- Email: [email protected]
Ecological–economic modelling for the sustainable use and conservation of biodiversity
EC O L O G IC A L E C O N O M IC S 6 2 ( 2 0 07 ) 20 3 –2 06
a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m
w w w. e l s e v i e r. c o m / l o c a t e / e c o l e c o n
EDITORIAL
Ecological–economic modelling for the sustainable use and conservation of biodiversity 1.
Introduction
Models are important tools in the development of management recommendations for the sustainable use and conservation of biodiversity (in short biodiversity management). They are used both in ecological and economic research. In ecology, models are used to analyse how biodiversity management affects the dynamics and functioning of ecosystems (Burgman et al., 1993; Grimm and Railsback, 2005). However, the usefulness of these models for policy advice is limited, because they do not address the socio-economic dimensions of the problem, including economic, institutional, and political aspects. These issues are actual topics of economic research and modelling (Shogren et al., 2003). Many economic models are, however, also deficient, when they contain oversimplified assumptions on the ecological effects of conservation or do not represent current ecological knowledge (cf. Sanchirico and Wilen, 1999). The observation that disciplinary models exhibit complementary limitations leads to the simple conclusion that it is beneficial to merge ecological and economic knowledge via ecological–economic models (Wätzold et al., in press). The potential extra benefits of integrating ecological and economic knowledge in models can be illustrated by an example of research published in Science. Ando et al. (1998) found that integrating economic costs (land prices) into ecologically based selection algorithms for conservation sites make for superior management recommendations. For identical conservation outputs, these management recommendations lead to cost savings of up to 80% compared to traditional, ecology based recommendations. Apart from Ando et al. (1998), only a few studies have explicitly used ecological–economic modelling to evaluate and design biodiversity management strategies. Their results demonstrate that ecological–economic modelling may in many cases provide valuable insights into the interaction between ecological and economic systems. Although its benefits are obvious and more and more research is done in this area (e.g., Johst et al., 2002; Baumgärtner, 2004; Perrings and Walker, 2004), ecological–economic modelling is still a far cry from being an established approach. Besides organisational and institutional difficulties of interdisciplinary research (e.g., Jakobsen et al., 2004; Rhoten, 2004), another important reason complicating the develop-
ment of ecological–economic modelling may be a lack of scientific reflection: it is generally not clear how to deal with the challenges that arise if economic and ecological knowledge is merged into ecological–economic models. To work at overcoming this shortcoming a workshop took place at the UFZ — Centre for Environmental Research in Leipzig, Germany in September 2004. The workshop centered on three themes: (I) Differences and common ground between ecological and economic models for biodiversity management. (II) Ideas and approaches used in the development of ecological–economic models and a discussion of their pros and cons. (III) Identification and discussion of existing barriers to ecological–economic modelling and means to overcome them. The articles in this special issue are based on presentations held at the workshop.
2.
