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Recent problems in ecosystem theory - conclusions of the workshop
Ecological Modelling, 63 (1992)325-331 Elsevier SciencePublishersB.V., Amsterdam
325
Recent problems in ecosystem theory conclusions of the workshop F. Miiller a, W. Windhorst a and S.E. J~argensen b a Project Centre for Ecosystem Research, University of Kid, Schauenburger Str. 112, D 2300 Kiel 1, Germany b Royal Danish School of Pharmacy, University Park 2, DK 2900 Copenhagen, Denmark
At first glance the papers in this volume appear to represent a very wide and heterogeneous spectrum of approaches to ecosystem theory and modelling. But looking at the basic ideas, the essential problems, and the outstanding results it is possible to outline several unifying structures and complementary conclusions. Summarizing these aspects we refer to the questions mentioned in the introduction to this volume, and in addition to discussions that took place at the workshop and to statements that some of the authors sent us. One of the questions was directed towards the limitations o f ecosystem analysis a n d modelling. Obviously, one of the crucial problems ecologists have to face is the high complexity of ecosystems. which has to be reduced in order to make the object feasible. By means of abstraction the observer has to develop a conceptual model of reality which refers to his very specific aims. This abstraction defines the basic hypothesis of the investigation. Therefore, the investigator has to be aware of the fact that he himself has designed the system and that the coaclusions he draws in the first step of interpretatien are only valid for this abstracted object and not for reality itself (Breckling, 1992). As ecosystem ecologists try to establish general statements about natural interactions instead of descriptions of singular cases (Wissel, 1992), a careful validation of theoretical conclusions or models with measured field data is one of the most important means for controlling and handling complexity in an acceptable way. Strictly associated with these problems is the complexity o f models. Wissel discusses this point very intensively, stressing the disadvantages of
Correspondence to: F. Miiller, Project Centre for EcosystemResearch, Universityof Kiel, Schauenburger Str. 112, D 230(IKiel 1, Germany.
0304-3800/92/$05.00 © 1992 - ElsevierSciencePublishersB.V. All rights reserved
326
F. MULLER ET AL.
complex models. From our point of view the limits mentioned are generally accepted, while the real problem arises from the scale of observation: - - T h e optimal complexity of a model depends on its purpose: there are qualitative and quantitative differences between simple graphical models and models of global climatic change. Furthermore there is no contradiction between 'realistic models' and 'specific purposes', because the quality of models has to be evaluated by validation, and the final aim of each model should be a description and a better understanding of reality. - - F o r each model type and problem there is an optimal complexity. If it increases beyond a specific level, knowledge of the modeled system is not increased while the effectiveness decreases (Joergensen, 1986). --Therefore, today's complex models consist of small, compatible and interacting subunits based on a hierarchical distinction of the observed system. Another problem arising from the complexity of ecosystems is discussed by Breckling (1992). In his opinion, the uniqueness of ecosystems provides a high degree of uncertainty which can be minimized, but not eliminated completely. Therefore, procedures of how to handle this uncertainty have to be developed. One possibility for quantifying uncertainty is based on fuzzy set theory (uncertainty-factor, Salski, 1992). Refering to the problems of heterogeneities in ecosystems and landscapes and to thc representativeness of samples, ecologists should make use of the experience of geostatistics, because this science offers elaborate methods for quantifying the demanded uncertainties and for optimizing sampling designs (Turner and Gardner, 1991). Of course, these methods are based upon the assumption that using different spatial scales different requirements to the grain of the observation have to be taken into account. For that reason, implicit uncertainties arising from individual properties or behaviour can rarely be described, but the significance of this intra-system uniqueness is again dependent on the aims and scales of the observations: the swimming speed of a single fish will be of minor influence on the nutrient budget of the