12 Decision Support for Ecosystem Management and ... · gently needed so that federal land managers...

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12Decision Support forEcosystem Management andEcological AssessmentsH. Michael Rauscher and Walter D. Potter

12.1 Introduction

In the face of mounting confrontation and after al-most 20 years of increasingly contentious publicunhappiness with the management of NationalForests, the USDA Forest Service officially adopt-ed ecosystem management as a land managementparadigm (Overbay, 1992). Other federal forestland management agencies, such as the USDI Bu-reau of Land Management, the USDI National ParkService, the USDI Fish and Wildlife Service, theUSDC NOAA, and the Environmental ProtectionAgency, have also made the commitment to adoptecosystem management principles (GovernmentAccounting Office, 1994). Ecosystem managementrepresents different things to different people. Atthe heart of the ecosystem management paradigmlies a shift in emphasis away from sustaining yieldsof products toward sustaining the ecosystems thatprovide these products (Thomas, 1995; Rauscher,1999). The ecosystem management paradigm rep-resents the latest attempt, in a century-long strug-gle between resource users and resource preservers,to find a sensible middle ground between ensuringthe necessary long-term protection of the environ-ment while protecting the right of an ever-growingpopulation to use its natural resources to maintainand improve human life (Chase, 1995; Taylor,1998). As the concept of ecosystem managementevolves, debates over definitions, fundamental

The authors thank Don Nute, Geneho Kim, and ShanyinLiu of the University of Georgia Artificial IntelligenceCenter for .their many years of hard work and wonderfulcooperation in our long-term effort to build good deci-sion support systems. We also thank Mark Twery andthe entire NED development team for their hard workand great ideas.

principles, and policy implications will probablycontinue and shape the new paradigm in ways notyet discernible.

The ecosystem management paradigm wasadopted quickly. No formal studies were conductedto identify the consequences of the changes ush-ered in by this new approach, nor was any well-documented, widely accepted, organized method-ology developed for its implementation (Thomas,1997). Today, ecosystem management remains pri-marily a philosophical concept for dealing withlarger spatial scales, longer time frames, and the re-quirement that management decisions must be so-cially acceptable, economically feasible, and eco-logically sustainable. Because the definition andfundamental principles that make up the ecosystemmanagement paradigm have not yet been resolvedand widely accepted, the challenge is to build thephilosophical concept of ecosystem managementinto an explicitly defined, operationally practicalmethodology (Wear et al., 1996; Thomas, 1997).Effective ecosystem management processes are ur-gently needed so that federal land managers canbetter accommodate the continuing rapid change insocietal perspectives and goals (Bormann et al.,1993).

Ecosystem management represents a shift fromsimple to complex definitions of the ecosystemsthat we manage (Kohm and Franklin, 1997). It willrequire the development of effective, multiobjec-tive decision support systems to (1) assist individ-uals and groups in their decision-making processes;(2) support, rather than replace, the judgment of thedecision makers; and (3) improve the quality,reproducibility, and explicability of decisionprocesses (Janssen, 1992; Larsen et al., 1997;Reynolds et al., 1999). The complexity of envi-ronmental dynamics over time and space, over-

12.2 The Decision-making Environment 163

whelming amounts of data, information, andknowledge in different forms and qualities, andmultiple, often conflicting, management goals vir-tually guarantee that few individuals or groups ofpeople can consistently make good decisions with-out powerful decision support tools (Janssen, 1992).

Both the ecosystem and the management sub-systems of ecosystem management are part of aninterlocking, nested hierarchy (Bonnicksen, 1991;Forest Ecosystem Management and AssessmentTeam, 1993; Kaufmann et al., 1994). Local man-agement of ecosystems occurs within and is influ-enced by national and international social-economic-political systems. Similarly, local ecosys-tems operate within and are influenced by largerbiophysical systems, such as ecoregions or biomes.Ecosystem management can and should occur atmany scales: global-international, biome-national,ecoregion-multistate, or forest landscape-NationalForest (GAO, 1994, p. 62). At the regional, na-tional, and international scales, the ecosystem man-agement decision process should render the mosaicof environmental issues manageable by (1) identi-fying and labeling the issues, (2) defining the prob-lems, and (3) identifying who is causing the prob-lems, who has responsibility for solving theproblems, who are the stakeholders associated withthe problems, and who will pay for finding and im-plementing solutions (Hannigan, 1995). The deci-sion process at this macro scale should also coor-dinate solution efforts and supervise social ,andecological system sustainability (Tonn et al., 1998).Decision support systems that operate on the mul-tistate and national scale have been developed andtested in Europe and can serve as illustrations ofwhat is needed for United States federal forest man-agement (Van den Berg, 1996). There is as muchwork to be done in ecosystem management at thelarger scales as there is at the local scale. At pres-ent, no one agency, committee, or other organizedbody in the United States manages ecosystems atthe scale suggested by Bonnicksen (1991) andTonn et al. (1998). It is not obvious that our soci-ety has addressed the need to manage ecosystemsat the biome-national and ecoregional-multistatescale to cope with the complex environmental prob-lems that we have created for ourselves at thesescales (Caldwell, 1996). It seems reasonable thatwe should try.

For the purposes of this chapter, we considerthree spatial scales for which clear, precise, practi-cal ecosystem management processes are needed:(1) the regional assessment scale, (2) the forestlandscape management scale, and (3) the projectimplementation scale. These three scales are con-

nected by complex information linkages that makeit difficult to treat them separately. Indeed, deci-sion support systems (DSS) have been developedto operate at each of these scales, with special at-tention given to what is needed from the higherscale and what needs to be supplied‘to the lowerone. This chapter reviews the state of ecosystemmanagement decision support across all threescales. Although most of the examples are drawnfrom experiences with USDA Forest Service re-search and management, the principles discussedare generally applicable to all federal land man-agement agencies.

12.2 The Decision-makingEnvironment

The decision-making environment consists of thesocial, economic, political, and legal context inwhich any agency charged with ecosystem man-agement operates. Ecosystem management itself iscomposed of two parts: an ecological subsystemand a management subsystem (Figure 12.1) (Bon-nicksen, 199 1). The ecological subsystem containsphysical or conceptual objects such as trees, birds,deer, rivers, smells, sights, and sounds. Each ob-ject can be a resource if it has positive value in theminds of people or a pest if it has negative value.Otherwise, it is value neutral (Behan, 1997). A crit-ical feature of this view is that, as goals change,objects can change in status from resource to valueneutral or even to pest (Bonnicksen, 1991). For ex-ample, the white-tailed deer, once regarded as asought-after resource, is now considered a pest insome forest ecosystems of the eastern UnitedStates. People need to be considered as part of thecommunity of organisms that inhabit, use, or di-rectly influence an ecosystem (Behan, 1997). Thuspeople, in their role as users, are part of the eco-logical subsystem, like trees, soil, and wildlife.

The management subsystem is defined as mak-ing decisions about and controlling ecosystems toachieve desired ends. ,People in the managementrole participate in ecosystem management in a verydifferent manner.‘They are the risk takers, the set-ters of objectives, the judges of value, the substi-utters, in other words, the decision makers. It is use-ful to keep this distinction clearly in mind.Normally, many people participate in the manage-ment subprocess of ecosystem management. Thespecific values, goals, and constraints that charac-terize public preferences and needs may be identi-fied through a group negotiation process involving

164 Decision Support for Ecosystem Management and Ecological Assessments

Ecosystem Management Decision Environment

t (Goals, Values, Constraints)

FIGURE 12.1. Decision environment for eco-system management.

Ecosystem Management Organization andDecision-making Processes

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(Social, Economic, Political, and Legal Context)

a variety of stakeholders and management decisionmakers (Figure 12.1). Management decision mak-ers organize and lead the group negotiation process;they must ensure that the resultant goals are so-cially acceptable, legal, economically feasible, andecologically sustainable. If ecosystem managementdecision support systems (EM-DSS) are available,all participants need to be able to rely on them ata reasonable level of confidence for relevant infor-mation and analyses. This group negotiationprocess is probably the most difficult part ofecosystem management (Bormann et al., 1993).