Overview of the special issue
The first three papers relate to workshop theme (I). The article by Anders Skonhoft represents a “typical” economic modelling approach to analyse biodiversity management problems, the article by Andreas Huth and Britta Tietjen a “typical” ecological modelling approach. Both articles include an assessment of how modelling may be changed to better integrate knowledge from the other discipline, respectively. The article by Martin Drechsler, Volker Grimm, Jaroslav Myšiak and Frank Wätzold compares ecological and economic modelling approaches, showing that the models employed by Skonhoft and Huth and Tietjen indeed represent typical approaches of their disciplines. Skonhoft investigates conflicts over conservation and rural development in a developing country context with traditional simple bioeconomic models, where wildlife is modelled as biomass and where economy and ecology interact through harvesting and land-use change. Firstly, he investigates the conflicting interests of a park agency on the one hand and local people on the other hand in a setting where wildlife is considered valuable as well as harmful. The park agency benefits from wildlife due to tourism and safari hunting, while
204
EC O LO GIC A L E CO N O M ICS 6 2 ( 2 00 7 ) 2 0 3 –2 06
the local people also hunt wildlife, partly motivated by their need to reduce crop damage. Secondly, he addresses the issue that the conservation area and the area containing wildlife are not identical due to dispersal. Here, problems may arise because of hunting and possible habitat degradation outside the conservation area. Within his framework, he analyses how various factors influence conflicts between conservation and other activities, and also investigates how, and to what extent, harvesting and habitat deterioration in outside areas may spill over to conservation areas. Skonhoft argues that the strength of the stylized natural resource models he uses is that it clarifies some principles of wildlife conservation problems, thus helping to derive and understand simple policy implications. He emphasises that in more realistic, and hence more complex, models, the findings are often more difficult to interpret. However, he concedes that for some actual conservation problems, more complex models may be useful. As an example he cites models that represent wildlife not simply as (mere) biomass but also include species demography. Huth and Tietjen present two different forest models which can be used to analyse the impact of logging on tropical rain forests. The models differ in terms of their complexity. FORMIX3 is a rather complex individual based model which simulates the development of individual trees. The FORREG model is much simpler and uses differential equations to describe forest growth by making use of aggregated equations. The model describes the dynamics of the three different tree species groups in a tropical forest by tracking down their wood volume. The advantage of the FORREG model is that it is easier and faster to apply to a specific forest than the complex FORMIX3 model whose parameterisation needs time due to the variety of processes described by the model. However, the FORMIX3 model can be used to analyse more detailed effects of logging than the FORREG model, e.g., variations in tree species composition. Huth and Tietjen demonstrate the usefulness of their models by means of an analysis of the regeneration of logged forests (FORMIX3) and a comparison of two management strategies (FORREG). Huth and Tietjen point out that their ecological models cannot explain why a reduced-impact logging management strategy is not applied in practice although it seems to produce a more natural tree species composition and higher wood yield than conventional logging. They note that an extension of their models, also considering costs of different logging methods may be necessary for this purpose. They see the monetary evaluation of ecological values and, more generally, of ecosystem services of forests as another rewarding area of integrated research. Quantifying the impact of different logging strategies on ecosystem services would make it possible to determine optimal logging strategies. A comparison of the model approaches of Skonhoft and of Huth and Tietjen makes it clear that the models differ in terms of complexity and the level of generality. The economic model is simpler and much more general than both ecological models. In their paper, Martin Drechsler, Volker Grimm, Jaroslav Myšiak and Frank Wätzold take up the theme of differences in modelling approaches between economics and ecology. They refer to sixty models randomly selected from economic and ecological journals, comparing them on a number of criteria, such as type of model formulation and solution, model complexity, level of generality and consideration of phenomena like uncertainty,
dynamics and spatial heterogeneity. They find that the differences between the model approaches of Skonhoft and of Huth and Tietjen are indeed examples of typical differences of economists' and ecologists' approaches to modelling. Other differences are the extent and manner of how space, uncertainty and time are taken into account, and a greater variety of modelling approaches in ecology compared to economics. However, despite these differences Drechsler et al. are optimistic about the development of ecological–economic models because they also found common ground between ecological and economic models. Furthermore, among the sixty randomly chosen models they identified a significant number of models that may be classified as ecological–economic models. As a conclusion from their analysis, they emphasise that communication problems may arise if both economists and ecologists talk about “models” believing they mean the same but in fact having entirely different concepts in mind. The next two papers address workshop theme (II) and contain two examples of ecological–economic modelling that differ in terms of their purpose and structure. The model by Jana Verboom, Rob Alkemade, Jan