Research Article |
Corresponding author: Yrjö Haila ( yrjo.haila@tuni.fi ) Academic editor: Lyubomir Penev
© 2014 Yrjö Haila, Klaus Henle, Evangelia Apostolopoulou, Joanna Cent, Erik Framstad, Christoph Goerg, Kurt Jax, Reinhard Klenke, William Magnuson, Yiannis Matsinos, Birgit Mueller, Riikka Paloniemi, John Pantis, Felix Rauschmayer, Irene Ring, Josef Settele, Jukka Simila, Konstantinos Touloumis, Joseph Tzanopoulos, Guy Pe'er.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Haila Y, Henle K, Apostolopoulou E, Cent J, Framstad E, Goerg C, Jax K, Klenke R, Magnuson W, Matsinos Y, Mueller B, Paloniemi R, Pantis J, Rauschmayer F, Ring I, Settele J, Simila J, Touloumis K, Tzanopoulos J, Peer G (2014) Confronting and Coping with Uncertainty in Biodiversity Research and Praxis. Nature Conservation 8: 45-75. https://doi.org/10.3897/natureconservation.8.5942
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This paper summarises discussions in a workshop entitled “exploring uncertainties in biodiversity science, policy and management”. It draws together experiences gained by scientists and scholars when encountering and coping with different types of uncertainty in their work in the field of biodiversity protection. The discussion covers all main phases of scientific work: field work and data analysis; methodologies; setting goals for research projects, taking simultaneously into account the agency of scientists conducting the work; developing communication with policy-makers and society at large; and giving arguments for the societal relevance of the issues. The paper concludes with a plea for collaborative learning that would build upon close cooperation among specialists who have developed expertise in different fields in research, management and politics.
Biodiversity science, biodiversity management, biodiversity policy, dimensions of uncertainty, governance of biodiversity, research practice, scientific agency, social deliberation, social learning, uncertainty
Biodiversity is a multidimensional issue. When working with biodiversity, there are multiple sites of uncertainties involved at all stages from mundane steps of empirical field research to formulating political recommendations. However, uncertainty has usually been addressed from a narrow perspective (
The aim of the workshop was to establish a comprehensive agenda for assessing uncertainties in biodiversity praxis. We use the term ‘biodiversity praxis’ as a shorthand for all the activities supporting applied biodiversity conservation, including conducting research by collecting and analysing data, summarising and interpreting the results, drawing conclusions on conservation targets and formulating management guidelines and policy goals. All such tasks comprise decisions oriented toward the future. Formulating grounds for such decisions entails uncertainties.
We adopted a pragmatic focus: our goal was to produce an inventory of how natural and social scientists involved in biodiversity research have come across and coped with uncertainty in their working practice. The background of the workshop is described in more detail by
The workshop procedure was structured by short prepared presentations, most but not all of them with slides, invited from most of the participants with different background experience. The discussions were recorded and transcribed. This paper summarises main themes that were raised in the workshop, based on screening the transcripts (by YH) and a repeated editing process by the participants. As the paper is built on the points made by the participants during the workshop discussion, we use citations extracted and edited from the transcripts in the body of text. The speaker is indicated by his or her first and family names on the first occasions, and by the first name later on.
We adopted semantic space as a basic tool for drawing distinctions among specific types of uncertainties. A first step in analysing a semantic space is to define its dimensions (
[1] Of critical importance is the question asked and the type of data and model used to elaborate the case and identify what can potentially count as an answer. Our view of models as research tools is akin to the pragmatic perspective of
[2] The variety of practical roles that we, the participants, have had in our professional experience laid the ground for the discussions. This experience ranges from theoretical and empirical ecological and social science research, including the application of statistics and modelling, to science-policy dialogue, work in environmental administration, and hands-on biodiversity conservation.