Most forest managers, administrators, and sci-entists are much more familiar with the structureand function of the ecological subsystem than withthe management subsystem. This state of affairs isindicative of where we have put our attention andenergy in the past. One new message for ecosys-tem management is that forest managers, adminis-trators, and scientists need to rapidly redress thisimbalance.

While biophysical scientists at the national andinternational scale struggle to understand local, re-gional, and global environmental systems, socialand institutional scientists must struggle to under-stand how to sustain societal systems that will pro-tect the ability of humans and nature to coevolve(Bormann et al., 1993; Tonn et al., 1998). Thusdefining and understanding the nature of sustain-able societies and the nature of sustainable ecosys-tems are equally important. Tonn and White (1996)describe sustainable societies as wise, participative,tolerant, protective of human rights, spiritual, col-laborative, achievement oriented, supportive of sta-ble communities, able to make decisions under un-

certainty, and able to learn over time. One of thedefining characteristics of a society, will be how ef-fectively it manages to sustain both itself and itsecosystems. Even a cursory review of history re-veals numerous extinct civilizations that did notsuccessfully sustain both society and ecosystem(Toynbee, 1946).

12.2.1 Major F’qments of theManagement Subsystem

The study of the management subsystem must in-clude understanding the dynamics of public pref-erences, conflict management and resolution, andcost evaluation and containment as it relates toecosystem management. Defining and understand-ing stakeh.>lders and their preferences is an impor-tant par: of ecosystem management (Gz&x!.1997). Stakeholder and general public preferencesare volatile and sensitive to manipulation throughthe control of information transmitted through pub-lic media (Montgomery, 1993; Smith, 1997). Un-derstanding the dynamics of social preferences andhow they can be influenced over both the short andlong term is a vital part of the ecosystem manage-ment process. Ecosystem management processesand the institutions that use them must be able lodetect and accommodate rapid, and sometimes rad-ical, changes in public preferences (Kohm andFranklin, 1997).

Successful social conflict management is as im-portant as understanding stakeholder preferencedynamics. Currently, “the dominant means of sei-tling public land disputes have been either litiga-

12.2 The Decision-making Environment 165

tion or quasi-judicial administrative appeals. Suchcontentious methods of handling disputes expendmuch goodwill, energy, time, and money. Thesemethods produce winners and losers, may leavefundamental differences unresolved, and poten-tially please few or none of the parties” (Daniels etal., 1993, p. 347). Decision makers need a funda-mental understanding of the nature of environ-mental conflicts and disputes and how to use con-flict-positive dispute management techniqueseffectively (Daniels et al., 1993). New approachesto managing the social debate surrounding ecosys-tem management, such as alternative dispute reso-lution (ADR) techniques (Floyd et al., 1996),should be evaluated, taught, and used. Adaptivemanagement techniques are as applicable to themanagement side of ecosystem management asthey are to the ecosystem side. They could be usedto suggest a series of operational experiments thatstudy actual public participation and conflict man-agement activities to quickly determine what worksand what does not (Daniels et al., 1993; Shindlerand Neburka, 1997).

The ability of federal land managers to avoidgridlock is heavily dependent on stakeholder will-ingness to negotiate and ultimately agree on thegoals for ecosystem management (Bormann et al.,1993). Unfortunately, people sometimes have pref-erences based on core values that are so strong andso conflicting that no solution is acceptable (Smith,1997). To avoid societal gridlock, we must designand implement robust strategies that encourage vol-untary conflict resolution among contentious stake-holders and explore other options leading toward asettlement if voluntary resolution is impossible.Such options might include binding arbitration, anagreed-upon delay in order to improve our data andknowledge about the ecosystem, or various otherI’orms of conflict resolution.

Ecosystem management cost evaluation is a crit-ical area for economists to study. As a general rule,iucreases in problem complexity increase the cost01’ finding satisfactory solutions (Klein and Meth-lie, 1990). Ecosystem management should accom-modate limits on time, expertise, and moneyt Smith, 1997), because sustainable forest manage-rucnt is impossible if there are unsustainable socialund economic costs (Craig, 1996). Documentationof costs should be prepared and made public, be-c:tuse few people know or appreciate the costs ofcl’lbrts to solve complex ecosystem managementproblems. For example, the USDA Forest Servicehits spent approximately $2 billion, equal to 16%tmnually of the entire National Forest system bud-get- on planning since the National Forest Man-

agement Act was passed in 1976 (Behan, 1990).The additional costs of implementing ecosystemmanagement prescriptions and monitoring andevaluating the results have not yet been estimated.Are we willing or able to marshal the funding toimplement ecosystem management in a way thatwill ensure that federal forest managers can com-ply with the law and satisfy public preferences? Theamount of money that could be spent on ecosys-tem management nationally may be extremelylarge, and identifying clear benefits may be diffi-cult (Oliver et al., 1993).

In the last century of federal forestland manage-ment, timber harvesting has largely paid for multiple-use management activities. Many forecast that thelevel of timber harvesting under ecosystem man-agement will greatly decline, while the cost ofecosystem management will greatly increase. Un-til managers evaluate the true costs and benefits, itwill be difficult to determine whether the public iswilling to pay for ecosystem management pro-grams. In any case, a new and rational means ofcapital resource allocation will be required to fundthe ecosystem management process adopted (Sam-ple, 1990; Kennedy and Quigley, 1993; Oliver etal., 1993; Dombeck, 1997). Refusing to fundecosystem management and opting for the “donothing” alternative is likely to result in unaccept-able future conditions. “Plant and animal speciesdo not stop growing, dying, and burning; andfloods, fires, and windstorms do not stop when allmanagement is suspended” (Botkin, 1990). Naturedoes not appear to care, either about threatened andendangered species or about humans. People careand people must define goodness and badness. Na-ture will not do it for us. “Nature in the twenty-firstcentury will be a nature that we make; the questionis the degree to which this molding will be inten-tional or unintentional, desirable or undesirable”(Botkin, 1990). Making the nature that we wantmay be expensive. A good understanding of eco-logical economics will help society make rationalchoices.

12.2.2 Regional Ecological AssessmentsViewed as a DSS

Recently, regional ecological assessments havebeen used to describe the large-scale context forecosystem management and therefore can be con-sidered decision support tools (Figure 12.1). Re-gional assessments have been large, collaborativeinteragency efforts, often with public stakeholderparticipation, that have taken 2 to 5 years and sev-


era1 millions of dollars to finish. The objectives forintegrated ecological assessments are to provide (1)a description of current and historic composition,structure, and function of ecosystems; (2) a de-scription of the biotic (including human) and abi-otic processes that contributed to the developmentof the current ecosystem conditions; and (3) a de-scription of probable future scenarios that might ex-ist under different types of management strategies(Jensen et al., 1998). For examples of regional as-sessments, see the case studies provided in thisguidebook.

Currently, precisely how these regional assess-ments fit into the ecosystem management processis unclear. One alternative would be to view re-gional assessments as DSS for ecosystem manage-ment at the ecoregion-multistate scale. In this ca-pacity, the current objectives for assessments focusentirely too much on the ecosystem component ofecosystem management. The Southern Appala-chian Assessment (SAMAB, 1996), for example,examines the social and economic activities of peo-ple within the region, but only in their roles asecosystem members and users. The role of peopleas managers of ecosystems, including their role asmanagers of social-economic-political systems inthe region, is largely ignored. To correct this defi-ciency, another list of objectives for regional as-sessments might include the following: (1) identifya set of regional scale goals and desired future con-ditions and compare these to the current conditions;(2) identify regional stakeholders, their preferencesand values, and how they compare to the generalpublic in the region; (3) identify the legal and po-litical climate within which ecosystem manage-ment must function; (4) identify the regional costsand who will bear them, as well as the regionalgains and who will reap them; and (5) identify themajor problems, who is responsible for solvingthem, and who has supervisory responsibility tomonitor progress and assure that a satisfactory so-lution is eventually reached.

12.3 Ecosystem ManagementProcesses

The decision-making environment determines thegoals, values, and constraints for the organization.Organizational policy then translates the mandatesof the decision-making environment into specificdecision-making processes. A decision-makingprocess is a method or procedure that guides man-agers through a series of tasks, from problem iden-

tification and analysis to alternative design and fi-nally alternative selection (Mintzberg et al., 1976;Clemen, 1996). Ideally, decision support systemsshould not be developed until the ecosystem man-agement decision-making processes that they are tosupport have been articulated. In reality, both thedecision processes and the software systems neededto support them are evolving simultaneously, eachhelping to refine the other.