Klijn, Marc J. Metzger and Rien Reijnen operates on a very large scale and provides an aid for EU policy makers to analyse the impact of socio-economic scenarios on European policy targets (e.g., the EU aim of halting the loss of biodiversity by 2010). Consequently, it neglects ecological and socio-economic interactions at small scales. The model by Martin F. Quaas, Stefan Baumgärtner, Christian Becker, Karin Frank and Birgit Müller focuses on the level of an individual farm, which allows to take multiple interactions between the decision behaviour and preferences of the farmer, uncertainties in the environment, and the dynamics of the managed resources into account. Due to the explicit consideration of these interactions the analysis contributes to a better conceptual understanding of the sustainable management of natural resources. Verboom et al. present results from the EURURALIS-project which analysed the effects of different scenarios of socioeconomic development on biodiversity in the European Union over a period of 25 years. They differentiate between four scenarios: Global economy, continental markets, global cooperation and regional communities. To analyse the effects of these scenarios on biodiversity they combine models based on general relationships between environmental factors and biodiversity loss with socio-economic, land-use and environmental models. The data derived are integrated into an interactive tool for policy makers to stimulate discussion about the long-term effects of policies on biodiversity. Verboom et al. emphasise that they had to make a substantial amount of simplifying assumptions for their models. Such simplifications, however, are justified by the large spatial scale of the project and the flexibility requirements of an interactive tool for policy making. Although the simplifications led Verboom et al. to a certain scepticism about the quantitative reliability of their predictions (e.g., “scenario A is 10% better than scenario B”), they are quite confident that their tool gives a realistic picture in terms of qualitative statements (“scenario A is better than scenario B”). The model by Quaas et al. analyses the management of a sheep farm in Namibia that applies so-called rotational resting (some proportion of paddocks is kept free of sheep). In contrast to common farming strategies, the proportion of resting paddocks is
EC O L O G IC A L E C O N O M IC S 6 2 ( 2 0 07 ) 20 3 –2 06
not fixed but depends on the actual rainfall conditions. Through this adaptive strategy, the model contains an important feedback loop between the ecological and economic system components. Due to its simplicity and abstractness, the predictive power of the model is limited. On the other hand the tight integration of ecological and economic dynamics allows an in-depth analysis of the consequences of feedback loops on the sustainable management of a natural resource under uncertainty. In particular, the relationship between risk-attitude of the resource manager and sustainability of resource use can be investigated. The authors show that due to the feedback loops, under certain circumstances riskaverse decision behaviour is fully sufficient for the long-term persistence of both income and natural resource. The papers by Claire W. Armstrong and by David Finnof, Jason F. Shogren, Brian Leung and David Lodge contribute to theme (III), highlighting the relevance of communication in order to overcome barriers between economic and ecological research. In the first part of her paper, Armstrong reviews the ecological, economic and ecological–economic literature on marine reserves in fisheries. She presents the views of ecologists and economists on various problems, such as discounting and conflict management. Due to different model assumptions (e.g., regarding the consideration of certain types of costs or ecological interactions), ecologists and economists often arrive at different conclusions regarding the suitability of reserves for marine biodiversity conservation in general and, e.g., the optimal selection of reserves in particular. Recently, however, communication between disciplines has been improving. A good example, as the authors emphasize, is the analysis of bycatch fisheries. Here ecological complexity and multispecies interactions that were previously the realm of ecological research gains relevance to economists as well. Taking up criticism voiced by ecologists that economists used over-simplified ecological models, in the second part of her paper Armstrong demonstrates with the help of a model for the effects of a marine reserve on fish stocks and yields how the outcome of an economic fisheries model changes when its ecological realism is improved. Although communication between disciplines may be regarded as a key to integrated, ecological–economic research it is by no means simple, as Finnof et al. point out. Based on the example of the decision maker's optimal response to uncertainty, the authors demonstrate how tricky the correct communication of concepts between ecology and economics can be. They present a model where the decision maker's objective is to minimise adverse economic impacts of an invading species. In each period of time the decision maker has to allocate resources between efforts to control an invasive population and efforts to reduce the risk of invasion (“prevention”). It turns out that, contrary to normal expectations, a riskaverse decision maker will invest relatively fewer resources into prevention than a risk-neutral decision maker. At first sight this seems to be at odds with the precautionary principle that advocates eliminating risks in the first place. The solution to this apparent contradiction is that precautionary behaviour as understood by ecologists is not the same as risk-averse behaviour as understood by economists. Instead, as the authors point out, it is more consistent with economists' notion of risk-neutrality. As in their model prevention leads to higher welfare, the authors recommend to “take a risk” when it comes to the management of invaders.
