research and implementation of principles within landscape ecology into this critical arena’ and argues that landscape ecologists‘can provide expertise to efficiently use funds to the greatest value and to research sustainable, integrated pathways to development’. After discussing several aspects of the current state of the global poverty problem (poverty statistics, water scarcity, Millennium Development Goals, environmental constraints on development), Iverson suggests landscape ecologists can contribute to these issues by;

  1. Modelling the impacts and possible mitigation of climate change on water and agricultural production, especially in the most vulnerable zones with high levels of extreme poverty
  2. Creating innovative, landscape-level systems for efficient water use, agricultural production, and infrastructure in the zones of extreme poverty
  3. Working towards sustainable management of ecosystems, especially fragile ecosystems, that are deteriorating due to human pressures
  4. Assisting in planning for urban growth that also sustains agriculture productivity using appropriate water, soil, and food management systems
  5. Building models of low-cost but sustainable means of protection against natural or technological disasters, especially storms, floods, and droughts (climate-related disasters)
  6. Designing infrastructure and energy improvements in developing countries with maximum positive human impact and minimum negative environmental impact
  7. Working to better understand the diseases of the poor and spatial and temporal relationships of these diseases
  8. Working to understand how over-consumption and excessive wealth contributes to environmental degradation and poverty elsewhere in the global landscape, and propose/model remedial solutions
  9. Developing partnerships with ecologists, economists, landscape architects, wildlife managers, and land managers in developing countries that make a difference
  10. Seeking out students from poor countries who can provide direct linkages to projects back in their home countries
  11. Assisting in land-use and urban planning efforts where practical and feasible, focusing on improving conditions for slum dwellers
  12. Working to help influence decision-makers to realize that investments toward the goals outlined above are well spent and the right thing to do


More inspiration, if it were needed, to continue this field of research…

The Wilderness Ideal

One evening whilst sitting on a deck overlooking a tranquil lake in the wilds of the UP’s northern hardwood forests, I began reading William Cronon’s contributions to the volume he edited himself; Uncommon Ground. The book has been around for a decade and more but it is only recently that I came across a copy in a secondhand book store. It seems apt that I considered what it had to say about the ‘social construction’ of nature in a setting of the type that has long intrigued me. Maybe the view of a landscape which confronted me is another of the reasons I am doing what I am right now. I have had pictures of these large wilderness landscapes on the walls of my mind, and elsewhere, for a while.

Cronon examines “the trouble with wilderness” with reference to the Edenic ideal that underlay it from the beginning. Wordsworth and Thoreau were in bewildered or lost awe of the sublime landscapes they travelled, but by the time John Muir came to the Sierra Nevada the landscape was an ecstasy. Whilst Adam and Eve may have been driven from the garden out into the wilderness, the myth was now ‘the mountain as cathedral’ and sacred wilderness was a place to worship God’s natural world. Furthermore, as the American frontier diminished with time and technology,

“wilderness came to embody the national frontier myth, standing for the wild freedom of America’s past and and seeming to represent a highly attractive natural alternative to the ugly artificiality of modern civilization. … Ever since the nineteenth century, celebrating wilderness has been an activity mainly for well-to-do city folks. Country people generally know far too much about working the land to regard unworked land as their ideal.” (p.78)

Cronon suggests that there is a paradox at the heart of the Wilderness ideal, this conception that true nature must also be wild and that humans must set aside areas of the world for it to remain pristine. As Cronon puts it, this paradox is that “The place where we are is the place where nature is not”. Taking this logic to its extreme results in the need for humans to kill themselves in order to preserve the natural world;

“The absurdity of this proposition flows from the underlying dualism it expresses. … The tautology gives us no way out: if wild nature is the only thing worth saving, and if our mere presence destroys it, then the sole solution to our own unnaturalness, the only way to protect sacred wilderness from profane humanity, would seem to be suicide. It is not a proposition that seems likely to produce very positive or practical results.” (p.83)

I’ll say. But Cronon is not saying that protected wilderness areas are themselves undesirable things, of course not. His point is about the idea of Wilderness. As a response he suggests that rather than thinking of nature as ‘out there’, we need to learn how to bring the wonder we feel when in the wilderness closer to home. We need to abandon the idea of the tree in the garden as artificial and the tree in the wilderness as natural. If we see both trees as natural, as wild, then we will be able to see nature and wildness everywhere; in the fields of the countryside, between the cracks in the city pavement, and even in our own cells.