In the following sections we take up main themes that were raised in the discussions of the workshops; the section titles are listed in Table
1. Introduction |
2. Starting Up |
2.1 What is the question? |
2.2 The basics of modelling |
2.3 Assessing errors |
2.4 Social-ecological models – a different species |
3. Research to support biodiversity praxis |
3.1 Scientific agency |
3.2 To reduce, or to deal with uncertainty? |
3.3 Multiple uncertainties in a well-elaborated case: The Norwegian Nature Index |
3.4 Contingencies of adaptation |
3.5 What about ecosystem services? |
4. Social and political reception |
4.1 Institutions and governance |
4.2 Assessing governance success |
4.3 Economic instruments |
4.4 Precaution |
4.5 The concept of biodiversity and its surrogates |
5. Communication and societal relevance |
5.1 Aiming at closures, albeit temporarily |
5.2 Enhancing public discussion |
5.3 Deliberation and social learning |
6. Conclusions: collective effort with a division of labour |
“Uncertainty becomes acute whenever we ask a question. If there is no question, there is no uncertainty. When we try to reduce uncertainty in one specific question we keep asking new questions, and more uncertainty will come out.” (Joseph Tzanopoulos)
The question asked sets the stage. Alan Garfinkel used the notion of ‘contrast space’ to describe the set of alternatives among which the explanation has to be found for a specific problem. Contrast space makes explicit the context and precise sense of a research question (
With this requirement, we get to where strictly scientific questions end and other types of questions show up. The need to continuously ask new questions is an inherent part of the societal process of coping with changing conditions. New questions build upon existing knowledge. Closure, albeit a temporary one, is a method to bring together the elements deemed necessary for making sense of a question asked [see Sect. 5.1].
“One should try to narrow down the level of uncertainty by trying to ask a fairly concrete question. For me, this would mean in practical terms that if we ask new questions, we should not say older things are not important any more although that is often done in science. Nature conservation is perhaps sufficiently diverse in practice that we should retain a stronger memory.” (Klaus Henle)
A basic epistemological strategy in empirical research is to explicate the context of the research question by constructing a model. The model delimits the research object by making visible the basic structure of the object being modelled so that necessary data can be collected, preferably experimentally although this is seldom feasible in field conditions, and calculations can be performed. A model abstracts and leaves out what is not considered essential (e.g.,
Several kinds of technical uncertainty are inherent in the modelling process and can be controlled to a certain extent by using systematic technical procedures.
“Systematic errors are measurement errors, and mainly caused by imperfect calibration of the measuring process that produces data. We can take into account random errors as standard deviations of our parameters. --- So, we may sometimes deal with a lack of accuracy, or poor quality of our data, but models are meant to work also with imprecise data.” (Yiannis Matsinos)
The nature of the research problem sets specific requirements on data quality.
“Incomplete data could be particularly detrimental when we deal with spatial modelling on different scales. --- It is important to focus on the quantitative aspects of assessment in modelling, to see how particular types of uncertainty affect model outputs in different scenario ranges, and to determine the accuracy of predictions that the model allows.” (Yiannis)
In addition, the modeller faces the dilemma that processes in nature may be inherently stochastic. Consequently, it is very difficult if not impossible to trace such inherent stochasticity and detach it from uncertainty stemming from inadequacy or lack of data. In real life, scientists have to cope with stochasticity because it is simply impossible to get enough data. Weather forecasts demonstrate this problem very concretely: The system is utterly sensitive to small differences in initial conditions, as the ”butterfly effect” parable of Edward Lorenz demonstrates (
“Can we handle the difference between deterministic and stochastic components of a setting? Sometimes we don’t know if something is stochastic because it is stochastic by nature, or only seems stochastic because we do not know enough. So for me this differentiation is superimposed on our lack of knowledge and our wish to assume that everything can be explained.” (Guy Pe’er)
The distinction between type I error (rejecting a true null hypothesis) and type II error (accepting a false null hypothesis) is familiar, but drawing the borders requires care. In particular, the decision as to which one to emphasise is consequential. Statistics provides technical criteria for evaluating the reliability of the decision, but this is strictly conditional upon the formulation of the null hypothesis and the nature of the data [as to type III error, see Sect. 5.1].
Assessing errors is particularly relevant in biodiversity research studies that attempt to give advice for management. A manager may adopt a “rule of thumb” that cannot be validated by a formal statistical test because it functions only 70% of the time, say, due to variation in environmental or societal conditions.