watershed it lives in. This does not at all mean that heterogeneities are unimportant. Wissel (1992a) and Jetschke (1992) show that heterogeneities can affect self-organization processes and that buffer capacities of ecosystems can increase with spatio-temporal patchiness. Joergensen (1992b) shows that ecosystems will receive more exergy by increasing heterogeneity in space and time, and it has to be considered that increasing heterogeneity implies an increasing number of organization levels. Thus, an interesting question is whether or not spatial and temporal heterogeneity can be looked upon as a "strategy" of ecosystems. On the other hand, Ulrich (1992) and Miiller (1992) postulate that temporal heterogeneities (variances of state variables), in particular, can be used as indicators for unstable
CONCLUSIONS
327
states. Last but not least, the subject of diversity properties has to be mentioned in this context. Heterogeneity, therefore, appears to be a very interesting field for further investigations (Kolasa and Picket, 1991). The above-mentioned uncertainties also lead to the question of whether prognoses about ecological processes are really possible or not. In our opinion they are, if a valid model is used to describe the system behaviour under conditions similar to the range of validation data and if quantitative results with a certain degree of accuracy are aspired. Difficulties arise when qualitative trends are to be predicted, when the forcing functions change rapidly, when local deviations are concerned, and when changes in the system structure or functional bifurcations appear during the temporal extent of the simulation. The most important shortcoming is the rigidity of models, the limitation of validity due to the exactly, mathematically defined, non-changeable structural fundamentals. Looking at the purposes of applied modelling, --prognoses of stability, --definitions of sustainable disturbance thresholds, or --evaluations of environmental planning alternatives, exactly this integration of transition states is demanded. Therefore, the coupling of simulations and expert systems and new concepts such as real structural modelling (Nielsen, 1992; Bossel, 1992) provide useful solutions. A general problem of these approaches is the necessary definition of goal functions (Table 1). Using them, we are in danger of imputing certain teleological properties or purposes to ecosystems, for which often no empirical evidence can be found. Thus the evaluation of a certain system state is based upon an hypothesis that is still under discussion. On the other hand, these goal functions enable us to combine ecological knowledge with Darwin's theory, gaining the possibility of integrating ecological selection processes on the ecosystem level. Real structure modelling, therefore, should as a first step be used to test the empirical validity of the goal function concept. When these hypotheses are proved, which is a very TABLE 1 Hypothetical goal functions of ecosystems Goal functions(Nielsen, 1992) Maximum power Maximum biomass Maximum entropy Ascendency Maximum exergy
Basic orientors (Bossel, 1992) Existence Effectiveness Freedom of action Security Adaptivity Attention to other systems
328
F. MULLER ET AL.
deserving task, especially because ecosystem theory and empirical practice have to be combined, the above-mentioned limits of prognosis can be overcome, and the applicability of models increases. What model types are available to answer these questions? In the papers of this volume different mathematical approaches (e.g., differential equation models, partial differential equation models, cellular automata, stochastic models, object-oriented models, fuzzy set models, semantical networks, modelling sys~tems, GIS°models), model classifications and various modelling techniques are presented (Wissel, 1992b; Breckling, 1992; Albrecht, 1992; Jetschke, 1992; Salski, 1992; Wenzel, 1992; Grimm et al., 1992; J/irgensen, 1992b; Mi~ller, 1992; Marcus, 1992; Bossel, 1992; Gaedke, 1992, Lenz and Haber, 1992). Each of these has specific advantages and shortcomings and has to be selected with reference to the purpose of the model and its scale. It should be noted that there is a broad range of purposes, from contributions to a better understanding of selected ecosystem processes or the analysis of interactions in more complex subsystems and patterns, to the test of different planning alternatives concerning landscapes. Thus the aims can be classified within a hierarchical framework, and the requirements concerning the accuracy of input parameters, the degrees of randomness and