Addressing sustainability concerns in Mediterranean forest ecosystem management

Transcription

1 Addressing sustainability concerns in Mediterranean forest ecosystem management José F. Loff 1, Marc Palahí 2, André O. Falcão 3, Helena Martins 1, José G. Borges 1 1 Centro de Estudos Florestais, Departamento de Engenharia Florestal, Instituto Superior de Agronomia, Tapada da Ajuda, Lisboa, Portugal 2 MEDFOREX Program, Forest Technology Centre of Catalonia, Passeig Lluis Companys 23, Barcelona, Spain 3 Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa Summary Multiple Criteria Spatial Decision Support Systems (MC-SDSS) may help address the complexity of Mediterranean forest ecosystems management and promote its sustainability. This statement is demonstrated by showing the application of MC- SDSS to cork oak ecosystems in Portugal and simulated Catalan landscapes in Spain. Results show that MC-SDSS effectively integrate ecosystem data, silviculture and growth-and-yield models, giving a range of management alternatives to the forest owners, managers and policy makers, while allowing a better understanding of the management problems. However, their application is limited by the amount of quantitative information available. Ill-defined and data-poor forest management problems cannot be conveniently addressed by these systems. Recommendations emphasize the need of 1) prioritizing efforts towards the satisfaction of data and model requirements for the development of effective MC- SDSS; and 2) promoting their adaptation to a broader range of applications. A call is also made for the integration of participatory methods to promote public participation and generate knowledge that can be used in management planning. Introduction Mediterranean ecosystem management encompasses multiple economic, social and ecological objectives. Addressing sustainability concerns in Mediterranean forest ecosystems management is thus a complex task that requires the integration of diverse information, models and methods. This is the case with the two Mediterranean typical forest management problems addressed in this communication: the management of cork oak stands and the management of pine stands while taking the fire risk into account. Both are common to a large area of Mediterranean forest and are of great relevance to local and regional forest economy and development. The aim of this communication is to demonstrate how Multiple Criteria Spatial Decision Support Systems (MC-SDSS) may help address the complexity of such forest management problems. A Decision Support System (DSS) is an interactive and flexible set of computer-based tools that integrate the insights of the decision maker with information processing capabilities in order to improve the quality of decisionmaking 1, 2, 3. A real world cork oak ecosystem test case - Serra de Grândola in Southern Portugal - and a typical Catalan landscape case study in Spain will be used to illustrate applications of MC-SDSS as decision support tools in forest management. Cork oak (Quercus suber, L.) stands are characteristic of the Mediterranean basin and its main product, the cork, is one of the most valuable products at the Portuguese forest sector. In most cases, the structure of the cork oak stands allows the exploitation of other forest species and the existence of complementary agro-pastoral activities. The study site is located in Serra de Grândola, a management area with about 18,600 ha in Southern Portugal. This area has been classified as a CORINE Biotope (C-108) and has been integrated in the set of sites proposed to be part of the 1

2 EU network Natura The main cover types are dominated by cork oak and umbrella pines (Pinus pinea, L). The management of the cork oak stands was used to illustrate the application of a scalable and interactive MC-SDSS as a decision support tool for optimizing cork and cork oak timber production. The system developed presents a modular structure, comprising a management information system (MIS), a prescription simulator, a set of models to address specific ecosystem management problems, all of which are front-ended by a graphical user interface. The MIS stores spatial and aspatial data from the management areas, containing physical, floral, faunal and socioeconomic information, which will be used by the prescription simulator. Combining the data stored in the MIS with the adequate growth and yield models, the prescription simulator plays a key role in the DSS, as it allows the automated generation of all management options available to the decision maker. It also encompasses the definition of the debarking cycle for each tree in the land unit and the thinning regime, considering three debarking models. The user is allowed to choose between three land unit density target levels and the number of years between harvest entries. The thinning regime is then simulated over a 30-year planning horizon by interpreting the inventory data and the selected target level, allowing thinnings to take place only in debarking years to avoid cork production losses. The problem is outputted as a linear programming matrix for subsequent optimization by both a linear programming solver and a simulated annealing algorithm. At the Spanish case study, the need of taking fire risk into account when planning for sustainable forestry is addressed, both at the stand and landscape level. About fires sweep through an average of hectares (1% of the forest area) of Mediterranean forest each year, causing enormous economic and ecological damage as well as loss of human life 4. The first part of the study discussed how the risk of fire could be addressed as part of a stand management optimisation problem for Pinus sylvestris stands. A simulation-optimisation system, SPINE, was used to examine the effect of risk of fire on the optimal stand management schedule when maximising soil expectation value (SEV), in which the simulation sub-system included a deterministic stand growth and yield simulator based on individual-tree growth and mortality models. The simulator was modified to include stochastic fire occurrence 5. In most calculations, the probability of fire was assumed to be constant over the whole rotation, but an analysis was also conducted in which the probability depended on management and the stage of stand development based on a fire probability model 6. For the landscape management optimisation problem, landscapes were simulated and spatial metrics were measured to represent the relative arrangement and connectivity with respect to fire risk 7 (see Annex 1). These spatial metrics represent the overall fire risk and safeness of a forest landscape. As most landscape metrics were spatial, the computational complexity of the planning problem called for the use of heuristic search techniques 8. A model was first used to predict a fire resistance index for every management schedule of each stand 7, and then various landscape metrics, computed from the stands' fire resistance indices, were included in optimization problems that were solved with heuristic methods. A MC- SDSS called Monte 9 was used to perform all the calculations and as a platform for the integration of the different simulators. Main Findings 2