205
3. Conclusions for better ecological–economic modelling What conclusions regarding barriers to ecological–economic modelling and possibilities to overcome them can be drawn from the research presented in this special issue? In a recent essay Wätzold et al. (in press) identify three key challenges for successful ecological–economic modelling: (I) In-depth knowledge of the two disciplines, (II) adequate identification and framing of the problem, and (III) common understanding of modelling and scales. All three key challenges point to the importance of good communication between the disciplines of ecology and economics as a key element that is both a precondition and a result of addressing these challenges. The papers of this special issue confirm that communication is the decisive factor when it comes to integrating knowledge of the two disciplines. Four levels of communication may be distinguished within the development of ecological–economic models. (1) Communication about the relevant causes for the decline of biodiversity and natural resources, plus the factors to be considered when strategies to counteract this decline are developed and evaluated. Armstrong demonstrates the importance of this question in her review of the ecological, economic and ecological–economic literature on marine reserves. (2) Communication about appropriate modelling approaches and their pros and cons. The papers by Skonhoft, Huth and Tietjen, and Drechsler et al. show that ecologists and economists favour different approaches. The growing number of ecological–economic models in scientific literature, however, demonstrates that this problem can be overcome in principle. (3) As indicated above, Verboom et al. and Quaas et al. use different modelling approaches which reflect the different purposes behind the models (decision aid versus conceptual analysis). Models may be used for different purposes such as analysis, understanding, predictions, communication and decision aiding. As pointed out by Drechsler et al. economists and ecologists may use different modelling approaches because they have different modelling purposes in mind. Therefore, communicating a model's purpose can be expected to ease conflicts between ecologists and economists about the appropriate modelling approach. (4) Finally, ecologists and economists must ensure that they interpret each other's concepts correctly. Finnof et al. demonstrate that although precautionary behaviour and risk-averse behaviour (the one being a concept developed by ecologists and the other by economists) seem to mean the same thing, they are very different and misinterpretation can lead to unintended consequences in proposed management strategies. Communication is not only beneficial but also costly. With growing understanding between the disciplines, however, communication costs will probably decrease. Furthermore, as pointed out by Wätzold et al. (in press), the costs of not
206
EC O LO GIC A L E CO N O M ICS 6 2 ( 2 00 7 ) 2 0 3 –2 06
using integrated approaches – showing itself in underproductive and cost-ineffective management recommendations – are probably much greater.
Acknowledgements This special issue is based on presentations held at a workshop in Leipzig (Germany) in September 2004. The workshop was funded by the European Science Foundation (ESF) under its “Explorative Workshops” series (Proposal No. EW03-041, LESC), which is gratefully acknowledged. We would like to thank all the participants who contributed to the workshop and stimulated our thinking.
REFERENCES Ando, A., Camm, J., Polasky, S., Solow, A., 1998. Species distributions, land values, and efficient conservation. Science 279, 2126–2128. Baumgärtner, S., 2004. Optimal investment in multi-species protection: interacting species and ecosystem health. EcoHealth 1, 101–110. Burgman, M.A., Ferson, S., Akçakaya, H.R., 1993. Risk Assessment in Conservation Biology. Chapman and Hall, London. Grimm, V., Railsback, S.F., 2005. Individual-Based Modeling and Ecology. Princeton University Press, Princeton, N.J. 428 pp. Jakobsen, C.H., Hels, T., McLaughlin, W.J., 2004. Barriers and facilitators to integration among scientists in transdisciplinary landscape analyses: a cross-country comparison. Forest Policy and Economics 6, 15–31. Johst, K., Drechsler, M., Wätzold, F., 2002. An ecological–economic modelling procedure to design effective and efficient com-
pensation payments for the protection of species. Ecological Economics 41, 37–49. Perrings, C., Walker, B., 2004. Conservation in the optimal use of rangelands. Ecological Economics 49, 119–128. Rhoten, D., 2004. Interdisciplinary research: trend or transition? Items and Issues 5, 6–11. Shogren, J.F., Parkhurst, G.M., Settle, C., 2003. Integrating economics and ecology to protect nature on private lands: models, methods and mindsets. Environmental Science and Policy 6, 233–242. Sanchirico, J.N., Wilen, J.E., 1999. Bioeconomics of spatial exploitation in a patchy environment. Journal of Environmental Economics and Management 37, 129–150. Wätzold, F., Drechsler, M., Armstrong, C.W., Baumgärtner, S., Grimm, V., Huth, A., Perrings, C., Possingham, H.P., Shogren, J.F., Skonhoft, A., Verboom-Vasiljev, J., Wissel, C., in press. Ecological–economic modelling for biodiversity management: Potential, pitfalls, and prospects. Conservation Biology.