uncertainty, the complexity of the model, and the exactness of the model results, depend on the hierarchical levels the described processes operate on. If these factors are defined strictly, misunderstandings could be avoided and the tolerance of scientists modelling on different levels could be improved, a fact which might lead to better cooperation between the more reductionistic and the more holistically oriented ecologists. Another important requirement for better cooperation concerns ecological terminology. Unharmonized language hinders conversation, impedes scientific development and promotes misunderstanding (Jax et al., 1992; Grimm et al., 1992). As an example, Grimm et al. illustrate the terminological confusion by the "fact" that "the number of stability definitions to be found in the literature is limited only by the time spent on reading it. Jax et al. therefore recommend that in any investigation basic terms should be defined as explicitly and precisely as possible, taking the role of the observer and the a priori assumptions into account. Furthermore, the delimitations of the observed object and its scale have to be defined, as mentioned above. Grimm et al. present a good example of the necessary precision with an "ecological checklist", which should be applied when stability parameters are discussed (level of description, variable of interest, referential dynamics of this variable, disturbance, spatial scales, time scales). The importance of such strict definitions can be illustrated by the different "types of stability" described (e.g., Albrecht, 1992; Nielsen, 1992; Ulrich,
CONCLUSIONS
329
1992; Bossel, 1992; Mii!ler, 1992; Wissel, 1992; Lenz and Haber, 1992). This variety of approaches furthermore illustrates that there are close correlations between the spatio..temporal scales and the specific frequencies of disturbances and the scale the disturbed element or process operates on. Stability measures, therefore, have to take the hierarchical organization of ecosystems into account. This type of structure is a supposition for the existence of emergent properties, which are discussed in many papers. There is no doubt that they are not equivalent to non-understandable characteristics, and in discussing these properties ecologists should strictly avoid mysticism or incorrect holistic attitudes. Emergent properties arise from hierarchical organization, whereby on each level properties are generated wh~,:h are not obvious and deducible from the lower levels (elements, subsystems) only. As Wissel (1992b) states, they are consequences of self-organization processes. Although all contributors to this volume are convinced that emergent properties exist, none could show how they can be measured or evaluated. In our opinion the addressing of this question should be considered a very important future task for ecosystem theory, because emergent properties are the central qualities of the system approach. As pure ecological theoly does not provide us with sufficient information, different theoretical approaches are needed to capture the entire spectrum of emergent properties. In this volume, information theory, thermodynamics (Bossei, 1992; Jfrgensen, 1992a; Messer, 1992; Nielsen, 1992; Ulrich, 1992), hierarchy theory (Gaedke, 1992; Lenz and Haber, 1992; Miiller, 1992) and chaos theory (Marcus, 1992) are applied to ecological problem,3. Thermodynamics, in particular, can be used as a basically holistic approach as it deals with energy, which is a very important state variable in ecological systems. Furthermore, there are many linkages between thermodynamics, cybernetics, network theory and the concept of ascendency. Joergensen (1992b) shows that exergy is thus a bridge between ecology and thermodynamics including evolutionary processes. Because exergy captures some of the above-mentioned emergent properties it appears to be a good candidate for a goal function in structural dynamic modelling. A similar integrating property is provided by hierarchy theory. It can be successfully used as a tool for distinguishing organization levels, which ~s necessary if emergent properties have to be quantified. The theory enables us to understand the patterns of interactions and it helps in solving the diverse problems of scale, heterogeneity, and structural invariances (Miiller, 1992) inherent in ecological research. One of the most important qualities of the theory is its high compatibility with other approaches (O'Neill et al., 1986). Therefore, we should take advantage of it by applying goal functions such as exergy or ascendency to discrete subsystems of different scales,