“If wildness can stop being (just) out there and start being (also) in here, if it can start being as humane as it is natural, then perhaps we can get on with the unending task of struggling to live rightly in the world – not just in the garden, not just in the wilderness, but in the home that encompasses both” (p.90)

Sitting on that deck looking out over the lake it was clear that landscapes such as the one I was in aren’t the idealised, pristine, wilderness that they may be portrayed as in books, photographs and travel brochures. Just as in studying its nature I have come to understand a little better the uncertainties of the scientific method that is supposed to bring facts and truth, so I think have come to better understand the place of human needs within these ‘wild’ landscapes. As naive as it is to think that science might offer the absolute truth (it can’t, but it is still the best game in town to understand the world around us), thinking humans are inseparable from nature seems equally foolish.

In the introduction to a book on natural resource economics (which has mysteriously vanished from my bookshelf), an author describes a similar situation. As a young man he wanted to study the environment in order that he might save it from destructive hands of humans. But in time he came to realise this was unrealistic and that better would be to study the means by which humans use the ‘natural world’ to harvest and produce the resources we need to live. Economics is concerned with the means by which we allocate, and create value from, resources. Just as it is important to understand how ‘nature’ works, it is also important to understand how a world in which humans are a natural component works, and how it can continue to function indefinitely.

Landscape Ecology and Ecological Economics have grown out of this understanding. Whilst theories and models about the natural world independent of humans remain necessary, increasingly important are theories and models that consider the interaction between the social, economic and biophysical components of the natural world. These tools might help us get on with the task of living sustainably in the place which humans should naturally call home.

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Useless Arithmetic?

Can we predict the future? Orrin Pilkey and Linda Pilkey-Jarvis say we can’t. They blame the complexity of the real world alongside a political preference to rely on the predictive results of models. I’m largely in agreement with them on many of their points but their popular science book doesn’t do an adequate job of explaining why.

The book is introduced with an example of the failure of mathematical models to predict the collapse of the Grand Banks cod fisheries. The second chapter tries to lay the basis of their argument, providing an outline of underlying philosophy and approaches of environmental modelling. This is then followed by six case studies of the difficulties of using models and modelling in the real world: the Yucca Mountain nuclear waste depository, climate change and sea-level rise, beach erosion, open-cast pit mining, and invasive plant species. Their conclusion is entitled ‘A Promise Unfulfilled’ – those promises having been made by engineers attempting to apply methods developed in simple, closed systems to those of complex, open systems.

Unfortunately the authors don’t describe this conclusion in such terms. The main problems here are the authors’ rather vague distinction between quantitative and qualitative models and their inadequate examination of ‘complexity’. In the authors’ own words;

“The distinction between quantitative and qualitative models is a critical one. The principle message in this volume is that quantitative models predicting the outcome of natural processes on the surface of the earth don’t work. On the other hand, qualitative models, when applied correctly, can be valuable tools for understanding these processes.” p.24

This sounds fine, but it’s hard to discern, from their descriptions, exactly what the difference between quantitative and qualitative models is. In their words again,

Quantitative Models:

  • “are predictive models that answer the questions ‘where’, ‘when’, ‘how much'” p.24
  • “if the answer [a model provides] is a single number the model is quantitative” p.25

Qualitative Models:

  • “predict directions and magnitudes” p.24
  • do not provide a single number but consider relative measures, e.g “the temperature will continue to increase over the next century” p.24

So they both predict, just one produces absolute values and the other relative values. Essentially what the authors are saying is that both types of models predict and both produce some form of quantitative output – just one tries to be more accurate than another. That’s a pretty subtle difference.

Further on they try to clarify the definition of a qualitative model by appealing to concepts;

“a conceptual model is a qualitative one in which the description or prediction can be expressed as written or spoken word or by technical drawings or even cartoons. The model provides an explanation for how something works – the rules behind some process” p.27.

But all environmental models considering process (i.e. that are not empirical/statistical) are conceptual, regardless of whether they produce absolute or relative answers! Whether the model is Arrhenius’ back of the envelope model of how the greenhouse effect works, or a Global Circulation Model (GCM) running on a Cray Supercomputer and considering multiple variables, they are both built on conceptual foundations. We could write down the structure of the GCM, it would just take a long time. So again, their distinction between quantitative and qualitative models doesn’t really make things much clearer.

With this sandy foundation the authors examine suggest that the problem is that the real world is just too complex for the quantitative models to be able to predict anything. So what is this ‘complexity’? According to Pilkey and Pilkey-Jarvis;

“Interactions among the numerous components of a complex system occur in unpredictable and unexpected sequences.” p.32

So, models can’t predict complex systems because they’re unpredictable. hmm… A tautology no? The next sentence;

“In a complex natural process, the various parameters that run it may kick in at various times, intensities, and directions, or they may operate for various time spans”.