a
b
Martin Drechsler* UFZ Centre for Environmental Research Leipzig-Halle GmbH, Department of Ecological Modelling, Germany E-mail address: [email protected]. ⁎ Corresponding author. Frank Wätzoldb UFZ Centre for Environmental Research Leipzig-Halle GmbH, Department of Economics, Germany 28 April 2006 0921-8009/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2006.05.004
a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m
w w w. e l s e v i e r. c o m / l o c a t e / e c o l e c o n
EDITORIAL
Ecological–economic modelling for the sustainable use and conservation of biodiversity 1.
Introduction
Models are important tools in the development of management recommendations for the sustainable use and conservation of biodiversity (in short biodiversity management). They are used both in ecological and economic research. In ecology, models are used to analyse how biodiversity management affects the dynamics and functioning of ecosystems (Burgman et al., 1993; Grimm and Railsback, 2005). However, the usefulness of these models for policy advice is limited, because they do not address the socio-economic dimensions of the problem, including economic, institutional, and political aspects. These issues are actual topics of economic research and modelling (Shogren et al., 2003). Many economic models are, however, also deficient, when they contain oversimplified assumptions on the ecological effects of conservation or do not represent current ecological knowledge (cf. Sanchirico and Wilen, 1999). The observation that disciplinary models exhibit complementary limitations leads to the simple conclusion that it is beneficial to merge ecological and economic knowledge via ecological–economic models (Wätzold et al., in press). The potential extra benefits of integrating ecological and economic knowledge in models can be illustrated by an example of research published in Science. Ando et al. (1998) found that integrating economic costs (land prices) into ecologically based selection algorithms for conservation sites make for superior management recommendations. For identical conservation outputs, these management recommendations lead to cost savings of up to 80% compared to traditional, ecology based recommendations. Apart from Ando et al. (1998), only a few studies have explicitly used ecological–economic modelling to evaluate and design biodiversity management strategies. Their results demonstrate that ecological–economic modelling may in many cases provide valuable insights into the interaction between ecological and economic systems. Although its benefits are obvious and more and more research is done in this area (e.g., Johst et al., 2002; Baumgärtner, 2004; Perrings and Walker, 2004), ecological–economic modelling is still a far cry from being an established approach. Besides organisational and institutional difficulties of interdisciplinary research (e.g., Jakobsen et al., 2004; Rhoten, 2004), another important reason complicating the develop-
ment of ecological–economic modelling may be a lack of scientific reflection: it is generally not clear how to deal with the challenges that arise if economic and ecological knowledge is merged into ecological–economic models. To work at overcoming this shortcoming a workshop took place at the UFZ — Centre for Environmental Research in Leipzig, Germany in September 2004. The workshop centered on three themes: (I) Differences and common ground between ecological and economic models for biodiversity management. (II) Ideas and approaches used in the development of ecological–economic models and a discussion of their pros and cons. (III) Identification and discussion of existing barriers to ecological–economic modelling and means to overcome them. The articles in this special issue are based on presentations held at the workshop.
2.