330
F. MOLLER El' AL.
increasing the complexity of t h e system. This may help us test the abovementioned hypotheses and goal 'functions and may lead to rapid applications of the gained theoretical knowledge concerning environmental problems. Therefore, from our point of view the question of whether or not the different concepts can be unified must be answered positively, if unification means combination and integration. ,We should look for analogies and overlaps merging promising approaches to an integrating ecosystem theory. As a consequence, ecologists will have t o realize that their science is founded on an interdisciplinary theoretical base, and colleagues from other disciplines will have to face up to the fact that their results will be evaluated with respect to the Darwinian theory of natural selection. Thus far, many problems of ecosystem theory have been mentioned, but at least two are missing: (1) theory has to be combined with empirical measurements, and (2) theory has to contribute to the actual environmental problems. The papers of Ulrich (1992), Marcus (1992) and Gaedke (1992) can be taken to illustrate the first point. Gaedke's study of the biomass size distribution in Lake Constance demonstrates the general difficulty in biology of establishing strict rules. Furthermore, she insists on better cooperation between theoretical and practical ecologists. The acceptance of theoretical concepts will only be possible if they are rigorously tested with existing data sets. The paper of Ulrich shows that thi,~ is possible if long-term ecological research is combined with continuou~ theoretical reflection. On the other hand, ecology faces many practical environmental problems (Lenz and Haber, 1992) which seem to be far removed from theoretical and methodological discussions. Thus we should not be satisfied with the assumption that theory is necessary to improve the efficiency of applied research. It is essential to broaden the limits of models and get to the root of the problems of reality. REFERENCES Albrecht, K.-F., 1992. Problems of modelling and forecasting on the basis of phenomenological investigations. Ecol. Modelling, 63: 45-69. Bossei, H., 1992. Real-structure process description as the basis of understanding ecosystems and their development. Ecol. Modelling, 63: 261-276. Breekling, B., 1992. Uniqueness of ecosystems versus generalizability and predictability in ecology. Ecol. Modelling, 63: 13-27. Gaedke, U., 1992. Identifying ecosystem properties: a case study using plankton biomass size distribution. Ecol. Modelling, 63:277-298 Grimm, V., Schmidt, E. and Wissel, C., 1992. On the application of stability concepts in ecology. Ecol. Modelling, 63: 143-161. Jax, K., Zauke, G.-P. and Vareschi, E., 1992. Remarks on terminology and the description of ecological systems. Ecol. Modelling, 63: 133-141.
CONCLUSIONS
331
Jetschke, G., 1992. Stochastic population models and their relevance for the conservation of species. Ecol. Modelling, 63: 71-89. Joergensen, S.E., 1986. Fundamentals of Ecological Modelling. Elsevier Amsterdam. Joergensen, S.E., 1992a. Exergy and ecology. Ecol. Modelling, 63: 185-214. Joergensen, S.E., 1992b. A Pattern of Ecosystem Theories. Elsevier, Amsterdam. Kolasa, J. and Picket, S.T.A., 1991. Ecological Heterogeneity. Ecological Studies, 86. Springer-Verlag, Berlin. Lenz, R. and Haber, W., 1992. Approaches for the restoration of forest ecosystems in northeastern Bavaria. Ecol. Modelling, 63: 299-317. Markus, M., 1992. Are one-dimensional maps of any use ir~ ecology? Ecol. Modelling, 63: 243-259. Messer, J., 1992. Statistical mechanics of terrestrial ecosystems. Ecol. Modelling, 63: 319-324. Miiller, F., 1992. Hierarchical approaches to ecosystem theory. Ecol. Modelling, 63: 215-242. Nielsen, S.N., 1992. Strategies for structural-dynamic modelling. Ecol. Modelling, 63: 91-101. O'Neill, R.V., De Angelis, D.L., Waide, J.B. and Allen T.H.F., 1986. A hierarchical Concept of Ecosystems. Princeton University Press, Princeton. Salski, A., 1992. Fuzzy knowledge-based models in ecological research. Ecol. Modelling, 63: 103-112. Turner, M.G. and Gardner, R.H., 1991. Quantitative Methods in Landscape Ecology. Ecological Studies, 82. Springer-Verlag, Berlin. Ulrich, B., 1992. Forest ecosystem theory based on material balance. Ecol. Modelling, 63: 163-183. Wenzel, V., 1992. Semantics and syntax elements of a unique calculus for modelling of complex ecological systems. Ecol. Modelling, 63: 113-131. Wissel, C., 1992a. Aims and limits of ecological modelling exemplified by island theory. Ecol. Modelling, 63: 1-12. Wissel, C., 1992b. Modelling the mosaic cycle of a Middle European beech forest. Ecol. Modelling~ 63: 29-43.