Okay, now were getting somewhere – a complex system is one that has many components in which the system processes might change in time. But that’s it, that’s our lot. That’s what complexity is. That’s why environmental scientists can’t predict the future using quantitative models – because there are too many components or parameters that may change at any time to keep track of such that we couls calculate an absolute numerical result. A relative result maybe, but not an absolute value. I don’t think this analysis quite lives up to it’s billing as a sub-title. Sure, the case-studies are good, informative and interesting but I think this conceptual foundation is pretty loose.

I think the authors’ would have been better off making more use of Naomi Oreskes’ work (which they themselves cite) by talking about the difference between logical and temporal prediction, and the associated difference between ‘open’ and ‘closed’ systems. Briefly, closed systems are those in which the intrinsic and extrinsic conditions remain constant – the structure of the system, the processes operating it, and the context within which the system sits do no change. Thus the system – and predictions about it – are outside history and geography. Think gas particles bouncing around in a sealed box. If we know the volume of the box and the pressure of the gas, assuming nothing else changes we can predict the temperature.

Contrast this with an ‘open’ system in which the intrinsic and extrinsic conditions are open to change. Here, the structure of the system and the processes operating the system might change as a result of the influence of processes or events outside the system of study. In turn, where the system is situated in time and space becomes important (i.e. these are geohistorical systems), and prediction becomes temporal in nature. All environmental systems are open. Think the global atmosphere. What do we need to know in order to predict the temperature in the future in this particular atmosphere? Many processes and events influencing this particular system (the atmosphere) are clearly not constant and are open to change.

As such, I am in general agreement with Pilkey and Pilkey-Jarvis’ message, but I don’t think they do the sub-title of their book justice. They show plenty of cases in where quantitative predictive models of environmental and earth systems haven’t worked, and highlight many of the political reasons why this approach has been taken, but they don’t quite get to the guts of why environmental models will never be able to accurately make predictions about specific places at specific times in the future. The book Prediction: Science, Decisions Making, and the Future of Nature provides a much more comprehensive consideration of these issues and, if you can get your hands on it, is much better.

I guess that’s the point though isn’t it – this is a popular science book that is widely available. So I shouldn’t moan too much about this book as I think it’s important that non-modellers be aware of the deficiencies of environmental models and modelling and how they are used to make decisions about, and manage, environmental systems. These include:

  • the inherent unpredictability of ‘open’ systems (regardless of their complexity)
  • the over-emphasis of environmental models’ predictive capabilities and expectations (as a result of positivist philosophies of science that have been successful in ‘closed’ and controlled conditions)
  • the politics of modelling and management
  • the need to publish (or at least make available) model source code and conceptual structure
  • an emphasis on models to understand rather than predict environmental systems
  • any conclusions based on experimentation with the model are conclusions about the structure of the model not the structure of nature

I’ve come to these conclusions over the last couple of years during the development of a socio-ecological model, in which I’ve been confronted by differing modelling philosophies. As such, I think the adoption of something more akin to ‘Post-Normal’ Science, and greater involvement of the local publics in the environments under study is required for better management. The understanding of the interactions of social, economic and ecological systems poses challenges, but is one that I am sure environmental modelling can contribute. However, given the open nature of these systems this modelling will be more useful in the ‘qualitative’ sense as Pilkey and Pilkey-Jarvis suggest.

Orrin H. Pilkey and Linda Pilkey-Jarvis (2007)
Useless Arithmetic: Why Environmental Scientists Can’t Predict the Future
Columbia University Press
ISBN: 978-0-231-13212-1

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[June 3rd 2007: I just noticed Roger Pielke reviewed Useless Arithmetic for Nature the same day as this original post. Read the review here.]

‘What I Want’ versus ‘What Is Best’

When ‘what is best’ doesn’t align with ‘what I want’, making the right decision is hard. We need to find ways of working out how make these options align as closely as possible.

Jared Diamond’s point in Collapse is that the fate of contemporary society is in our own hands. I read and wrote about the introductory chapter to a while ago. Eventually I did read the whole book, though as Michael Kavanagh points out;

“You could read the introduction and the last few chapters and get the point. But then you’d miss out on what Jared Diamond does best: tell stories.”