3 Cork oak ecosystem management The results suggested that the cork oak productive potential was underutilized and with a careful choice of treatments it was possible to increase the cork production without compromising sustainability or incurring into excessive expenditure. Moreover, the comparison of the optimal net present value (NPV) of an unconstrained problem with the NPVs of the linear problem and simulated annealing solutions, show the trade-offs between the sustainable cork flow restriction stated on the problem definition (see Annex 2). The inclusion of GIS data in the Management Information System allows a landscape-wide analysis of the treatment schedule. As it would be expected, the simulated annealing solution has a more evenly distributed schedule, whilst the unconstrained financial optimum scenario proposes a more concentrated debarking schedule, with simultaneous harvest entries on the northern and western areas, which have higher altitudes, steeper slopes and lower productivity, as a mean to reduce costs. Integration of forest fires in forestry decision-making Results at the stand level showed that increased fire probability caused reductions in the optimal rotation length, and also decreased soil expectation value. The effect of fire risk on the timing and intensity of thinnings was less systematic when a constant fire risk was assumed. However, when fire risk depended on stand structure, increased risk level led to earlier and heavier low thinnings (see Annex 3). At the landscape level, the results pointed out that the smoothest and least fragmented landscape, with respect to fire resistance, is obtained when the amount and dispersion of high-risk stands are minimized (maximization of the MMin metric, see Annex 4). Conclusions Overall results show that MC-SDSS represent valuable decision support tools by providing a technological platform for the integration of information, models and methods necessary to optimize forest management objectives. The cork oak case study clearly shows the applicability of such systems for aiding cork producers in either maximizing their income or maintaining a sustainable product flow. The system s flexible design allows the definition of diverse management problems that can be solved by different methods, which solutions can be more easily analysed and compared with the usage of GIS elements. Since it has a modular structure, the system can easily be expanded to encompass timber growth or wildlife models, hence broadening its usability on other ecosystems. Other problemsolving methods can also be used, such as dynamic programming or landscape-wide heuristics. The second case study emphasises the importance of taking fire risk into account in forest management planning, both at the landscape and at the stand level, since it might lead to a different spatial and time allocation of thinnings and rotations. Bearing this in mind, the MC-SDSS presented for fire risk management proved particularly useful, as it allows: - the study of the impact of fire risk on rotation length and thinning intensity and timing through simulations. Thus, it is an adequate decision-support tool that forest managers can use to optimize stand management according to specific fire probabilities; - the identification of the optimal combination of stand management alternatives to maximize or minimize a given landscape metric, and consequently minimize fire risk and maximize safeness at the landscape level; 3