Overview of the special issue
The first three papers relate to workshop theme (I). The article by Anders Skonhoft represents a “typical” economic modelling approach to analyse biodiversity management problems, the article by Andreas Huth and Britta Tietjen a “typical” ecological modelling approach. Both articles include an assessment of how modelling may be changed to better integrate knowledge from the other discipline, respectively. The article by Martin Drechsler, Volker Grimm, Jaroslav Myšiak and Frank Wätzold compares ecological and economic modelling approaches, showing that the models employed by Skonhoft and Huth and Tietjen indeed represent typical approaches of their disciplines. Skonhoft investigates conflicts over conservation and rural development in a developing country context with traditional simple bioeconomic models, where wildlife is modelled as biomass and where economy and ecology interact through harvesting and land-use change. Firstly, he investigates the conflicting interests of a park agency on the one hand and local people on the other hand in a setting where wildlife is considered valuable as well as harmful. The park agency benefits from wildlife due to tourism and safari hunting, while
204
EC O LO GIC A L E CO N O M ICS 6 2 ( 2 00 7 ) 2 0 3 –2 06
the local people also hunt wildlife, partly motivated by their need to reduce crop damage. Secondly, he addresses the issue that the conservation area and the area containing wildlife are not identical due to dispersal. Here, problems may arise because of hunting and possible habitat degradation outside the conservation area. Within his framework, he analyses how various factors influence conflicts between conservation and other activities, and also investigates how, and to what extent, harvesting and habitat deterioration in outside areas may spill over to conservation areas. Skonhoft argues that the strength of the stylized natural resource models he uses is that it clarifies some principles of wildlife conservation problems, thus helping to derive and understand simple policy implications. He emphasises that in more realistic, and hence more complex, models, the findings are often more difficult to interpret. However, he concedes that for some actual conservation problems, more complex models may be useful. As an example he cites models that represent wildlife not simply as (mere) biomass but also include species demography. Huth and Tietjen present two different forest models which can be used to analyse the impact of logging on tropical rain forests. The models differ in terms of their complexity. FORMIX3 is a rather complex individual based model which simulates the development of individual trees. The FORREG model is much simpler and uses differential equations to describe forest growth by making use of aggregated equations. The model describes the dynamics of the three different tree species groups in a tropical forest by tracking down their wood volume. The advantage of the FORREG model is that it is easier and faster to apply to a specific forest than the complex FORMIX3 model whose parameterisation needs time due to the variety of processes described by the model. However, the FORMIX3 model can be used to analyse more detailed effects of logging than the FORREG model, e.g., variations in tree species composition. Huth and Tietjen demonstrate the usefulness of their models by means of an analysis of the regeneration of logged forests (FORMIX3) and a comparison of two management strategies (FORREG). Huth and Tietjen point out that their ecological models cannot explain why a reduced-impact logging management strategy is not applied in practice although it seems to produce a more natural tree species composition and higher wood yield than conventional logging. They note that an extension of their models, also considering costs of different logging methods may be necessary for this purpose. They see the monetary evaluation of ecological values and, more generally, of ecosystem services of forests as another rewarding area of integrated research. Quantifying the impact of different logging strategies on ecosystem services would make it possible to determine optimal logging strategies. A comparison of the model approaches of Skonhoft and of Huth and Tietjen makes it clear that the models differ in terms of complexity and the level of generality. The economic model is simpler and much more general than both ecological models. In their paper, Martin Drechsler, Volker Grimm, Jaroslav Myšiak and Frank Wätzold take up the theme of differences in modelling approaches between economics and ecology. They refer to sixty models randomly selected from economic and ecological journals, comparing them on a number of criteria, such as type of model formulation and solution, model complexity, level of generality and consideration of phenomena like uncertainty,
dynamics and spatial heterogeneity. They find that the differences between the model approaches of Skonhoft and of Huth and Tietjen are indeed examples of typical differences of economists' and ecologists' approaches to modelling. Other differences are the extent and manner of how space, uncertainty and time are taken into account, and a greater variety of modelling approaches in ecology compared to economics. However, despite these differences Drechsler et al. are optimistic about the development of ecological–economic models because they also found common ground between ecological and economic models. Furthermore, among the sixty randomly chosen models they identified a significant number of models that may be classified as ecological–economic models. As a conclusion from their analysis, they emphasise that communication problems may arise if both economists and ecologists talk about “models” believing they mean the same but in fact having entirely different concepts in mind. The next two papers address workshop theme (II) and contain two examples of ecological–economic modelling that differ in terms of their purpose and structure. The model by Jana Verboom, Rob Alkemade, Jan