Kavanagh is right; as I’ve talked about before here storytelling is an important way of understanding the world. William Cronon has suggested narratives of global change that offer hope are needed for us to tackle the (potential) problems that contemporary society faces. Most of Diamond’s stories about the fate of previous societies don’t offer much hope however – most collapsed and the only modern example of positive action on the environment is Iceland. Diamond’s identifies five contributing factors to societal collapse:

“… climate change, hostile neighbours, trade partners (that is, alternative sources of essential goods), environmental problems, and, finally, a society’s response to its environmental problems. The first four may or may not prove significant in each society’s demise, Diamond claims, but the fifth always does. The salient point, of course, is that a society’s response to environmental problems is completely within its control, which is not always true of the other factors. In other words, as his subtitle puts it, a society can “choose to fail.”

Diamond emphasises the need for individual action – for a bottom-up approach to make sure that we choose not to fail. Kavanagh suggests the implications is that

“in a world where public companies are legally required to maximize their profits, the burden is on citizens to make it unprofitable to ruin the environment — for an individual, a company, or a society as a whole.”

Others suggest more dramatic action is needed however. Richard Smith suggests that this ‘market meliorist strategy’ won’t be enough. Smith contrasts the bottom-up decision-making of the New Guinea villages that Diamond uses as a potential model for contemporary decision-making with that of contemporary capitalist society. Whereas the New Guinea villages’ decision-making process takes into account everyone’s input:

“…we do not live in such a ‘bottom-up’ democratic society. We live in a capitalist society in which ownership and control of the economy is largely in the hands of private corporations who do not answer to society. In this system, democracy is limited to the political sphere. …under capitalism, economic power is effectively monopolized by corporate boards whose day-to-day requirements for reproduction compel their officers to systematically make ‘wrong’ decisions, to favour the particular interests of shareholders against the general interests of society.”

Smith’s solution? As the global issues contemporary society faces are so interconnected and international, international governance by a “global citizenry” is required. Critics to this approach are likely to be many, but whether it will be enough for individual consumers to “make it unprofitable to ruin the environment”, or whether the we develop a “global citizenry”, the ultimate question here seems to be ‘Are we prepared to change our lifestyles to ensure the survival of our contemporary (global) society’?

With the “End of Tradition” in western societies (i.e. life is no longer lived as fate in these societies) maybe the future of society really is in our hands as Diamond suggests. On the other hand, as Beck points out, as contemporary problems are due to dispersed causes (e.g. individuals driving their car to work everyday) responsibility is rather easily evaded and some form of global decision-making would be useful. To me the latter seems unlikely – those with power are unlikely to give it up easily. The ‘global’ institutions we currently have are frequently undermined by the actions of individual states and leaders. The power to change society and lifestyles (in the west at least) now lies with individuals. But with power comes a responsibly which, on the whole, currently we individuals are shirking.

The changes my and the next generation will need to make will have to go further than simply throwing our glass, paper and plastic in different boxes. There are small ways in which we can save ourselves money whilst helping the environment and they all add up. But sea changes in lifestyle are likely to be required. Governments will not make people do that, and have no right in a democracy. They can cajole via taxation (if they do it right) but they can’t force people to change their lifestyles. People must make those changes themselves because they want to make it profitable to sustain contemporary society. The problem is it’s very difficult to do what’s best when it doesn’t align with what you want. It can hurt. Findings ways of making the two align will become increasingly important. Often the two will not align and it will be necessary to take individual responsibility by accepting there will be a degree of pain. But once this responsibility has been accepted, the next step can be taken – working to minimise the pain whilst ensuring people get as close to what they want as possible.

Inevitably, I think modelling may have something to offer here. Just as Diamond uses evidence of historical environmental, technological and social change to discuss and tell stories about past problems we might use models to discuss and tell stories about potential problems we might face in the future. Simulation models, if appropriately constructed, offer us a tool to reconstruct and examine uncertain landscape change due to environmental, technological and social change in the future. Further, simulation models offer the opportunity to examine alternative futures, to investigate traps that might lie in wait. Just as we should learn from past histories of landscape change (as Diamond suggests), we should be able to use simulation models to construct future histories of change in our contemporary landscapes.

These alternative ‘model futures’ are unlikely to be realised exactly as the model says (that’s the nature of modelling complex open systems), and may not contain the details some people might like, but if they are useful to get people around a table discussing the most sustainable ways of managing their consumption of natural resources then they can’t be a bad thing. Modelling offers insight into states of potential future environmental systems given different scenarios of human activity. At the very least, models will provide a common focus for debate on, and offer a muse to inspire reflection about, how to align ‘what I want’ with ,‘what is best’.