325
Recent problems in ecosystem theory conclusions of the workshop F. Miiller a, W. Windhorst a and S.E. J~argensen b a Project Centre for Ecosystem Research, University of Kid, Schauenburger Str. 112, D 2300 Kiel 1, Germany b Royal Danish School of Pharmacy, University Park 2, DK 2900 Copenhagen, Denmark
At first glance the papers in this volume appear to represent a very wide and heterogeneous spectrum of approaches to ecosystem theory and modelling. But looking at the basic ideas, the essential problems, and the outstanding results it is possible to outline several unifying structures and complementary conclusions. Summarizing these aspects we refer to the questions mentioned in the introduction to this volume, and in addition to discussions that took place at the workshop and to statements that some of the authors sent us. One of the questions was directed towards the limitations o f ecosystem analysis a n d modelling. Obviously, one of the crucial problems ecologists have to face is the high complexity of ecosystems. which has to be reduced in order to make the object feasible. By means of abstraction the observer has to develop a conceptual model of reality which refers to his very specific aims. This abstraction defines the basic hypothesis of the investigation. Therefore, the investigator has to be aware of the fact that he himself has designed the system and that the coaclusions he draws in the first step of interpretatien are only valid for this abstracted object and not for reality itself (Breckling, 1992). As ecosystem ecologists try to establish general statements about natural interactions instead of descriptions of singular cases (Wissel, 1992), a careful validation of theoretical conclusions or models with measured field data is one of the most important means for controlling and handling complexity in an acceptable way. Strictly associated with these problems is the complexity o f models. Wissel discusses this point very intensively, stressing the disadvantages of
Correspondence to: F. Miiller, Project Centre for EcosystemResearch, Universityof Kiel, Schauenburger Str. 112, D 230(IKiel 1, Germany.
0304-3800/92/$05.00 © 1992 - ElsevierSciencePublishersB.V. All rights reserved
326
F. MULLER ET AL.
complex models. From our point of view the limits mentioned are generally accepted, while the real problem arises from the scale of observation: - - T h e optimal complexity of a model depends on its purpose: there are qualitative and quantitative differences between simple graphical models and models of global climatic change. Furthermore there is no contradiction between 'realistic models' and 'specific purposes', because the quality of models has to be evaluated by validation, and the final aim of each model should be a description and a better understanding of reality. - - F o r each model type and problem there is an optimal complexity. If it increases beyond a specific level, knowledge of the modeled system is not increased while the effectiveness decreases (Joergensen, 1986). --Therefore, today's complex models consist of small, compatible and interacting subunits based on a hierarchical distinction of the observed system. Another problem arising from the complexity of ecosystems is discussed by Breckling (1992). In his opinion, the uniqueness of ecosystems provides a high degree of uncertainty which can be minimized, but not eliminated completely. Therefore, procedures of how to handle this uncertainty have to be developed. One possibility for quantifying uncertainty is based on fuzzy set theory (uncertainty-factor, Salski, 1992). Refering to the problems of heterogeneities in ecosystems and landscapes and to thc representativeness of samples, ecologists should make use of the experience of geostatistics, because this science offers elaborate methods for quantifying the demanded uncertainties and for optimizing sampling designs (Turner and Gardner, 1991). Of course, these methods are based upon the assumption that using different spatial scales different requirements to the grain of the observation have to be taken into account. For that reason, implicit uncertainties arising from individual properties or behaviour can rarely be described, but the significance of this intra-system uniqueness is again dependent on the aims and scales of the observations: the swimming speed of a single fish will be of minor influence on the nutrient budget of the watershed it lives in. This does not at all mean that heterogeneities are unimportant. Wissel (1992a) and Jetschke (1992) show that heterogeneities can affect self-organization processes and that buffer capacities of ecosystems can increase with spatio-temporal patchiness. Joergensen (1992b) shows that ecosystems will receive more exergy by increasing heterogeneity in space and time, and it has to be considered that increasing heterogeneity implies an increasing number of organization levels. Thus, an interesting question is whether or not spatial and temporal heterogeneity can be looked upon as a "strategy" of ecosystems. On the other hand, Ulrich (1992) and Miiller (1992) postulate that temporal heterogeneities (variances of state variables), in particular, can be used as indicators for unstable