“The determinism vs stochasticity balance is shifted toward the stochastic side when we move to societal issues, but in ecology at least, some processes can be regarded as reasonably deterministic. --- The question is, where to best place this balance. If you do the study only yourself, fine. You do the analysis and you know where you put the balance. But others may continue the methodological procedure and ignore the simplifying assumptions made when placing the balance. In statistics, we can witness an increasing move towards the use of information theoretical decision criteria, such as Akaike’s Information Criterion for selecting among competing models. While the authors who introduced these concepts to biodiversity research outlined underlying assumptions and warned against careless interpretation of results [
Approaches to modelling in the social sphere are distinctly different from those adopted in the natural sciences. The differences are basically due to historical contingency and context specificity of processes in the social realm, but also to the fact that humans, as research objects, belong to the “interactive kind” (
A fruitful possibility is to use socio-economic models in biodiversity research primarily for understanding and communication, and to refrain from making concrete predictions. Basically, the heuristics allowing generalizations are context specific in the socio-economic sphere. Objects modelled in the social sphere are hardly ever thought of as representations of a background population that would constitute a natural domain for generalizations. Rather, socio-economic models can be viewed as analogues that allow qualified generalisations over cases of a similar type (
“In social-ecological modelling, we use process-based simulation models, which include ecological and socio-economic components and feedbacks. We often use very simple models, we call them toy models, which are not aimed really for prediction, but rather for understanding, and as a tool of thinking and communication [
For dealing with practical issues of natural resource management and conservation, participatory modelling has gained importance in the last decade (for a review see
A further approach – management strategy evaluation – may turn out to be promising for research on conservation issues in the future (
The practical purpose of research is to increase understanding and explanatory capacity concerning the phenomenon of interest and, thus, to support reasonable recommendations on what should be done. This is a pragmatic dimension of biodiversity praxis. However, there is no smooth linear transition from the realm of research to the realm of policy across what is often depicted as the ”science–policy interface.” Rather, the transition implies choices between several interpretative frameworks concerning what aspects of the results to emphasise and what significance to give to uncertainty (
Scientists have had a major role in identifying biodiversity loss as a major problem, ever since the foundational BioDiversity meeting held in Washington DC in 1985 (
Value systems and ideological positions influence attitudes of scientists toward developing strategies for promoting the social relevance of what they do (
In science, the type of successful work conducted previously acts as a point of reference, without which one cannot explain anything (
“Scientists don’t use only logical arguments. In an interview, a scientist gave me three advices: first, love the birds; second, love the birds; and third, love the birds. As
Scientists may adopt alternative roles when going public. As Roger
“It’s fine to have value components in your arguments, but one needs to be conscious about them. Then you can partially separate, let’s say, logical arguments and value systems – although never completely. --- What is perhaps even more fundamental for politicians or any other stakeholders who deal with scientists is that scientists are often living in a system of their own theories and values in a broader sense. And scientists tend to adhere to their systems of theories and values. And it’s often very difficult for them to change them.” (Klaus)
One of the obligations of scientists is to acknowledge the uncertainty pertaining to the practical mundane detail of the research process. This is all the more important as research on biodiversity and related issues has grown explosively during the last couple of decades (
“Does uncertainty have a varying role depending on the role we adopt? Do we have a different concept of uncertainty when acting as a lobbyist or advocate, as a knowledge broker, or when we act as, let’s say, pure modellers? When being a lobbyist for butterflies in Israel, I would probably be much more easy-handed with uncertainties regarding the results as long as I can make a point that would move a policy maker to do something for the butterflies. But when I’m modelling, I’m much more careful to fix the confidence level to, say 0.05 or 0.04. When I’m facilitating workshops as a scientist, then perhaps I might use uncertainty primarily as a means to facilitate discussion.” (Guy)
Scenarios are built-up images of possible futures with varying assumptions as to what kind of decisions are made (
“There are two things: we want to reduce uncertainty and we want to deal with it. To reduce uncertainty, predictions probably would be a useful tool. For me the big question is whether the goal of formulating precise predictions can actually be reached. I tend to believe that eventually entropy keeps increasing. If we cannot actually reduce uncertainty, and we have to deal with it, then predictions and scenarios may primarily help us to see what alternative futures may be in the coming. And then based on good scenarios you can actually build strategies and try to be proactive.” (Joseph)
When scientific results are used for policy advice, uncertainties become multidimensional. This situation calls for what political scientist Giandomenico
The Norwegian Nature Index (NNI) is a framework for integrated measurement of the state of Norwegian nature and its biodiversity, mandated by a government decision in 2005. The NNI is based on more than 300 individual indicators representing a wide range of species, populations, and indirect indicators of biodiversity (e.g. dead wood), covering nine major marine, freshwater and terrestrial ecosystems (
The process of developing the NNI revealed different types of uncertainties, from the traditional issues of precision and accuracy of natural science data to the more fundamental issues of the meaning of biodiversity and interpretation of the concept of the reference state. The assessments of indicator values were based partly on actual data and modelling, partly on expert judgement, and often on a combination of both. For most indicators, available data were not statistically robust, due to, for instance, a low number of subjectively-selected sites. Hence, some form of expert judgement or ‘model-based inference’ (
This type of uncertainty is within the scientific paradigm that most natural scientists are comfortable with. They have some data and knowledge about their indicator and the ecosystem(s) it is part of, and they can, with more or less confidence, say what the state of a particular indicator might be at different times and sites, even though actual observations are lacking.