First-generation ecosystem management pro-cesses have evolved from two sources: (1) acade-mia, where several ecosystem management pro-cesses have been described at a general, conceptuallevel and their macrolevel structures and functionshave been identified; and (2) federal forest man-agers at the field level, where numerous, local ad-hoc processes have been developed and tested un-der fire. The academic, high-level descriptions ofecosystem management processes do not supplyadequate details to guide the development of deci-sion support systems; also, they are theoretical,lacking adequate field testing to determine howthey work in practice. The local, ad-hoc ecosystemmanagement processes are too numerous for ef-fective software-based decision support (approxi-mately 400 ranger districts in the U.S. National For-est System each have their own process; otherfederal agencies have hundreds of additional fieldorganizations), and few, if any, have been studiedand described formally so that similarities and dif-ferences can be identified. Moreover, no particularprocess(es) have been widely accepted and imple-mented in federal forest management. We shoulddevote as much creative attention to devising goodecosystem management decision processes as wedo to assuring the quality of the decisions them-selves (Ticknor, 1993). In this section, the majorelements of a generic ecosystem managementprocess are identified based on a synthesis of theliterature.

Adaptive management, a continuing process ofplanning, monitoring, evaluating, and adjustingmanagement methods (Bormann et al., 1993:FEMAT, 1993; Lee, 1993), provides a frameworkfor describing a generic management process. Theusefulness of adaptive management as an ecosys-tem management process is being field tested bythe Forest Service in the Northwest and in other re-gions of the country (Shindler et al., 1996). De-scribed at the most general level, adaptive man-agement consists of four activities, plan-act-monifor-evahate, linked to each other in a networkof relationships (Figure 12.2). At each cycle, theresults of the evaluation activity are fed back to theplanning activity so that adaptive learning can take

12.3 Ecosystem Management Processes 167

ProblemIdentification

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Alternative Altematlve Autiori.&ion FWJRE 12.2. Adaptive managementDevelopment Selection to Implement process for ecosystem management.

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Revised Goals\

New Knowledge 4

New Inventory Data /”

Newy Technology/

PLAN

MONITOR

place. Without adding further detail to this defini-tion, almost any management activity could erro-neously be labeled adaptive management. In real-ity, adaptive management is a well-described,detailed, formally rigorous, and scientifically de-fensible management-by-experiment system (Wal-ters and Holling, 1990). Baskerville (1985) pre-scribes a nine-step process for implementingadaptive management correctly. Adaptive manage-ment requires that a series of steps be followed foreach of the four major activities described.

12.3.1 PlanThe Mintzberg et al. (1976) planning process canbe viewed as a detailed description of the planningstage of the adaptive management process (Figure

ProblemIdentification

AlternativeDevelopment

12.3). Janssen (1992) argued that the planning stageof any decision process generally would need to besome variant of the Mintzberg et al. (1976) method.The planning stage consists of four steps: (1) prob-lem identification, including goal selection, (2) al-ternative development, (3) alternative selection,and (4) authorization to implement the selected al-ternative (Figures 12.2 and 12.3). Each of these ma-jor steps can be decomposed into one or morephases (Janssen, 1992).

The problem identification step consists of twophases: (la) recognition: identifying opportunities,problems, and crises, launching the decisionprocess; and ( 1 b) diagnosis: exploring the differentaspects of the problem situation, identifying thegoals, and deciding how to approach the problem.If the diagnosis phase, step lb, is unnecessary, it

AlternativeSelection

Authorizationto Implement

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ACT .

Feedback Loops

RGURE 12.3. Detailed view of the planning activity in the adaptive management process for ecosystem management.


can be skipped (Figure 12.3). The next step, alter-native development, has three phases: (2a) search:f inding previously designed and tes ted solut ions tothe entire problem or to any of its parts; (2b) de-sign: developing new al ternat ives; and (2~) screen:determining whether the number and quali ty of thealternatives found, developed, or both, provide anadequate range of choices for the selection step.The selection step also has three phases: (3a) analy-sis and evaluation: evaluating and understandingthe consequences over space and time of each ofthe proposed al ternat ives and communicat ing theseresults clearly to the decision makers; (3b) judg-ment and choice: one individual makes a choice;and (3~) bargaining and choice: a group of deci-sion makers negotiates a choice. The final step, au-thorization, may have two outcomes: (4a) autho-rization achieved: approval inside and outside theinst i tut ional hierarchy is obtained, marking the endof the planning process and the beginning of theimplementat ion process; and (4b) authorizat ion de-nied: evaluating the cause for denial and loopingback to the appropriate part of the decision processto make another at tempt at achieving authorization(Janssen, 1992).

A particular decision can take many pathwaysthrough these four steps, and i terat ive cycles are anormal part of how environmental decisions are ac-tually made (Janssen, 1992). These cycles occur asthe decision part icipant’s understanding of a com-plex problem evolves and when alternative solu-tions fail to meet administrative, scientific, or po-li t ical s tandards. Mintzberg et al . (1976) maintainedthat problems can be classif ied into seven types andthat the solution cycle for each type can be mappedon Figure 12.3. Janssen (1992) i l lustrates this pointby present ing and discussing the solut ion cycles of20 actual environmental problems in the Nether-lands, ranging from measures to reduce NH3 emis-sions, to clean-up of a polluted site, to protectingforests from acid rain. The effectiveness of com-peting EM-DSSs may be evaluated by how manyof the above phases are supported, how well theyare supported, and whether the complex iterativecycling of real-world problems is supported(Janssen, 1992).

12.3.2 ActThe planning stage of adaptive management resultsin decisions about goals and constraints. The ac-tion stage determines how, where, and when to im-plement activities to achieve the goals and adhereto the constraints. Given a clear statement of man-

agement goals and objectives, the implementationstage creates testable adaptive management hy-potheses, explici t ly describes the assumptions sup-porting them, and generates an appropriate set oftargeted actions (Everett et al., 1993). How eachhypothesis is tested must be carefully and clearlydocumented (Walters and Holling, 1990; Lee,1993; Kimmins, 1995).

12.3.3 MonitorDocumentation in the action stage is stressed be-cause the monitoring stage often occurs months toyears later, and the individuals who implementedthe actions may not be involved in monitoring orsubsequent evaluation. Documentation may be theonly link between the two stages. The monitoredresults of experimental actions must also berecorded carefully and in detail so that a complete,understandable package exists for the evaluationstage.

This definition of the monitoring stage of adap-tive management has immediate consequences.Which variables are monitored and when, how, andwhere they are monitored depend almost entirely onthe hypotheses created in the action stage and on thetype of actions deemed necessary to test those hy-potheses. A unique goal-hypotheses-action se-quence will probably need to be designed for eachspecific management unit. Similarly, each unit willprobably have unique monitoring requirements todistinguish among the adaptive experimental hy-potheses proposed for it. As a result, no general,broad-spectrum monitoring program can or shouldbe designed to support adaptive management. Adap- .tive management means management by experi-ment. Management by experiment requires hy-potheses that must be implemented and tested.Hence monitoring can only occur after the hypothe-ses have been designed and their tests devised so thatit is clear what needs monitoring (FEMAT, 1993).

12.3.4 EvaluateFinally, the documentation describing each adap-tive management experiment must be analyzed andthe resul ts evaluated. Promising s tat is t ical methodshave been identified (Carpenter, 1990), but usingthem requires considerable expertise. At the end ofthe adaptive management cycle, a written reportshould communicate the results publicly to stake-holders and managers, influencing future cycles ofthe planning activity of adaptive management(Everett et al., 1993). In fact, a metaanalysis of all

12.3 Ecosystem Management Processes 1 6 9

adaptive experimental results should be compiledperiodically and forwarded to the next higher plan-ning level for corrective change leading to new ac-tions.