4 - the assessment, for a certain area, of the optimal fuel fragmentation and the fire resistance of a "break area". For example, it was possible to study the effect of fuel fragmentation at the two landscapes and conclude that the effectiveness of this fragmentation was site specific. In relation to the "break area", it was possible to conclude that its fire resistance has to be high to make it efficient; - the assessment of the impact of using several spatial metrics in the costeffectiveness of the results and identify the metrics and related special stand treatments that lead to both low-risk and cheap solutions. Recommendations The increasing complexity and specificity of forest management at Mediterranean forest ecosystem can no longer be addressed empirically. Effective management requires decision-support tools to help stakeholders and policy makers deciding upon reliable data and integrated information. Since predictions and scenario analysis are of crucial importance in modern forest planning, these tools should have, as core components, simulation models, based on advanced growth and yield functions that can produce detailed predictions of stand development under different management schedules. This communication makes a call for the usefulness and relevance of MC-SDSS as technological platforms for the effective integration of data, information and models, providing simulations and outputs that decision makers can use to guide their decisions. Transparency and easy access to information ought to be the assumptions underlying the development of MC-SDSSs' architecture, namely in what concerns the articulation among databases, simulators and interfaces. Equally important to an adequate architecture, are the data, models and methods that are integrated within the systems. The collection of data and the development of models should be considered a priority. Reliable inventory and modelling data across Mediterranean countries should be gathered in a coordinated way to respond to this main need. The same applies to the development and test of models and methods, which would benefit from coordinated research programmes and activities undertaken by Mediterranean research institutions. Special concern should be given to yield and growth models for even- and uneven-aged, and pure and mixed stands; models for predicting the production of non-wood forest products (eg. cork and mushrooms); and methods to address ecological value and social forest benefits. Both models and methods should be test at different areas and scales. The quality of the data and information included in the SI is also determinant for the success of the MC-SDSS applications discussed previously. Ribeiro et al. 10 stressed the fact that low quality data might undermine the effectiveness of decisionmaking. Moreover, "the lack of information in a usable form and inefficient and/or untimely transfer of data to appropriate users have been barriers to utilizing the best available knowledge in Mediterranean ecosystem management and to identifying priorities for information generation through research". The authors defended the need of specific approaches for data integration and the development of data standards, i.e. a framework for data quality. In reality, the existence of adequate databases and good quality data to support sustainable forest management is not very common at Mediterranean forest ecosystems, characterized by its complex and multifunctional nature. An adequate framework for data quality might help the identification of data/information needs that should be considered a priority in research programmes and forest inventories. However, the collection of more data will not be the solution for all ill-defined forest management problems. Some of these problems are too complex to be quantitatively 4

5 and objectively described. There might be also unknown components of the problem and unpredictable cause-and-effect relationships. This is particularly evident in forest systems with high social, cultural and ecological value. Ultimately, it might be too expensive to collect the data or it would take too long to develop a reasonable database. In these situations, capturing and representing local and expert knowledge might be the best possible way to obtain information to support forest planning 11,12. Traditional participatory methods 13, 14 can be used for knowledge elicitation among stakeholders and experts with the advantage of promoting interdisciplinary approaches to forest planning. The representation of knowledge is the conceptual approach to the development of innovative planning and decision support tools such as the Knowledge-Based Systems (KBS) and the Multi-Agent Systems (MAS). Reynolds 15 developed a KBS, the Ecosystem Management Decision Support System (EMDS), which translates expert knowledge into logical models. EMDS has been used, for example, to assess watershed condition 16 and to evaluate forest ecosystem sustainability 17. MAS applications have been developed to represent knowledge and reasoning of several heterogeneous agents that need to be accommodated in forest planning in a collective way 18, 19. The integration of knowledge elicitation and representation in the planning process can be in line with the increasingly advocated participatory forest planning. For that, stakeholders should not act only as information providers and should participate throughout the planning process, namely in the value-based stages: problem identification, specification of goals and priorities, and evaluation of management alternatives 20. In terms of the underlying decision-making process, this direct involvement of stakeholders matches the concept of participatory modelling. This means that stakeholders contribute knowledge for the development of decision support models representing the management problem. Participatory modelling of management problems is the methodological approach adopted, for example, by Softsystems. Applications of these systems developed for natural resources management have used modelling methods such as cognitive mapping 21, qualitative system dynamics 22, and fuzzy cognitive mapping 23. Soft systems approaches give emphasis to defining the most relevant factors, perspectives and issues that have to be taken into account, and in designing strategies upon which the problem can be better understood and the decision process better guided 24,25. Facilitation and structuring are the two key aspects of soft systems. Facilitation aims to provide the adequate environment for guiding discussions. Structuring, on the other hand, concerns the process with which the management problem is organized in a manner that stakeholders or participants can understand, and hence, ultimately participate in the planning and decision-making processes. The knowledge-based systems and the soft-systems approaches to natural resources management point to further innovations in MC-SDSS that would enable their effective use in ill-defined forest management problems. MC-SDSS can be seen as technological platforms with potential to facilitate the integration of participatory techniques for knowledge elicitation, with tools for participatory modelling. The interfaces, the IS and the modules used for scenario analysis and modelling can also be adapted to promote live and web-based discussion among stakeholders and to facilitate participation. Besides enhancing public participation, such adaptations would facilitate the access to information and, therefore, more effective decisions. Acknowledgements The fourth author wishes to acknowledge the support provided by Fundação para a Ciência e Tecnologia and the European Social Fund within the III FP. 5