CONCLUSIONS
327
states. Last but not least, the subject of diversity properties has to be mentioned in this context. Heterogeneity, therefore, appears to be a very interesting field for further investigations (Kolasa and Picket, 1991). The above-mentioned uncertainties also lead to the question of whether prognoses about ecological processes are really possible or not. In our opinion they are, if a valid model is used to describe the system behaviour under conditions similar to the range of validation data and if quantitative results with a certain degree of accuracy are aspired. Difficulties arise when qualitative trends are to be predicted, when the forcing functions change rapidly, when local deviations are concerned, and when changes in the system structure or functional bifurcations appear during the temporal extent of the simulation. The most important shortcoming is the rigidity of models, the limitation of validity due to the exactly, mathematically defined, non-changeable structural fundamentals. Looking at the purposes of applied modelling, --prognoses of stability, --definitions of sustainable disturbance thresholds, or --evaluations of environmental planning alternatives, exactly this integration of transition states is demanded. Therefore, the coupling of simulations and expert systems and new concepts such as real structural modelling (Nielsen, 1992; Bossel, 1992) provide useful solutions. A general problem of these approaches is the necessary definition of goal functions (Table 1). Using them, we are in danger of imputing certain teleological properties or purposes to ecosystems, for which often no empirical evidence can be found. Thus the evaluation of a certain system state is based upon an hypothesis that is still under discussion. On the other hand, these goal functions enable us to combine ecological knowledge with Darwin's theory, gaining the possibility of integrating ecological selection processes on the ecosystem level. Real structure modelling, therefore, should as a first step be used to test the empirical validity of the goal function concept. When these hypotheses are proved, which is a very TABLE 1 Hypothetical goal functions of ecosystems Goal functions(Nielsen, 1992) Maximum power Maximum biomass Maximum entropy Ascendency Maximum exergy
Basic orientors (Bossel, 1992) Existence Effectiveness Freedom of action Security Adaptivity Attention to other systems
328
F. MULLER ET AL.
deserving task, especially because ecosystem theory and empirical practice have to be combined, the above-mentioned limits of prognosis can be overcome, and the applicability of models increases. What model types are available to answer these questions? In the papers of this volume different mathematical approaches (e.g., differential equation models, partial differential equation models, cellular automata, stochastic models, object-oriented models, fuzzy set models, semantical networks, modelling sys~tems, GIS°models), model classifications and various modelling techniques are presented (Wissel, 1992b; Breckling, 1992; Albrecht, 1992; Jetschke, 1992; Salski, 1992; Wenzel, 1992; Grimm et al., 1992; J/irgensen, 1992b; Mi~ller, 1992; Marcus, 1992; Bossel, 1992; Gaedke, 1992, Lenz and Haber, 1992). Each of these has specific advantages and shortcomings and has to be selected with reference to the purpose of the model and its scale. It should be noted that there is a broad range of purposes, from contributions to a better understanding of selected ecosystem processes or the analysis of interactions in more complex subsystems and patterns, to the test of different planning alternatives concerning landscapes. Thus the aims can be classified within a hierarchical framework, and the requirements concerning the accuracy of input parameters, the degrees of randomness and uncertainty, the complexity of the model, and the exactness of the model