Adaptive management, when implemented asdefined by Walters and Holling (1990), FEMAT(1993), and Lee (1993), is a complex and chal-lenging process. The adaptive management processis not a license to manipulate ecosystems haphaz-ardly simply to relieve immediate sociopoliticalpressure (Everett et al., 1993). Adaptive manage-ment must be applied correctly and rigorously asmanagement by experiment if we are to achieve ourstated goals. “Managing to learn entails imple-menting an array of practices, then taking a scien-tific approach in describing anticipated outcomesand comparing them to actual outcomes. Thesecomparisons are part of the foundation of knowl-edge of ecosystem management” (FEMAT, 1993,p. 11-87). The whole point of adaptive managementis to generate change in the way ecosystem man-agement is applied.

A number of institutional challenges must be ad-dressed before adaptive management can make itsexpected positive contribution to the ecosystemmanagement process (Lee, 1993). Adaptive man-agement requires a greater level of expertise in sta-tistical experimental design and analysis than othercompeting decision processes. Kessler et al. (1992)suggested that adaptive management requires closecollaboration between forest managers and scien-tists. “Finding creative ways of conducting power-ful tests without forcing staffs to do things theythink are wrong or foolish is of central importanceto the human part of adaptive management” (Lee,1993, p. 113). Managers and the interested stake-holders must accept that adaptive managementmeans making small, controlled mistakes to avoidmaking big ones. Keeping adaptive managementunbiased may be difficult; research that has conse-quences is research with which managers or stake-holders may try to tamper or prevent altogether(Lee, 1993). The costs of properly monitoringresults and documenting the entire managerialexperiment are unknown (Smith, 1997). Nonethe-less, adaptive management, supplemented by theMintzberg et al. (1976) planning process, is an at-tractive candidate for an ecosystem managementprocess at several operational scales. Despite muchsupportive rhetoric, the institutional and fundingchanges needed to implement adaptive manage-ment as an ecosystem management process for fed-eral forestland management have not yet beenwidely accomplished.

Although the adaptive management concept ap-pears to be the most well developed candidate foran operational ecosystem management process,others also should be investigated. Lindblom(1990), cited by Smith (1997), advocated a conceptcalled probing as a candidate for an ecosystemmanagement decision process. Probing is an infor-mal process of observation, hypothesis formula-tion, and data comparison in which people of allbackgrounds can engage. Jensen and Everett (1993)pointed to a method called a land evaluation sys-tem as another candidate for an ecosystem man-agement process. The land evaluation system (Zon-nefeld, 1988) has been used by the United NationsFood and Agricultural Organization and by the In-ternational Society for Soil Sciences in forestryland-use planning. Howitt (1978), cited by Allenand Gould (1986), offered a “simple” approach todealing with decision processes for wicked prob-lems that might be useful for ecosystem manage-ment. Rittel(1972) advocated a “second generationsystems approach” to wicked problem solutionbased on the logic of arguments (Conklin and Bege-man, 1987; Hashim, 1990). Problems and their con-sequences can be made understandable to individ-uals and groups by asking and answering crucialquestions while diagramming the process using theformal logic of argumentation. Vroom and Jago(1988), cited in Sample (1993), suggested theircontingent decision process may be used for prob-lems like ecosystem management. Of course, anydecision-making process used to implementecosystem management in the United States mustsatisfy the requirements of the 1969 National En-vironmental Policy Act (NEPA) and the 1976 Na-tional Forest Management Act (NFMA).

Several formal, well-described candidates for anecosystem management decision process have beenintroduced here. In addition, numerous local, ad-hoc decision processes have been developed andtested under fire in every ranger district in the U.S.National Forest System. Few case studies (e.g.,Steelman, 1996) have been published, and notmany evaluations (e.g., Shindler and Neburka,1997) of the strengths and weaknesses of these in-formal decision-making processes have been con-ducted. Surely a concerted effort to study the ex-isting formal and informal ecosystem managementprocesses would result in some powerful candidatesto implement ecosystem management. Because theadaptive management-concept can also be used toimprove our management systems, it may not beoverly important which particular ecosystem man-agement decision processes we choose. It is, how-


ever, critically important that we choose severaland then usi: Ihe adaptive management philosophyto test and improve them in real-life situations(Kimmins, 1991).

12.4 Decision SupportSystems Defined

DSSs help managers make decisions in situationswhere human judgment is an important contributorto the problem-solving process, but where limita-tions in human information processing impede de-cision making (Rauscher, 1995). The goal of a DSSis to amplify the power of the decision makers with-out usurping their r ight to use human judgment andmake choices. DSSs attempt to bring together theintellectual flexibility and imagination of humanswith the speed, accuracy, and tirelessness of thecomputer (Klein and Methlie, 1990; Sage, 1991;Turban, 1993; Holsapple and Whinston, 1996).

A DSS contains a number of subsystems, eachwith a specific task (Figure 12.4). The first, andmost important, is the subsystem composed of thedecision maker(s). Decision makers are con-sciously diagrammed as part of the DSS because,without their guidance, there is no DSS. The groupnegotiat ion management subsystem helps decis ionmakers to organize their ideas, formulate relation-ships surrounding issues and arguments, and refine

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their understanding of the problem and their ownvalue -:jc’.ems (Jessup and Valacich, 1993; Hol-sapple ,md Whinston, 1996). Examples of groupnegotiation tools include the active response GISsystem (AR/GIS) (Faber et al., 1997), the issue-based information system (IBIS) (Conklin andBegeman, 1987; Hashim, 1990), and various so-cioecological logic programming models (Thom-son, 1993, 1996).

Group negotiation tools are .used to constructissue-based argument structures using variants ofbelief networks to clarify the values and prefer-ences of group members in the attempt to reachgroup consensus. For example, IBIS uses formalargument logic ( the logic of questions and answers)as a way to diagram and elucidate argumentativethinking (Hashim, 1990). By asking and answeringcrucial questions, you can begin to better under-stand the problem and its solution set. Under-standing the meaning of terms, and through themour thoughts, l ies at the heart of collaborative man-agement. Greber and Johnson (1991) illustratedhow the malleable nature of many terms, in thiscase “overcutting,” creates logically defensible dif-ferences of opinion that have nothing to do with aperson’s honesty or dishonesty in the argument.DSS should specifically deploy mechanisms bywhich the biological realities guide and, if appro-priate, constrain the desires of the stakeholders(Bennett, 1996). For example, compromise is notacceptable for some issues. If the productive ca-

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USER INTERFA

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Group Negotiation Support

4

Decision MakersFIGURE 12.4. Major compo-nents of a generic decisionsupport system.

12.4 Decision Support Systems Defined 171

pacity of an ecosystem is fixed, yet key stakehold-ers all want to extract a product from that ecosys-tem at a higher level, a compromise midway be-tween the levels will be unsustainable.

The next major subsystem, spatial and nonspa-tial data management, organizes the available de-scriptions of the ecological and management com-ponents of ecosystem management. Data must beavailable to support choices among alternativemanagement scenarios and to forecast the conse-quences of management activities on the landscape.There is a tension between the increasing numberof goals that decision makers and stakeholdersvalue and the high cost of obtaining data andunderstanding relationships that support thesechoices. Monitoring both natural and anthro-pogenic disturbance activities and disturbance-freedynamics of managed forest ecosystems are alsoextremely important if an EM-DSS is to accuratelyportray the decision choices and their conse-quences. Barring blind luck, the quality of the de-cision cannot be better than the quality of theknowledge behind it. Poor data can lead to poor de-cisions. It is difficult to conceive of prudent ecosys-tem management without an adequate biophysicaldescription of the property in question.

The next four subsystems, knowledge-based,simulation model, help-hypertext, and data visual-ization management, deal with effectively manag-ing knowledge in the many diverse forms in whichit is stored, represented, or coded (see Rauscher etal., 1993, for more detail). Knowledge that is notlanguage based is either privately held in people’sminds or publicly represented as photographs,video, or graphic art. Language-based knowledgeis found in natural language texts of various kinds,in mathematical simulation models, and in expertor knowledge-based systems. Data visualizationsoftware has been developed that can manipulatephotographic, video, and graphic art representa-tions of current and future ecosystem conditions.Data visualization software is beginning to be in-corporated into EM-DSS on a routine basis to helpdecision makers see for themselves the likely im-pact of their decisions on the landscape.