6 Annexes Annexe 1 Landscape metrics used Six landscape metrics were analyzed as means of affecting the spatial distribution of fire resistance in the landscape: (1) Mean fire resistance (MR) - the area-weighted mean of the resistance indices of stands. It is a non-spatial metric and gives reference for analyzing the remaining metrics, which are spatial; (2) Share of good-good boundary (G-G) - refers to the configuration of low risk stands within the landscape. Stand boundaries are bisected into two groups, separating two similar or dissimilar stands, according to a threshold value of 0.5 of RES. Then, the proportion of good-good stand boundary (RES > 0.5 for both stands) of the total boundary length is calculated. The idea for using the share of good-good stand boundary is that when such a metric is maximized, low-risk stands tend to be connected; creating continuous breaks in the landscape that can act as fuel breaks. A high share of good good boundary indicates a good connectivity of low risk stands within the landscape, which can result in reduction of the risk and the size of forest fires; (3) Share of good-bad boundary (G-B) - refers to the percentage of a boundary between stands of high (RES > 0.5) and low (RES 0.5) fire resistance. The idea for using the share of good-bad stand boundary is to disconnect fire-risky stands, thereby increasing fragmentation with respect to fire risk.; (4) Mean difference (MD) - it measures the average change in resistance at the compartment boundary, giving the overall dissimilarity of neighbouring stands at the forest level. Maximizing the mean difference creates a forest landscape of maximally different neighbour stands with respect to their risk of fire resulting in a fragmented landscape with respect to fire risk. Compared to the G-B metric, MD does not use any threshold value for fire resistance; (5) Mean of neighbourhood minima (MMin) - calculated as follows: first, every stand receives the lowest RES value of the stands in its neighbourhood (RESmin). The neighbourhood includes the stand itself and all other stands having common boundary with it. Second, calculation of the mean of the RESmin values of stands produces the MMin metric. The rationale behind this metric is that a risky stand (low resistance) makes all its neighbours also risky. When the metric is maximized, fire-risky stands will be placed so that they affect the other stands minimally. MMin reduces the amount and dispersion of high-risk stands (reducing potential ignition points). (6) Mean of neighbourhood maxima (MMax) - calculated by first giving to each stand the resistance value of the most resistant stand of the neighbourhood (RESmax, the stand itself is included in the neighbourhood). Then the mean of the RESmax values of all stands in the forest is calculated. Maximizing the mean of neighbourhood maxima creates a forest landscape where as many stands as possible will have at least one low-risk neighbour. The reason for using the MMax metric is the results obtained with percolation models (Loehle 2004). Percolation models have shown that treated stands (with low risk of fire), even if they are not directly connected, can have a good effect, acting as fuel breaks as well as protecting themselves (Loehle 2004). 6

7 Annexe 2 Cork flows associated with the unconstrained net present value maximization and the simulated annealing solutions. Annexe 3 Development of stand basal area in the optimal management schedule for site 24 m, two thinnings, and 2 % discounting rate, when the prediction of the fire risk model is multiplied by 0, 0.5, 1, or Basal area (m 2 ha -1 ) Stand age (ye ars ) Annexe 4 Value of fire resistance index in 2034 in two simulated forest landscapes; Tarragona Random (A) and Uniform forest (B) when one of the landscape metrics was maximized with a mean resistance target of (Tarragona Random) or (Uniform forest) in Dark tones imply low resistance. The maximized landscape metric is: top left, Mean Resistance; top right, Good-Good boundary; middle left, 7