results, depend on the hierarchical levels the described processes operate on. If these factors are defined strictly, misunderstandings could be avoided and the tolerance of scientists modelling on different levels could be improved, a fact which might lead to better cooperation between the more reductionistic and the more holistically oriented ecologists. Another important requirement for better cooperation concerns ecological terminology. Unharmonized language hinders conversation, impedes scientific development and promotes misunderstanding (Jax et al., 1992; Grimm et al., 1992). As an example, Grimm et al. illustrate the terminological confusion by the "fact" that "the number of stability definitions to be found in the literature is limited only by the time spent on reading it. Jax et al. therefore recommend that in any investigation basic terms should be defined as explicitly and precisely as possible, taking the role of the observer and the a priori assumptions into account. Furthermore, the delimitations of the observed object and its scale have to be defined, as mentioned above. Grimm et al. present a good example of the necessary precision with an "ecological checklist", which should be applied when stability parameters are discussed (level of description, variable of interest, referential dynamics of this variable, disturbance, spatial scales, time scales). The importance of such strict definitions can be illustrated by the different "types of stability" described (e.g., Albrecht, 1992; Nielsen, 1992; Ulrich,
CONCLUSIONS
329
1992; Bossel, 1992; Mii!ler, 1992; Wissel, 1992; Lenz and Haber, 1992). This variety of approaches furthermore illustrates that there are close correlations between the spatio..temporal scales and the specific frequencies of disturbances and the scale the disturbed element or process operates on. Stability measures, therefore, have to take the hierarchical organization of ecosystems into account. This type of structure is a supposition for the existence of emergent properties, which are discussed in many papers. There is no doubt that they are not equivalent to non-understandable characteristics, and in discussing these properties ecologists should strictly avoid mysticism or incorrect holistic attitudes. Emergent properties arise from hierarchical organization, whereby on each level properties are generated wh~,:h are not obvious and deducible from the lower levels (elements, subsystems) only. As Wissel (1992b) states, they are consequences of self-organization processes. Although all contributors to this volume are convinced that emergent properties exist, none could show how they can be measured or evaluated. In our opinion the addressing of this question should be considered a very important future task for ecosystem theory, because emergent properties are the central qualities of the system approach. As pure ecological theoly does not provide us with sufficient information, different theoretical approaches are needed to capture the entire spectrum of emergent properties. In this volume, information theory, thermodynamics (Bossei, 1992; Jfrgensen, 1992a; Messer, 1992; Nielsen, 1992; Ulrich, 1992), hierarchy theory (Gaedke, 1992; Lenz and Haber, 1992; Miiller, 1992) and chaos theory (Marcus, 1992) are applied to ecological problem,3. Thermodynamics, in particular, can be used as a basically holistic approach as it deals with energy, which is a very important state variable in ecological systems. Furthermore, there are many linkages between thermodynamics, cybernetics, network theory and the concept of ascendency. Joergensen (1992b) shows that exergy is thus a bridge between ecology and thermodynamics including evolutionary processes. Because exergy captures some of the above-mentioned emergent properties it appears to be a good candidate for a goal function in structural dynamic modelling. A similar integrating property is provided by hierarchy theory. It can be successfully used as a tool for distinguishing organization levels, which ~s necessary if emergent properties have to be quantified. The theory enables us to understand the patterns of interactions and it helps in solving the diverse problems of scale, heterogeneity, and structural invariances (Miiller, 1992) inherent in ecological research. One of the most important qualities of the theory is its high compatibility with other approaches (O'Neill et al., 1986). Therefore, we should take advantage of it by applying goal functions such as exergy or ascendency to discrete subsystems of different scales,