In the last 20 years, an impressive amount ofmathematical simulation software has been devel-oped for all aspects of natural resource manage-ment. Schuster et al. (1993) conducted a compre-hensive inventory of simulation models availableto support forest planning and ecosystem manage-ment. They identified and briefly described 250software tools. Jorgensen et al. (1996) produced an-other compendium of ecological models that in-

corporate an impressive amount of ecosystem the-ory and data. The simulation model managementsubsystem of the EM-DSS is designed to providea consistent framework into which models of manydifferent origins and styles can be placed so thatdecision makers can use them to analyze, forecast,and understand elements of the decision process.

Despite our most strenuous efforts to quantifyimportant ecological processes to support a theoryin simulation model form, by far the larger body ofwhat we know can only be expressed qualitatively,comparatively, and inexactly. Most often this qual-itative knowledge has been organized over longyears of professional practice by human experts.Theoretical and practical advances in the field ofartificial intelligence applications in the last 20years now allow us to capture some of this quali-tative, experience-based expertise into computerprograms called expert or knowledge-based sys-tems (Schmoldt and Rauscher, 1996). It is still notpossible to capture the full range and flexibility ofknowledge and reasoning ability of human expertsin knowledge-based software. We have learned,however, how to capture and use that portion of ex-pertise that the human expert considers routine. Theknowledge management subsystem of the EM-DSSis designed to organize all available knowledge-based models in a uniform framework to supportthe decision-making process.

Finally, a large amount of text material existsthat increases the decision maker’s level of under-standing about the operation of the decision sup-port system itself, the meaning of results from thevarious modeling tools, and the scientific basis forthe theories used. This text material is best orga-nized in hypertext software systems. Hypermediamethodology supports a high degree of knowledgesynthesis and integration with essentially unlimitedexpandability. The oak regeneration hypertext(Rauscher et al., 1997b) and the hypermedia refer-ence system to the FEMAT report (Reynolds et al.,1995) are recent examples of the use of hypertextto synthesize and organize scientific subject mat-ter. Examples of the use of hypertext to teach andexplain software usage can be found in the helpsystem of any modem commercial computer pro-gram.

The software subsystems of an EM-DSS de-scribed so far help decision makers to organize thedecision problem, formulate alternatives, and ana-lyze their future consequences. The decision meth-ods management subsystem (Figure 12.4) providestools and guidance for choosing among the alter-natives, for performing sensitivity analysis to iden-


tify the power of specific variables to change theranking of alternatives, and for recording the deci-sions made and their rationale. Many facets or di-mensions influence the decision-making process.The rational-technical dimension, which concernsitself with the mathematical formulation of themethods of choice and their uses, is the one mostoften encountered in the decision science literature(Klein and Methlie, 1990; Rauscher, 1996). Butthere are others, including the political-power di-mension (French and Raven, 1959; O’Reilly, 1983)and the value-ethical dimension (Brown, 1984;Klein and Methlie, 1990, p. 108; Rue and Byars,1992, p. 61).

Decision makers might find themselves at anypoint along the political-power dimension boundedby a dictatorship (one person decides) on the oneextreme and by anarchy (no one can decide) on theother. Intermediate positions are democracy (ma-jority decides), republicanism (selected representa-tives decide), and technocracy-aristocracy (expertsor members of a ruling class decide). Currently,three approaches seem to be in use at multiple so-cietal temporal and spatial scales: management byexperts (technocracy), management by legal pre-scription (republicanism), and management by col-laboration (democracy) (Bormann et al., 1993). Noone approach predominates. In fact, the sharing ofpower among these three approaches creates ten-sions that help to make ecosystem management avery difficult problem. In the context of ecosystemmanagement, the value-ethical dimension might bedefined on the one extreme by the preservationistethic (reduce consumption and let nature take itscourse) and cn the other by the exploitation ethic(maximum yield now and let future generationstake care of themselves). Various forms of the con-servation ethic (use resources, but use them wisely)could be defined between these two extremes. Therational-technological dimension is defined by nor-mative-rational methods on the one hand and ex-pert-intuitive methods on the other. Numerous in-termediate methods also have been described andused (Janssen, 1992; Rauscher, 1996). The formalrelationships among these dimensions affecting thedecision process have not been worked out.

Informally, it is easy to observe decision-making situations where the political-power orvalue-ethical dimensions dominate the rational-technical dimension. Choosing an appropriate de-cision-making method is itself a formidable task(Silver, 1991; Turban, 1993) that influences boththe design of alternatives and the final choice.Many EM-DSSs do not offer a decision methodsubsystem due to the complexity and sensitivity of

the subject matter. Unfortunately, providing no for-mal support in EM-DSS for choosing among al-ternatives simply places all the burden on the usersand may make them more vulnerable to challengesof their process and choice mechanisms.

12.5 Comparison of ExistingEcosystem Management DSS

Mowrer et al. (1997) surveyed 24 of the leadingEM-DSSs developed in the government, academic,and private sectors in the United States. Their re-port identified five general trends: (1) while at leastone EM-DSS fulfilled each criteria in the ques-tionnaire used, no single system successfully ad-dressed all important considerations; (2) ecologicaland management interactions across multiple scaleswere not comprehensively addressed by any of thesystems evaluated; (3) the ability of the current gen-eration EM-DSSs to address social and economicissues lags far behind biophysical issues; (4) theability to simultaneously consider social, eco-nomic, and biophysical issues is entirely missingfrom current systems; and (5) group consensus-building support was missing from all but one sys-tem, a system that was highly dependent on trainedfacilitation personnel (Mowrer et al., 1997). In ad-dition, systems that offered explicit support forchoosing among alternatives provided decisionmakers with only one choice of methodology. Thereviewers noted that little or no coordination hadoccurred between the 24 development teams, re-sulting i:i large, monolithic, stand-alone systems,each with a substantially different concept of theecosystem management process and how to sup-port it.

Different EM-DSSs appear to support differentparts of the ecosystem management process. Table12.1 lists 33 EM-DSSs, the 24 systems surveyedby Mowrer et al. (1997) plus 9 DSSs not includedin that study. Nineteen of the 33 are labeled full-service EM-DSSs at their scale of operation be-cause they attempt to be comprehensive EM-DSSs,offering or planning to offer support for a completeecosystem management process. These EM-DSSscan be further classified by the scale of support thatis their primary focus: regional assessments, forestplanning, or project-level planning. The remain-ders, labeled functional service modules, providespecialized support for one or a few phases of theentire ecosystem management process. These ser-vice modules can be organized according to thetype of functional support that they provide group

12.5 Comparison of Existing Ecosystem Management DSS 173

‘i’mLE 12.1. A representative sample of existing ecosystem management decision support software for forestconditions of the United States arranged by operational scale and function.

Full-service EM-D% Functional service modules

O p e r a t i o n a l s c a l e M o d e l s Fur&on M o d e l s

Regional assessments EMDSLUCASa

Forest-level planning RELMSPECTRUMWOODSTOCKARCFORESTSARATERRA VISIONEZ-IMPACT”DECISION PLUS=DEFINITEa

Project-level planning NEDINFORMSMAGISKLEMSTEAMSL M S ’

Group negotiations

Vegetation dynamics

AWGISIBISaF V SLANDISCRBSUMSIMPPLLE

Disturbance simulations FIREBGCGYPSESUPEST

Spatial visualization UTOOLS/UVIEWsvsaSMARTFORESTB

Interoperable system architecture LOKICORBA”

Economic impact analysisActivity scheduling

IMPLANSNAP

“References for models not described in Mowrer et al. (1997): EZ-IMPACT (Behan, 1994); DECISION PLUS (Sygenex, 1994);IBIS (Hashim, 1990); DEFINITE (Janssen and van Herwijnen, 1992); SMARTFOREST (Orland, 1995); CORBA (One et al.,1996); SVS (McGaughey. 1997); LMS (McCarter et al., 1998); LUCAS (Berry et al., 1996).

negotiations, vegetation dynamics, disturbancesimulation, spatial visualization, and interoperablesystem architecture.