It turns out, however, that individual experts vary considerably in their ability or willingness to use their expert knowledge to extrapolate indicator values beyond the set of observed values, and several have expressed concerns about the uncertainty involved in such extrapolations (
The most immediate question of uncertainty in constructing the NNI appeared at the very beginning of the development process: How can a complex phenomenon, such as biodiversity, be captured by just a few indicators, and which indicators should be included to represent the state of nature and biodiversity? The core group in charge of the project decided early on to focus explicitly on biodiversity components as far as possible, i.e. by mainly using indicators based on some form of species population levels or indirect indicators with close association to species, and to avoid indicators representing direct drivers.
The actual selection of indicators was done by the invited experts in cooperation with the core group and was based on a specified set of criteria. The possible biases stemming from the selection process were then reduced by including more rather than fewer indicators and by weighing each indicator in such a way that each functional group defined had the same overall weight in the index (e.g., many primary producers were equivalent to a few decomposers). Some experts pointed to the uncertainty as to whether this resulted in an index sufficiently sensitive to key direct drivers and thereby producing an NNI relevant to the mandate.
In assigning values to the NNI, the experts were not just asked to assess values for their indicators for the present (2010) or previous years, but also to give their best assessments of indicator values for the future (2020), given current management policies for the relevant ecosystem (
In addition, most experts found it difficult to consider forecasting without worrying about some potential fundamental changes in management policies or ecosystem dynamics. A similar type of uncertainty may apply to assessment of indicator values in the somewhat distant past (e.g., 1950), where little credible supporting information may be available for most indicators. Such ‘back-casting’, as well as forecasting, would force the expert to address a more basic kind of uncertainty than the mere lack of precision or accuracy in the existing observations, namely the uncertainty of whether the fundamental dynamics of their ecosystems are preserved over time or not.
Perhaps the most challenging type of uncertainty about the NNI pertains to how the reference state is understood. It relates to more than a mere starting point for measurement. For each indicator, the experts must also link the concept of a reference state to the significance of observed changes in their indicators for the state of nature and biodiversity and decide on a scaling model for this relationship. Also, unless the various indicators relate to the same concept of a reference state (at least for the same major ecosystem), the values of the indicators cannot be compared or aggregated into one index.
For all major ecosystems except “open lowlands”, a reference state based on intact natural ecosystems with minimal human impact was specified. However, various operational interpretations of such a reference state were allowed, reflecting quite different perceptions of the appropriate basis for comparisons against the current state among the experts. Some had fundamental objections to a reference state of ‘pristine nature’, others felt the overall objective of the NNI was better reflected by a reference state of sustainable management of ecosystems, whereas others simply found it impossible to decide on indicator values for a ‘pristine’ reference state. The conceptual uncertainty about the reference state continues to challenge the experts and their approach to the NNI.