8 Good-Bad boundary; middle right, Mean Difference; bottom left, Mean of neighbourhood Minima; bottom right, Mean of neighbourhood Maxima (annexe 2). A B Annexe 5 References [1] Gadow, K., Information: a key resource in plantation forestry. Sud-Afrikaanse Tydskrift vir Wetenskap. 85: (1989). [2] Turban, E, Decision Support and Expert Systems. Macmillan Publishing Company. New York [3] Varma, V. K., Ferguson, I., Wild, I, Decision support system for the sustainable forest management, For. Ecol. Manage.128(2000).49-5 [4] Vélez, R.. (2002). Causes of forest fires in the Mediterranean Basin. EFI Proceedings 45: [5] González, J.R., Pukkala, T., Palahí, M. 2005a. Optimising the management of Pinus sylvestris L. stand under risk of fire in Catalonia (north-east of Spain). Annals of Forest Science 62: [6] González J.-R., Palahí M., Trasobares A. and Pukkala, T. 2005b. A fire risk model for forest stands in Catalonia. Forthcoming to Annals of Forest Science. [7] González, J.R., Palahí, M., and Pukkala, T. 2005c. Integrating fire risk considerations in forest management planning in Spain a landscape level perspective. Landscape Ecology 20 (8), [8] Borges, J., Hoganson, H. and Falcão, A. (2002). Heuristics in multi-objective forest management. In Pukkala, T. (Ed.), Multi-objective forest planning, Kluwer Academic Publishers, Netherlands, Pp [9] Palahí, M., Pukkala, T., Pérez, E., Trasobares, A Herramientas de soporte a la decision en la planificación forestal. Revista forestall MONTES. N-78-IV Trimestre. Page 40. 8

9 [10] Ribeiro, R. P., J. G. Borges and V. Oliveira. (2004). A framework for data quality for Mediterranean sustainable ecosystem management. Annals of Forest Science 61: [11] Thomson, A.J. (2000) Elicitation and representation of traditional ecological knowledge, for use in forest management. Computers and Electronics in Agriculture 27, [12] Sicat, R.S., Carranza, E.J. and Nidumolu, U.B. (2005) Fuzzy modelling of farmer s knowledge for land suitability classification. Agricultural Systems (in press). [13] Chambers, R. and Guijt, I. (1995) PRA five years later Where are we now? Forest, Trees and People 26/27, [14] Selener, D. (1997) Participatory Action Research and social change. The Cornell Participatory Action Research Network. Cornell University Press, New York. [15] Reynolds, K.M. (1999) NetWeaver for EMDS version 1.0 User Guide: a knowledge base development system. Gen. Tech. Rep. PNW-GTR-471. Portland, OR: US Department of Agriculture, Forest Service, Pacific Northwest Research Station. [16] Reynolds, K.M., Jensen, M., Andreasen, J. and Goodman, I. (2000) Knowledgebased assessment of watershed condition. Computers and Electronics in Agriculture 27, [17] Reynolds, K.M., Johnson, K.N. and Gordon, S.N. (2003) The science/policy interface in logic-based evaluation of forest ecosystem sustainability. Forest Policy and Economics 5, [18] Bousquet, F. and Le Page, C. (2004) Multi-agent simulations and ecosystem management : a review. Ecological Modelling 176, [19] Purnomo, H., Mendoza, G.A., Prabhu, R. and Yasmi, Y. (2005) Developing multi-stakeholder forest management scenarios: a multi-agent systems simulation approach applied in Indonesia. Forest Policy and Economics (in press). [20] Kangas, J.; Loikkanen, T.; Pukkala, T. and Pykäläinen, J. (1996). A participatory approach to tactical forest planning. Acta Forestalia Fennica 251: [21] Mendoza, G.A. and Prabhu, R. (2003) Qualitative multi-criteria approaches to assessing indicators of sustainable forest resource management. Forest Ecology and Management 174, [22] Purnomo, H., Mendoza, G.A. and Prabhu, R. (2004) Model for collaborative planning of community-managed resources based on qualitative soft systems approach. Journal of Tropical Forest Science 16(1), [23] Özesmi, U. and Özesmi, S.L. (2004) Ecological models based on people s knowledge: a multi-step fuzzy cognitive mapping approach. Ecological Modelling 176, [24] Checkland, P.B. (1981) Systems Thinking, Systems Practice. John Wiley and Sons, Chichester. [25] Rosenhead, J. (1989) Rational analysis of a problematic world. John Wiley and Sons, New York. Acknowledges This study has been co-financed by the Ministerio de Educación y Ciéncia de España through the project AGL E. 9