12.5.1 Full-service EM-DSS

Regional AssessmentsThe Ecosystem Management Decision Support(EMDS) program is a software system specificallydesigned to support the development of ecologicalassessments, usually at regional or watershedscales. It provides a general software environmentfor building knowledge bases that describe logicalrelat ions among ecosystem states and processes ofinterest in an assessment (Reynolds et al., 1997).Once users construct these knowledge bases, thesystem provides tools for analyzing the logicalstructure and the importance of missing informa-tion. EMDS provides a formal logic-based ap-proach to assessment analysis that faci l i tates the in-tegration of numerous diverse topics into a singleset of analyses. I t also provides robust methods forhandling incomplete information. A variety ofmaps, tables, and graphs provides useful informa-tion about which data are missing, the influence ofmissing data, and how data are distributed in thelandscape. EMDS also provides support for ex-ploring alternative future conditions. Finally,

EMDS is general in application and can be used atthe scale relevant to an assessment problem(Reynolds et al., 1997).

LUCAS is a multidisciplinary simulation frame-work for investigating the impact of land-use man-agement policies (Berry et al., 1996). LUCAS hasbeen used to support regional assessments of land-use change patterns as a function of social choicesand regulatory approaches (Wear et al., 1996).LUCAS can be used to compare the effects of al-ternative ecosystem management strategies thatcould be implemented over an ecoregion of anysize. These alternatives could be evaluated basedon any number of social choice assumptions as-cribed to private landowners (Wear et al., 1996).LUCAS could also be used to address the effectsof land cover changes on natural resource suppliesand local incomes. The advantage of an EM-DSSoperating at the ecoregional scale is that regionaldecision-making activities and their consequencescan be forecast with reasonable credibility.

Forest-level or Strategic PlanningForest-level planning corresponds to the strategicplanning process of decision science (Holsappleand Whinston, 1996). Many federal agencies, in-cluding the USDA Forest Service, have relied onlinear programming systems of various kinds as the

primary strategic planning decision support tool. In uated for their effectiveness in supporting these1979, a linear programming, harvest scheduling tasks, their ease of use in practice, and their abil-model, FORPLAN, was turned into a forest-level ity to communicate their internal processes clearlyplanning, too, and until 1996 all national forest su- and succinctly to both decision makers and stake-pervisors were required to use it as the primary an- holders. Such an evaluation has not yet been con-alytical tool for strategic forest planning. After 17 ducted.years of increasingly fierce criticism that the nor-mative, rational, optimization approach to decisionanalysis implemented by FORPLAN and its suc-

Project-level Implementation or

cessor, SPECTRUM, was not adequate, the Forest Tactical Planning

Service finally removed its formal requirement to “Forest plans are programmatic in that they estab-use FORPLAN/SPECTRUM (Stephens, 1996). lish goals, objectives, standards, and guidelines thatThe specifics of the arguments critical of FOR- often are general. Accordingly, the public andPLAN/SPECTRUM as an analytical tool for forest USDA Forest Service personnel have flexibility inplanning are beyond the scope of this chapter and interpreting how forest plan decisions apply, or cancan be readily found in the following publications: best be achieved, at a particular location. In addi-Barber and Rodman (1990), Hoekstra et al. (1987), tion, forest plans typically do not specify the pre-Shepard (1993), Liu and Davis (1995), Behan cise timing, location, or other features of individ-(1994, 1997), Kennedy and Quigley (1993), ual management actions” (Morrison, 1993, p. 284).Howard (1991), Canham (1990), Morrison (1993), EM-DSSs at the project level help to identify andand Smith (1997). design site-specific actions that will promote the

Forest-level planning may be more successfully achievement of forest plan goals and objectives.performed using soft, qualitative decision analysis For example, a strategic-level forest plan might as-formalisms than the hard, quantitative methods em- sign a particular landscape unit for management ofployed in rational, linear or nonlinear optimization bear, deer, and turkey, with minimum timber har-schemes. Many other decision analysis formalisms vesting and new road construction and no clearcut-exist (see Rauscher, 1996; Smith, 1997) along with ting. The tactical project implementation planthe tools that make them useful and practical (Table would identify specific acres within this manage-12.1). A number of these techniques may offer ment unit that would receive specific treatments ingreater support for dealing with power struggles, a specific year. Project-level EM-DSSs have beenimprecise goals, fuzzy equity questions, rapidly developed to support the tactical-level planningchanging public preferences, and uneven quality process.and quantity of information (Allen and Gould, Project-level EM-DSSs (Table 12.1) can be sep-1986). In particular, EZ-IMPACT (Behan 1994, arated into those that use a goal-driven approach1997) and DEFINITE (Janssen and van Herwijnen, and those that use a data-driven approach to the de-1992) are well-developed and tested analysis tools cision support problem. NED (Rauscher et al.,for forest planning that use judgment-based, ordi- 1997a; Twery et al., 2000) is an example of a goal-nal, and cardinal data to help users to characterize driven EM-DSS. Rauscher et al. (2000) present athe system at hand and explore hidden interactions practical, decision-analysis process for conductingand emergent properties. ecosystem management at the project implementa-

A forest plan should demonstrate a vision of de- tion level and provide a detailed example of its ap-sired future conditions (Jensen and Everett, 1993). plication to Bent Creek Experimental Forest inIt should examine current existing conditions and Asheville, North Carolina. Because management ishighlight the changes needed to achieve the desired defined to be a goal-driven activity, goals must befuture conditions over the planning period (Gross- defined before appropriate management actions canarth and Nygren, 1993). Finally, the forest plan be determined. It cannot be overemphasized that,should demonstrate that recommended alternatives without goals, management cannot be properlyactually lead toward desired future conditions by practiced (Rue and Byars, 1992). GoaI-driven sys-tracking progress annually for the life of the plan. tems, such as NED; assist the user in creating anThe forest plan should be able to send accom- explicitly defined goal hierarchy (Rauscher et al.,plishable goals and objectives to the level of pro- 2000).ject implementation and receive progress reports A goal is an end state that people value and arethat identify the changes in forest conditions that willing to allocate resources to achieve or sustainmanagement has achieved. Ideally, all competitors (Nute et al., 1999). Goals form a logical hierarchyin this class of EM-DSS should be objectively eval- with the ultimate, all-inclusive goal at the top, sub-


12.5 Comparison of Existing Ecosystem Management DSS 175

goals at the various intermediate levels, and a spe-cial goal, which may be called a desired future con-dition, at the bottom (Saaty, 1992; Keeney andRaiffa, 1993). A desiredfiture condition (DFC) isa goal statement containing a single variable mea-suring some observable state or flow of the systembeing managed (Nute et al., 1999). DFCs are thelowest level of the goal hierarchy. They are directlyconnected to the management alternatives beingconsidered (Saaty, 1992). Furthermore, DFCs pre-cisely define the measurable variables that each al-ternative must contain (Mitchell and Wasil, 1989).In other words, each DFC provides a measure ofthe degree to which any given ecosystem state, cur-rent or simulated future, meets the goal statement(InfoHarvest, 1996).

For example,

Goal: Freshwater Fishing Opportunities Exist IFDFC(1): pH of all (> = 90%) freshwater lakes > 5

DFC(2): popular game fish are plentiful ANDDFC(3): access to all (> = 90%) freshwater lakes is

adequate.

Given that “plentiful” and “adequate” are furtherdefined so that they are measurable, the three DFCsabove define three attributes, that is, pH, populargame fish, and access to freshwater lakes, that eachalternative under consideration must have. Other-wise, it will be impossible to determine whether thealternative can satisfy the goal “Freshwater Fish-ing Opportunities Exist.” DFCs should be objec-tive in nature. That is, there should exist a com-monly understood scale of measurement, and theapplication of that scale to the ecosystem shouldyield roughly the same results no matter who makesthe measurement. DFC( 1) is objective in this sense.On the other hand, DFC(2) and DFC(3) are usingsubjective, binary scales for “plentiful” and “ade-quate.” Research results indicate that subjectivelydeveloped scales can be reliably used by qualifiedprofessionals (Keeney and Raiffa, 1993, p. 40). Anoperationally practical DFC should (1) provide theappropriate information on theoretical grounds and(2) also be obtainable. In other words, a DFCshould accurately define the lowest-level subgoaland be measurable in practice.