The public understanding of biodiversity may involve an unrealistic perception of a desirable static balance, but the components of biodiversity are evolving. Species survive in the long term only if they manage to adjust to environmental variation. Evolution is an existential game, and success means ability to stay in the game (
“In some directions in conservation biology, we try to incorporate evolutionary aspects as well. My question is: to which extent should we differentiate our biodiversity management in saying that we ignore such adaptive processes and the way species are able to incorporate them, or that we focus on them and say, well, it’s not relevant what we have now, the only important thing is to maintain the process of adapting to a stochastic and changing world. Such an approach would imply very different conservation strategies from what is the dominant approach today. On the other hand, views on conservation have changed a lot in the past [
This consideration points toward another, dynamic source of uncertainty in conservation: we are facing a big question mark on how to improve the correspondence between human-induced changes in the environment with the dynamics of crucial habitat features critical to particular groups of organisms – across a range of temporal scales (
Ecosystem services are relative newcomers in the conceptual repertoire of conservation biology. It is not self-evident that the goal to safeguard ecosystem services is congruent with the goal of biodiversity protection (see
Also, ecosystem services can be specified using several criteria that may be connected with biodiversity in different ways. Biodiversity and ecosystem services are often clearly coupled, but problems arise when measures to protect ecosystem services have contradictory effects on biodiversity (
“Biodiversity and ecosystem services are very different; the Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES), for instance, considers them as two separate issues, exactly so they don’t compete with each other. What I like about both terms is that at least as ideas, they are both scale-independent. But questions arise when you try to measure either of them. The measures have to be scale-dependent. You cannot measure biodiversity on the global scale in the same way you would monitor changes on local scales. Neither can you consider the same set of ecosystem services, and their fluxes, at the local or global scales. --- So, if we can, at least, agree that the concepts of biodiversity and ecosystem services are both important at a given scale, then we can reduce some of the uncertainty that could come up if we try to always link the two concepts as if they were equal.” (Guy)
The adoption of formal institutional devices, such as laws and policy documents, create uncertainties as well. Unclear goals, or goals imposed from above, can create significant conflicts and legitimacy problems (
“But on the other hand, talking about the uncertainty of institutions gets often far too general. Institutions were actually created to reduce uncertainty in social life. But failure is always possible; analogously, we have market failures, we have state failures, and so on. I think this brings forth one dimension of uncertainty in social life. Furthermore, you may have a rule in social life but you cannot really predict whether people follow this rule. Perhaps it is followed as a statistical pattern, but you can never predict whether specific persons behave in a specific way.” (Christoph Görg)
Conditions of stability and predictability of social institutions raise general questions concerning good governance. It is not obvious what the ideal relation between good governance and uncertainty should be. Uncertainty is a pervasive phenomenon in social life in any case, so the question of whether to reduce uncertainty or cope with uncertainty becomes particularly acute in this context.
“We can say that we have a good governance process when the process itself is good, when its output is good, and/or when the outcome, i.e., the consequences of this process are good [
The scale of the political governance system and the size of administrative units it covers matters as well (
“Every policy strategy, every law is always a compromise, built upon different interests and power relations related to such interest. A law or governance measure in itself is not good governance, but it is real governance. It includes some interests and power relations and so on, but we have to reflect in a normative way on how to improve this.” (Christoph)
A difficulty with assessing consequences of particular governance measures, such as the process of designing the Natura 2000 network or reforming the Common Agricultural Policy in Europe, is that the effects of specific conservation measures get diluted over time into changes in the society due to other kinds of processes. The temporal scale is important in this context. Big changes do not take place overnight. In fact, 20 or 30 years may be a very short time for essential change to take place. We ought to think more in terms of social and political dynamics, their temporal matching and mismatching, and their mismatching with ecological processes (
“I very much like the idea of starting with accepting knowledge gaps and, despite this, realising that we have to do something. It is more motivating to look at processes instead of necessarily looking at the final outcomes. We have the same problem in ecology where sometimes we simply need to find some rules that govern the system, because such rules are more robust to uncertainty. --- We also need to consider trade-offs, for instance in the case of inefficient funding: we may put a lot of efforts into conservation and restoration but they fail because of a parallel process which may be more effective, and completely contradicting the first one. I think that agri-environmental schemes are an excellent example [
A possible approach to assess the uncertainty, which is inherent to policy instruments, is to take the aims at face value (for literature on environmental-policy evaluation, see e.g.
However, policies can change so quickly that indicators on what follows on the ground are lagging behind. The time-lag in feedback from policy to on-the-ground actions not to speak of time-lag in ecosystem processes is a critical aspect of uncertainty when it comes to informing politicians about what is effective and what is not. People who live close to nature often know very well the systems their sustenance depends on, and should be heard. They also often have good intuition on how different policies influence their livelihood.