330
F. MOLLER El' AL.
increasing the complexity of t h e system. This may help us test the abovementioned hypotheses and goal 'functions and may lead to rapid applications of the gained theoretical knowledge concerning environmental problems. Therefore, from our point of view the question of whether or not the different concepts can be unified must be answered positively, if unification means combination and integration. ,We should look for analogies and overlaps merging promising approaches to an integrating ecosystem theory. As a consequence, ecologists will have t o realize that their science is founded on an interdisciplinary theoretical base, and colleagues from other disciplines will have to face up to the fact that their results will be evaluated with respect to the Darwinian theory of natural selection. Thus far, many problems of ecosystem theory have been mentioned, but at least two are missing: (1) theory has to be combined with empirical measurements, and (2) theory has to contribute to the actual environmental problems. The papers of Ulrich (1992), Marcus (1992) and Gaedke (1992) can be taken to illustrate the first point. Gaedke's study of the biomass size distribution in Lake Constance demonstrates the general difficulty in biology of establishing strict rules. Furthermore, she insists on better cooperation between theoretical and practical ecologists. The acceptance of theoretical concepts will only be possible if they are rigorously tested with existing data sets. The paper of Ulrich shows that thi,~ is possible if long-term ecological research is combined with continuou~ theoretical reflection. On the other hand, ecology faces many practical environmental problems (Lenz and Haber, 1992) which seem to be far removed from theoretical and methodological discussions. Thus we should not be satisfied with the assumption that theory is necessary to improve the efficiency of applied research. It is essential to broaden the limits of models and get to the root of the problems of reality. REFERENCES Albrecht, K.-F., 1992. Problems of modelling and forecasting on the basis of phenomenological investigations. Ecol. Modelling, 63: 45-69. Bossei, H., 1992. Real-structure process description as the basis of understanding ecosystems and their development. Ecol. Modelling, 63: 261-276. Breekling, B., 1992. Uniqueness of ecosystems versus generalizability and predictability in ecology. Ecol. Modelling, 63: 13-27. Gaedke, U., 1992. Identifying ecosystem properties: a case study using plankton biomass size distribution. Ecol. Modelling, 63:277-298 Grimm, V., Schmidt, E. and Wissel, C., 1992. On the application of stability concepts in ecology. Ecol. Modelling, 63: 143-161. Jax, K., Zauke, G.-P. and Vareschi, E., 1992. Remarks on terminology and the description of ecological systems. Ecol. Modelling, 63: 133-141.
CONCLUSIONS
331
Jetschke, G., 1992. Stochastic population models and their relevance for the conservation of species. Ecol. Modelling, 63: 71-89. Joergensen, S.E., 1986. Fundamentals of Ecological Modelling. Elsevier Amsterdam. Joergensen, S.E., 1992a. Exergy and ecology. Ecol. Modelling, 63: 185-214. Joergensen, S.E., 1992b. A Pattern of Ecosystem Theories. Elsevier, Amsterdam. Kolasa, J. and Picket, S.T.A., 1991. Ecological Heterogeneity. Ecological Studies, 86. Springer-Verlag, Berlin. Lenz, R. and Haber, W., 1992. Approaches for the restoration of forest ecosystems in northeastern Bavaria. Ecol. Modelling, 63: 299-317. Markus, M., 1992. Are one-dimensional maps of any use ir~ ecology? Ecol. Modelling, 63: 243-259. Messer, J., 1992. Statistical mechanics of terrestrial ecosystems. Ecol. Modelling, 63: 319-324. Miiller, F., 1992. Hierarchical approaches to ecosystem theory. Ecol. Modelling, 63: 215-242. Nielsen, S.N., 1992. Strategies for structural-dynamic modelling. Ecol. Modelling, 63: 91-101. O'Neill, R.V., De Angelis, D.L., Waide, J.B. and Allen T.H.F., 1986. A hierarchical Concept of Ecosystems. Princeton University Press, Princeton. Salski, A., 1992. Fuzzy knowledge-based models in ecological research. Ecol. Modelling, 63: 103-112. Turner, M.G. and Gardner, R.H., 1991. Quantitative Methods in Landscape Ecology. Ecological Studies, 82. Springer-Verlag, Berlin. Ulrich, B., 1992. Forest ecosystem theory based on material balance. Ecol. Modelling, 63: 163-183. Wenzel, V., 1992. Semantics and syntax elements of a unique calculus for modelling of complex ecological systems. Ecol. Modelling, 63: 113-131. Wissel, C., 1992a. Aims and limits of ecological modelling exemplified by island theory. Ecol. Modelling, 63: 1-12. Wissel, C., 1992b. Modelling the mosaic cycle of a Middle European beech forest. Ecol. Modelling~ 63: 29-43.