Unlike goals, which depend primarily on valuejudgments, defining how to achieve a goal with aset of DFCs depends primarily on factual knowl-edge (Keeney, 1992). A set of DFCs that defines alowest-level goal is not generally unique (Keeneyand Raiffa, 1993). There are usually alternativeways to define the same lowest-level goal, and noa priori tests exist to show that one way is better

or worse than another. This disparity typically re-sults from competing scientific theories or profes-sional judgment. Consequently, it is important todocument the justification for defining the lowest-level goal in any particular way.

Constraints, like goals, have a standard that is ei-ther met or not (Keeney, 1992). This standard ismeant to screen unacceptable goals, objectives, al-ternatives, and management prescriptions fromconsideration. Requirements are equivalent to con-straints, but are phrased differently. Logically, “youmust” do something is exactly the same as “youmust not” do something else. Permissions are thelogical inverse of constraints. Permissions make itexplicitly clear that certain goals, objectives, alter-natives, and silvicultural prescriptions are allowedif they naturally surface in the managementprocess. Permissions are not mandatory; if theywere, they would be requirements.

The development of a goal hierarchy, the con-straint network, and the specification of DFCs fora complex decision problem is more art than sci-ence. Although no step by step procedures are pos-sible, some useful guidelines have been developedand summarized by Keeney and Raiffa (1993),Clemen (1996), and by InfoHarvest (1996).

Alternatives are the courses of action open to adecision maker for satisfying the goal hierarchy(Holtzman, 1989). In ecosystem management, eachalternative contains a set of action-location-timetriples that is intended to change the landscape sothat goal satisfaction is improved. These triples,called prescriptions, embody the purposeful appli-cation and expenditure of monetary, human, mate-rial, and knowledge resources that define ecosys-tem management.

The design of alternatives, like the design of thegoal hierarchy, is largely an art that relies heavilyon decision science expertise, along with an expert-level understanding of forest ecosystem manage-ment (Klein and Methlie, 1990; Clemen, 1996). Itis also very much an iterative process. High-quality decisions require the design of a set ofpromising, distinct alternatives to evaluate (Holtz-man, 1989; Keeney, 1992). Given knowledge about(1) the current condition of the forest ecosystem;(2) the goals and DFCs; (3) the standards, guides,best management practices, and constraints in forceat any given time; and (4) available managementprescriptions, an experienced manager can craft al-ternatives that represent reasonable answers to thequestion of what state of organization we want forthis forest ecosystem so that we can best meet thegoals. The human mind is the sole source of alter-natives (Keeney, 1992).


In contrast to goal-driven systems, INFORMS(Perish0 et al., 1995; Williams et al., 1995) is adata-driven EM-DSS. Data-driven systems do notrequire the existence of an explicit goal hierarchy.Indeed, it is often the case that the only existinggoals are implicit goals that reside in the privateknowledge base of the manager. Data-driven sys-tems begin with a list of actions that the user wantsto explore and search the existing landscape con-ditions, as reflected in the system database, to findpossible locations where these management actionscan be implemented.

Both approaches have their strengths and weak-nesses. Goal-driven systems tend to be rather pre-scriptive. They require the user to follow a certainsequence of events and force the user to make cer-tain critical decisions in order to follow a prede-fined ecosystem management process. Data-drivensystems allow users more freedom to craft theirown process in an ad-hoc fashion; this providesgreat freedom of action, but places all the burdenof knowing what to do and why to do it on the user.Goal-driven systems, by definition, tend to ensurethat management actions move the landscape to-ward the specified desired future conditions bycommitting to a particular ecosystem managementdecision process. This reduces the utility of theEM-DSS to that set of decision makers who wishto use this particular decision process. On the otherhand, data-driven systems offer no guarantee thatthe results of the sum of the actions have anyresemblance to the desired future conditions as de-fined by the strategic objectives. Data-driven sys-tems, however, do allow competent and know-ledgeable decision makers maximum flexibility inthe analyses that they perform and how they putthem together to arrive at a decision. Hybrid goal-and data-driven systems may offer users the ad-vantages of both approaches.

12.5.2 Functional Service ModulesThe full-service EM-DSSs rely on specialized soft-ware service modules to add a broad range of ca-pabilities (Table 12.1). Tools to support group ne-gotiation in the decision process are both extremelyimportant and generally unavailable and underuti-lized. AR/GIS (Faber et al., 1997) is the most fullydeveloped software available for this function.IBIS, another group negotiation tool, is an issue-based information system that implements argu-mentation logic (the logic of questions and an-swers) to help users to formally state problems,understand them, clearly communicate them, andexplore alternative solutions (Conklin and Bege-man, 1987; Hashim, 1990). Vegetation dynamics

simulation models, both at the stand and at the land-scape scale, provide EM-DSSs with the ability toforecast the consequences of proposed manage-ment actions. Disturbance models simulate the ef-fects of catastrophic events, such as fire, insect de-foliation, disease outbreaks, and wind damage.Models that simulate direct and indirect human dis-turbances on ecosystems are not widely available.Although models that simulate timber harvestingactivities exist, they provide little, if any, ecologi-cal impact analyses, such as the effect of extrac-tion on soil compaction, on damage to remainingtrees, or on the growth response of the remainingtree and understory vegetation. Models that simu-late the impact of foot traffic, mountain bikes, andhorseback riding on high-use areas are largelymissing. Models that simulate climate change, nu-trient cycling processes, acid-deposition impacts,and other indirect responses to human disturbanceexist, but are rarely practical for extensive forestanalyses. Stand- and landscape-level visualizationtools have improved dramatically in the last fewyears. It is now possible, with relatively little ef-fort, to link to and provide data for three-dimensional stand-level models such as SVS (Mc-Gaughey, 1997) and landscape-level models suchas UVIEW (Ager, 1997) and SMARTFOREST(Orland, 1995).

12.6 Interoperability in EcosystemManagement DSS

Existing EM-DSSs (Table 12.1), with few excep-tions, are islands of automation unable to easilycommunicate with each other. They have been writ-ten in different software languages, they reside ondifferent hardware platforms, and they have differ-ent data access mechanisms and different compo-nent-module interfaces. For example, nongeo-graphical databases may be written in Oracle,geographical information system (GIS) databasesin ARC/INFO, knowledge bases in Prolog, and asimulation model in C or Fortran. Some executeonly on a UNIX platform, others only in a Mi-crosoft Windows environment. As a group, theyhave poorly developed mechanisms for achievingintegrated operations with (1) other existing full-service EM-DSS; (2) the many available func-tional-service modules (Schuster et al., 1993;Jorgensen et al., 1996); (3) readily available, high-quality commercial software; or (4) new softwaremodules that independent development groups arecontinually producing in their efforts to supportecosystem management.

12.7 Conclusions 177

Interoperubili~ is the ability for two or moresoftware components to cooperate by exchangingservices and data with one another, despite the pos-sible heterogeneity in their language, interface, andhardware platform (Heiler, 1995; Wegner, 1996;Sheth, 1998). Interoperable systems provide a soft-ware standard that promotes communication be-tween components and provides for the integrationof legacy and newly developed components (Pot-ter et al., 1992; Liu et al., 2000). To date, effortsto achieve interoperability between EM-DSS mod-ules have used ad hoc techniques yielding unique,point-to-point custom solutions. Although suchunique solutions work, sometimes very efficiently,they are typically difficult to maintain and transferto other developers because of their idiosyncraticnature. After evaluating several of the leading EM-DSSs, Liu et al. (2000) concluded that no compre-hensive, theory-based interoperability standardscurrently exist for achieving integrated operationsof EM-DSS.

In contrast, interoperability outside the ecosys-tem management domain has received extensive at-tention. There is heavy emphasis in the larger com-puter science field toward the construction ofsystems from preexisting components based on in-teroperability standards (Mowbray and Zahavi,1995). Liu et al. (2000) evaluated four such ap-proaches (see Liu, 1998, for details). The ap-proaches addressed include CORBA (the CommonObject Request Broker Architecture, 1997),DCOM (the Distributed Component Object Model,Microsoft 1995, 1996, 1997), intelligent agents(Finin et al., 1994; Genesereth and Ketchpel, 1994;Mayfield et al., 1996), and DIAS/DEEM (the Dy-namic Information Architecture System/DynamicEnvironmental Effects Model, Argonne NationalLaboratory 1995a, 1995b). These approaches en-compass several different areas of computer sci-ence, including databases in which the emphasis ison interoperation and data integration, software en-gineering in which tool and environment integra-tion issues dominate, artificial intelligence forwhich systems consisting of distributed intelligentagents are being developed and explored, and in-formation systems.