Also, objectives of various policies may be diffuse to start with, and they may have been designed specifically to be diffuse to decrease tensions between different sectors of administration. Or there may be sheer lack of coordination between the sectors. For example, many agricultural and other subsidies are contradictory to biodiversity policies (
“For me the question is: How can we provide guidance to improve the governance of biodiversity, not governance in general? Are we far enough that we can say something specific, or can we merely offer a list of potentially important things without giving specific advice? --- Probably there is no single rule how to assess the governance process for improving chances of success. And also, probably we need different tools depending on the main goal, so perhaps clear diversification of goals and assessment of their synergies and incompatibilities is a good idea.” (Klaus)
The protection of biodiversity touches on economics in several ways. First of all, effectiveness and efficiency of policy instruments as well as their distributional effects need to be considered. Management measures produce costs through effects on accustomed sustenance that may be hard to evaluate in advance and, with an even higher degree of uncertainty, the measures might also produce benefits. Whereas costs of biodiversity conservation, in the form of opportunity costs associated with land-use restrictions or of direct management costs, mostly accrue to local actors, conservation benefits often reach far beyond local and regional boundaries (
As a consequence, various types of regulatory and economic instruments must be included in the toolbox of biodiversity management (
The strict requirement of cost-benefit optimality can be relaxed, but indicators and qualitative measures are necessary, and applying an evolutionary perspective to policy-making rather than a static-equilibrium-oriented perspective helps in this context (
“We don’t know the optimal solution at a certain point in time. It is more important to try as far as possible to move into the right direction, if you can say what the right direction is. This is relevant for the precautionary principle: it is easier to find the right direction than try always to do the optimal thing. Adaptive management builds upon a similar idea: if you are able to at least measure some properties related to sustainability, we should be able to see whether a course of action will lead in that direction, more or less.” (Irene Ring)
Another major issue is that all goals cannot be reached everywhere at the same time: priorities have to be defined, and choices have to be made, at least in part by weighing costs and benefits. This creates uncertainty: Are the weights appropriate? In this context,
The use of option value as a framework is a close kin to another decision rule recommended by economists in a situation of uncertainty: the strategy of the second best, i.e., setting goals that are more robust than the calculated optimum, which may be unattainable anyway (Lipsey and Lancaster 1956-1957,
The precautionary principle originated in the context of environmental policy and the volume of the literature on the topic is huge (as an introduction, see
“The precautionary principle is tricky and allows different interpretations. There was a huge contestation between the United States and Europe on what the precautionary principle exactly means in the biodiversity convention and the Cartagena protocol. It is not only about uncertainty, it is more about the possible impact of something that we perhaps do not really understand.” (Christoph)
The demand for precaution is the more convincing the better we can delineate alternative options and their concomitant uncertainties, but it is relevant also under less stringent conditions (
“I think that the protection of biodiversity and maintenance of ecosystem services can potentially go in opposite directions as regards the precautionary principle. Conservation of biodiversity is based on the assumption that we should protect biodiversity for its own sake, in accordance with the precautionary principle. We assume that it is beneficial also for humans, but it is valued for its own sake. However, in the context of ecosystem services attention to biodiversity is conditional upon its effect on specific ecosystem functions.” (Jukka Similä)
In other words, in the latter case uncertainty is more troublesome: we want to know what the service in question is and reduce uncertainty as to what actually follows when we protect a certain asset. Notwithstanding, as regards systems poorly known, precaution will remain a very important principle.
Ultimately, the aim to protect biodiversity has to make sense to a broad public that forms an active public; an idea building on the classic formulation of the dynamics of publicity by John Dewey (
“What is biodiversity? There is some conceptual ambiguity. First of all there is often uncertainty about proper objects of research and management. Is it species numbers, is it genetic variability, or is it life on Earth? This gets down to the question: What are the goals of conservation efforts? A specific question in this respect is how to deal with exotic species [
One way to clarify the confusion is to draw a distinction between the brief characterisation of biodiversity in the Convention on Biological Diversity versus problems that arise when it is applied to policy or management. On general terms, the brief definition provided by the convention can be considered clear and quite satisfactory, but it does not easily transform into guidelines.