Klijn, Marc J. Metzger and Rien Reijnen operates on a very large scale and provides an aid for EU policy makers to analyse the impact of socio-economic scenarios on European policy targets (e.g., the EU aim of halting the loss of biodiversity by 2010). Consequently, it neglects ecological and socio-economic interactions at small scales. The model by Martin F. Quaas, Stefan Baumgärtner, Christian Becker, Karin Frank and Birgit Müller focuses on the level of an individual farm, which allows to take multiple interactions between the decision behaviour and preferences of the farmer, uncertainties in the environment, and the dynamics of the managed resources into account. Due to the explicit consideration of these interactions the analysis contributes to a better conceptual understanding of the sustainable management of natural resources. Verboom et al. present results from the EURURALIS-project which analysed the effects of different scenarios of socioeconomic development on biodiversity in the European Union over a period of 25 years. They differentiate between four scenarios: Global economy, continental markets, global cooperation and regional communities. To analyse the effects of these scenarios on biodiversity they combine models based on general relationships between environmental factors and biodiversity loss with socio-economic, land-use and environmental models. The data derived are integrated into an interactive tool for policy makers to stimulate discussion about the long-term effects of policies on biodiversity. Verboom et al. emphasise that they had to make a substantial amount of simplifying assumptions for their models. Such simplifications, however, are justified by the large spatial scale of the project and the flexibility requirements of an interactive tool for policy making. Although the simplifications led Verboom et al. to a certain scepticism about the quantitative reliability of their predictions (e.g., “scenario A is 10% better than scenario B”), they are quite confident that their tool gives a realistic picture in terms of qualitative statements (“scenario A is better than scenario B”). The model by Quaas et al. analyses the management of a sheep farm in Namibia that applies so-called rotational resting (some proportion of paddocks is kept free of sheep). In contrast to common farming strategies, the proportion of resting paddocks is
EC O L O G IC A L E C O N O M IC S 6 2 ( 2 0 07 ) 20 3 –2 06
not fixed but depends on the actual rainfall conditions. Through this adaptive strategy, the model contains an important feedback loop between the ecological and economic system components. Due to its simplicity and abstractness, the predictive power of the model is limited. On the other hand the tight integration of ecological and economic dynamics allows an in-depth analysis of the consequences of feedback loops on the sustainable management of a natural resource under uncertainty. In particular, the relationship between risk-attitude of the resource manager and sustainability of resource use can be investigated. The authors show that due to the feedback loops, under certain circumstances riskaverse decision behaviour is fully sufficient for the long-term persistence of both income and natural resource. The papers by Claire W. Armstrong and by David Finnof, Jason F. Shogren, Brian Leung and David Lodge contribute to theme (III), highlighting the relevance of communication in order to overcome barriers between economic and ecological research. In the first part of her paper, Armstrong reviews the ecological, economic and ecological–economic literature on marine reserves in fisheries. She presents the views of ecologists and economists on various problems, such as discounting and conflict management. Due to different model assumptions (e.g., regarding the consideration of certain types of costs or ecological interactions), ecologists and economists often arrive at different conclusions regarding the suitability of reserves for marine biodiversity conservation in general and, e.g., the optimal selection of reserves in particular. Recently, however, communication between disciplines has been improving. A good example, as the authors emphasize, is the analysis of bycatch fisheries. Here ecological complexity and multispecies interactions that were previously the realm of ecological research gains relevance to economists as well. Taking up criticism voiced by ecologists that economists used over-simplified ecological models, in the second part of her paper Armstrong demonstrates with the help of a model for the effects of a marine reserve on fish stocks and yields how the outcome of an economic fisheries model changes when its ecological realism is improved. Although communication between disciplines may be regarded as a key to integrated, ecological–economic research it is by no means simple, as Finnof et al. point out. Based on the example of the decision maker's optimal response to uncertainty, the authors demonstrate how tricky the correct communication of concepts between ecology and economics can be. They present a model where the decision maker's objective is to minimise adverse economic impacts of an invading species. In each period of time the decision maker has to allocate resources between efforts to control an invasive population and efforts to reduce the risk of invasion (“prevention”). It turns out that, contrary to normal expectations, a riskaverse decision maker will invest relatively fewer resources into prevention than a risk-neutral decision maker. At first sight this seems to be at odds with the precautionary principle that advocates eliminating risks in the first place. The solution to this apparent contradiction is that precautionary behaviour as understood by ecologists is not the same as risk-averse behaviour as understood by economists. Instead, as the authors point out, it is more consistent with economists' notion of risk-neutrality. As in their model prevention leads to higher welfare, the authors recommend to “take a risk” when it comes to the management of invaders.
205
3. Conclusions for better ecological–economic modelling What conclusions regarding barriers to ecological–economic modelling and possibilities to overcome them can be drawn from the research presented in this special issue? In a recent essay Wätzold et al. (in press) identify three key challenges for successful ecological–economic modelling: (I) In-depth knowledge of the two disciplines, (II) adequate identification and framing of the problem, and (III) common understanding of modelling and scales. All three key challenges point to the importance of good communication between the disciplines of ecology and economics as a key element that is both a precondition and a result of addressing these challenges. The papers of this special issue confirm that communication is the decisive factor when it comes to integrating knowledge of the two disciplines. Four levels of communication may be distinguished within the development of ecological–economic models. (1) Communication about the relevant causes for the decline of biodiversity and natural resources, plus the factors to be considered when strategies to counteract this decline are developed and evaluated. Armstrong demonstrates the importance of this question in her review of the ecological, economic and ecological–economic literature on marine reserves. (2) Communication about appropriate modelling approaches and their pros and cons. The papers by Skonhoft, Huth and Tietjen, and Drechsler et al. show that ecologists and economists favour different approaches. The growing number of ecological–economic models in scientific literature, however, demonstrates that this problem can be overcome in principle. (3) As indicated above, Verboom et al. and Quaas et al. use different modelling approaches which reflect the different purposes behind the models (decision aid versus conceptual analysis). Models may be used for different purposes such as analysis, understanding, predictions, communication and decision aiding. As pointed out by Drechsler et al. economists and ecologists may use different modelling approaches because they have different modelling purposes in mind. Therefore, communicating a model's purpose can be expected to ease conflicts between ecologists and economists about the appropriate modelling approach. (4) Finally, ecologists and economists must ensure that they interpret each other's concepts correctly. Finnof et al. demonstrate that although precautionary behaviour and risk-averse behaviour (the one being a concept developed by ecologists and the other by economists) seem to mean the same thing, they are very different and misinterpretation can lead to unintended consequences in proposed management strategies. Communication is not only beneficial but also costly. With growing understanding between the disciplines, however, communication costs will probably decrease. Furthermore, as pointed out by Wätzold et al. (in press), the costs of not
206
EC O LO GIC A L E CO N O M ICS 6 2 ( 2 00 7 ) 2 0 3 –2 06
using integrated approaches – showing itself in underproductive and cost-ineffective management recommendations – are probably much greater.
Acknowledgements This special issue is based on presentations held at a workshop in Leipzig (Germany) in September 2004. The workshop was funded by the European Science Foundation (ESF) under its “Explorative Workshops” series (Proposal No. EW03-041, LESC), which is gratefully acknowledged. We would like to thank all the participants who contributed to the workshop and stimulated our thinking.
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Martin Drechsler* UFZ Centre for Environmental Research Leipzig-Halle GmbH, Department of Ecological Modelling, Germany E-mail address: [email protected]. ⁎ Corresponding author. Frank Wätzoldb UFZ Centre for Environmental Research Leipzig-Halle GmbH, Department of Economics, Germany 28 April 2006 0921-8009/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2006.05.004