“We need to decide which components of biodiversity we want to focus on. A number of policy documents are not clear about this. For instance the goal of higher biodiversity to me is meaningless.” (Erik Framstad)
This source of uncertainty demands that one should clearly recognise the context in which the term biodiversity is used as an argument. The term may give rise to problems as biodiversity can be operationalised in alternative ways (e.g.,
“Another issue is how much power and weight different parties have in the discussion [
Seemingly, uncertainty about what biodiversity is may create serious confusion with respect to what to monitor, where and how (
To succeed in the aim of protecting biodiversity, conservation biologists need to learn to get their message through. When exploring the concept of causality, Herbert
This idea is worth taking seriously. However, it is self-evident that communicating the need to protect biodiversity to the society at large is much more demanding than merely spreading a message. To become influential, the communication has to strike a cognitive chord.
A specific message needs a specified context. This principle corresponds to the demand that satisfactory closure conditions are necessary for a satisfactory scientific explanation (
To further specify this demand, we can use Herbert
A too hastily formed closure is, however, vulnerable to type III error: answering the wrong question (Dunn 2001,
Depending on the nature of the closure, the concomitant uncertainty is bounded as well. The better the understanding of the structure of a system under research, the more uncertainty is bounded (
“Differentiation between goals might help and trigger fruitful discussions. There are certainly policy goals, which have obtained so good a closure that it does not matter what the values of the people are. For instance, speed limits on motorways. Just make a speed limit and it does not matter what people think. After a new rule is enacted, people change their ways and values when they learn to follow the rules. Norbert Elias called this the technisation of society [
However, closure is always temporal and contextual. Any proposed closure can be challenged, and established closures can be opened up to further consideration. Speed limits may be lowered in residential areas, in the vicinity of primary schools or fire stations, and so on. A historical demonstration of changes both in closure and norms is offered by regulation of hunting and species protection (
Uncertainty can serve as an entry point to discussions, even concerning quite complicated issues, such as the relationship between ecosystem services and biodiversity. Uncertainty may play an important role. It is a question of communication, how we can use uncertainty instead of blowing it up all the time to levels where we just do not communicate at all (see
We also need to take into account the potential that uncertainty offers for opponents of environmental concerns, as the example of climate sceptics shows (
“For raising awareness about specific problems, uncertainty may create difficulties, but it may stimulate public discussion. But if you want to define policy strategies you have to look for costs and benefits, you have to look for side effects, and therefore you need some knowledge. --- It is much better to communicate uncertainty than to speak with a strong conviction: this is the result, this is the truth, the scientific truth.” (Christoph)
It is well known that conflicts can also provide entry points to fruitful discussions. This, however, depends on the nature of the conflict. For instance, the establishment of the Natura 2000 network in the EU has given rise to local conflicts in several countries. If such conflicts lock in as contests of prestige between authorities and local inhabitants, the consequences may be mainly detrimental, and this is what largely happened in Finland (
“Then the main approach of regional administration was to engage local politicians and authorities into consultation programs to try to talk to them and perhaps manage the conflict a little bit. A source of uncertainty at this stage was the relevance of social conflicts. Then there was new recognition of importance of local communities, especially landowners and people in charge of community-owned land at the municipal level. We as a research team tried to provide the authorities a diagnosis of the local conflicts of opinion, or at the very least insight into the consultation process and the role of stakeholders, but the policy priorities were different. If the focus on managing conflicts and landowners and local communities had been present from the beginning, the process might have looked quite different.” (Joanna Cent)
Given a communicative start, however, conflicts could become occasions of mutual learning. Local conflicts have the potential of bringing specific questions into focus, such as how to combine biodiversity preservation and local livelihoods.
“A potential conflict might bring different opinions into the open; for instance if nobody knows in advance what different stakeholders think about, say, the Natura 2000 process and what the consequences are for them. A research project functions almost like an intervention. --- And so such a situation is a fantastically interesting case of the potential of using uncertainty to enhance fruitful discussions. In fact, the process in Poland took quite a long time, something like five years. There would have been enough time for fruitful communication.” (Yrjö)
According to the view of political scientist
“Negotiations are possible, based on the common ground, eventually. The first step, the first stage in order to find common ground and negotiate is to understand different logics, different knowledges, and that basically we scientists are one specialist party, and there are many other people with completely different opinions.” (Joseph)
The development of the Nature Index for Norway is an interesting example in that it has a very ambitious aim: to have an instrument to provide an overall assessment of how well Norway maintains nature and avoids loss of biodiversity [Sect. 3.3 above]. With the various statistical and more fundamental uncertainties inevitably associated with the NNI, one may question whether such an objective will ever be obtained. The experts involved in the process expressed concern, but they still considered the resulting index values to be reasonable for their respective ecosystems (
“I’ve been in this sort of game for 30 odd years, communicating with policy makers and lay people. I’m not sure if I have really taken on the role as an advocate as such, to any great extent. But of course we communicate with different people, within different contexts. If I am talking to journalists or others who need to have a fairly simplified message to their readers, I probably do not spend a lot of time making any complex statement with a lot of uncertainties about this and that. Also, when communicating with bureaucrats who are there to execute policy, we often get criticised for not being clear enough; they dislike that. --- But in the context of the Norwegian Nature Index, we also had a lot of debate with a broader audience beyond natural scientists, particularly on forests, that was really the one nature type where everybody had opinions. We’ve been going around to local municipalities, talking to the forest managers, the officials at the municipality level, plus representative forest owners. And it’s surprising how benign and accommodating they are – it is a process that seems to be leading to greater consensus about the aim of the project, and how it can be useful.” (Erik)
Another interesting dimension of public deliberation has been raised by the potential tension between general goals and specific applications. It is generally assumed that specific topics offer grounds for fruitful discussion, but this is not always the case.
“Under which conditions does collaborative learning work, so that people merge towards a common understanding despite differing goals, and when it doesn’t work? What does this difference mean for our approach in the management? An example I can give is a study in the context of a conflict on the establishment of a nature reserve in which the utilisation of different spatial representations were compared. When they used virtual spatial experimentation that was not representing the real case study, it worked. But when they used the real map this was not helpful. People were too concerned to secure their own claims and not open to look for solution where all would be better off. [
Biodiversity praxis draws upon a diverse combination of specialised skills that range from field work and data analysis to formulating management targets or policy goals and lobbying for implementation. As
The semantic space is multidimensional.
Managing Natura 2000 sites offers an example. On the one hand, there are field surveys, and reports offering conclusions on the conservation values of any particular site. On the other hand, there are needs and wishes of local people and visitors. The problem is to fit these two sets of factors together.
“Of course, if it is an absolutely unique site, then there is no way to undermine the idea that this is a valuable site that has to be preserved. But if it’s not, as Natura areas usually aren’t, then there are potentially other things to consider, also compensatory procedures. So it’s not only that the conservation goals should be watered down, it’s also that conservation goals can be enriched by some kind of societal considerations.” (Yrjö)
The accustomed methodology of scientific research includes elements that point toward cooperation that promotes learning. Our task is to grab the opportunity.
“What we are mostly discussing is basically a scientific cycle within science. But then we also have the societal cycle, which is as large if not much larger – society with its own processes, or if you want, socio-economics. --- The stronger the links are, the more adaptive, for instance, management can be. So the idea of participatory modelling, for instance, is that the process itself is more compact. That’s the process also of developing good monitoring, or a good index: to put more and more people together, and then perhaps we can pack the societal and scientific processes, to get more adaptive and quicker, and better respon to uncertainty.” (Guy)
Also, work on the local level offers other kinds of potential openings for fruitful learning processes. There is a discrepancy that stems from the generality of global recommendations and the specificity needed in local contexts.
“The problem of scales becomes acute when you think the (local) system you are working with is closed, but it’s not, and that means that what you are doing is not achieving what you believe it will achieve. So the question for me here is not how you can make sure that the information from the outside gets into the local scale. Rather, the question is, how can you provide those who work on the local scale good enough guidance on when they have to go outside of the local scale, and when they can work on the local scale. --- Even if it’s not essential for the particular case they should at least be aware that a larger scale exists.” (Klaus)
Finally, the normative background of the concern over biodiversity requires attention because it gets mingled with all other dimensions of biodiversity praxis. Biodiversity preservation is basically a normative principle. It has a very strong material basis in the human biospheric dependence, but the normativity breaks through because there are always several ways to reach particular goals, the more so the more general the goals (Haila 2004,
A grant from the Academy of Finland (136661) facilitated Yrjö’s work on this project, including a stay at UFZ in Leipzig in October-December 2011. The participation of members of the SCALES project in the November workshop was supported by the SCALES project (Securing the Conservation of biodiversity across Administrative Levels and spatial, temporal, and Ecological Scales) funded by the European Commission as a Large-scale Integrating Project within FP 7 under grant #226 852 (