With NED-l (Rauscher et al., 1997a; Tweryet al., 1999) and FVS (Teck et al., 1996, 1997) asexample EM-DSSs, Maheshwari (1997) usedCORBA and Liu et al. (2000) used DCOM to de-velop a framework for achieving integrated opera-tions. CORBA and DCOM both provide standardspecifications for achieving language interoper-ability and platform independence. They definetheir own interface standards to deal with pecu-liarities of legacy applications. They support dis-

tributed processing, object reuse, and the Internet.Both architectures are well documented, and thedocumentation materials are easily accessible to thepublic, on line as well as through books and jour-nal articles. CORBA can be purchased from mul-tiple vendors, and DCOM is shipped with WindowsNT/98 or can be downloaded free for Windows 95.Based on their tests, Liu et al. (2000) concludedthat DCOM was easier to learn and more produc-tive than CORBA.

The proposed DCOM-based interoperability de-sign was found to be general and to make no as-sumptions about the software applications to be in-tegrated (Liu et al., 2000). Its standardized interfacescheme enables integration of a variety of applica-tions. It is an open framework in the sense that ap-plication components can be added and/or removedeasily without drastically affecting the functional-ity of the whole system. DCOM comes with Win-dows NT and 98, so there is no up-front cost. Theprototype worked smoothly and was totally trans-parent to the user. Although no performance testswere carried out, there appeared to be no perfor-mance degradation as a result of using DCOM (Liuet al., 2000).

The design, implementation, and maintenanceof interoperable software architectures for EM-DSS are challenging activities. System integratorsface computer science problems with differenthardware platforms, software languages, compilerversions, data access mechanisms, module inter-faces, and networking protocols (Mowbray and Za-havi, 1995). In addition, the ecosystem manage-ment arena contributes challenges such as differentdata sources, ecosystem management process vi-sions, decision-making methods, and solutionstrategies. Future generations of EM-DSS must be-come more interoperable to provide the best pos-sible support for ecosystem management processes.

12.7 Conclusions

Ecosystem management has been adopted as thephilosophical paradigm guiding federal forest man-agement in the United States. The strategic goal ofecosystem management is to find an acceptablemiddle ground between ensuring the necessarylong-term protection of the environment while al-lowing an increasing population to use its naturalresources for maintaining and improving humanlife. Adequately described and widely acceptedecosystem management processes do not yet exist,but a concerted effort to study the many formal andinformal ecosystem management processes that doexist is yielding results. Several powerful candidate


processes to support the practical implementationof ecosystem management have been identified todate and are undergoing evaluation.

The generic theory of decision support systemdevelopment and application is well established.Numerous specific ecosystem management deci-sion support systems have been developed and areevolving in their capabilities. Given a well-definedand accepted set of ecosystem management pro-cesses to support, along with adequate time and re-sources, effective EM-DSSs can be developed.However, major social and political issues presentsignificant impediments to the efforts to makeecosystem management operational. A sociopolit-ical environment in which everyone wants tobenefit and no one wants to pay incapacitates thefederal ecosystem management decision-makingprocess. The very laws that were adopted to solvethe problem, the 1969 National Environmental Pol-icy Act and the 1976 National Forest ManagementAct, have led to procedural paralysis at exponen-tially rising costs (Behan, 1990). Developing aworkable ecosystem management process and thedecision-making tools to support it is probably oneof the most complex and urgent challenges facingforest ecosystem managers today.

To date, none of the available EM-DSSs hasbeen found capable of addressing the full range ofsupport required for ecosystem management(Mowrer et al., 1997). This is not surprising, be-cause it is highly unlikely that a single DSS canprovide adequate support for ecosystem manage-ment (Grossarth and Nygren, 1993; Mowrer et al.,1997), meaning that a familiarity with the entirerange of available decision analysis methodologyand related modeling tools is required. Many of theEM-DSSs introduced in this chapter hold greatpromise, but this promise has not been fully real-ized. A major reason for this situation is that sys-tem development has been primarily driven bytechnology, not by user requirements.’ The require-ments to guide EM-DSS development are unknownor poorly defined, because the ecosystem manage-ment decision processes themselves have been in-adequately identified ani described. The frequentlyobserved tendency to substitute technology for aninadequate or nonexistent ecosystem managementdecision-making process should be avoided be-cause it is rarely satisfactory. Although formalevaluation procedures are available (see Adelman,1992), few of the current EM-DSSs have under-gone an unbiased, critical evaluation of their suit-ability for ecosystem management decision sup-port. Such an evaluation is long overdue. In thefinal analysis, EM-DSS software should be evalu-ated using a simple question: Does the EM-DSS

improve the decision maker’s ability to make gooddecisions?

A number of more specific conclusions about thecurrent state of EM-DSS appear obvious:

1. The complexity of environmental dynamicsover time and space; the overwhelming amountsof data, information, and knowledge in differ-ent forms and qualities; and the multiple, oftenconflicting, management goals virtually guar-antee that few individuals or groups can con-sistently make good decisions without adequatesupport tools.

2. Ecosystem management, by definition, is con-cerned with both ecological and managementscience. Yet much of the effort seems to con-centrate on ecological issues to the detriment ofequally important management issues. Under-standing and developing good decision-makingprocesses is as important for the success ofecosystem management as understanding eco-logical structure and function (http://biology.usgs.gov/dss/def.html).

3. Ecosystem management ought to be practiced atmany different scales: forest landscape-nationalforest, ecoregion-multistate, biome-national,and global-international. This fact has not beenas widely recognized in the United States as ithas in Europe. In the United States, we have,however, scaled ecosystem management up tothe ecoregional scale by developing regionalecological assessments as a management tool.

4. People play two roles in ecosystem manage-ment. First, they are part of the communities oforganisms that interact with each other and theirabiotic environment. Second, they are the risktakers, the setters of objectives, the judges ofvalue; in other words, the decision makers. Weneed to understand human behavior and char-acteristics in both roles.

5. Defining and understanding the nature of sus-tainable societies and the nature of sustainableecosystems are equally important. Human soci-eties and ecosystems are inseparable and cannotbe understood or managed without reference toeach other.

6. The study of the management subsystem mustinclude understanding the dynamics of publicpreferences, conflict management and resolu-tion, and cost evaluation as it relates to ecosys-tem management.

In closing, it is appropriate to discuss how eco-logical assessments and EM-DSS may be mutuallysupportive. From a decision support point of view,ecological assessments have not been as useful as

12.8 References

We suggest that ecological assessments can bemore effective decision support tools if some or allof the following items are included in their scope:

1 .

2.

3 .

4 .

5 .

6 .

Clarify, label, and define the major regional is-sues that concern people.Explicitly identify and define the major socio-political-biological problems facing the region.Identify what or who is causing the problems,who has responsibility for solving them, whothe stakeholders are that are associated with theproblem, and who will pay for finding and im-plement ing so lu t ions .Clarify how ecosystem management efforts areto be coordinated at the regional scale and howprogress in social and ecological sustainabilityis going to be identified and tracked.Develop a set of regional-scale goals and de-sired future condit ions to measure these goals interms of regionally appropriate variables.Describe probable future scenarios that mightexist under different types of regional-scalemanagement strategies.

they might be. The ecological assessments under-taken so far have concentrated mostly on provid-ing (1) a description of current and historic ‘ecosys-tern composition, structure, and function and (2) adescript ion of the biot ic ( including human) and abi-otic processes that contr ibuted to the developmentof the current ecosystem condit ions. Although suchdescript ive emphasis on the ecological foundationsof the region is necessary, it is not sufficient. Thisis another example of how the management side ofecosystem management is often all but ignored.

Including such management-oriented i tems in anecological assessment is likely to make it politi-cally more sensitive. However, the resultant docu-ment would help to organize the entire ecosystemmanagement process at lower scales, set the toneand parameters of the public and professional de-bate, and explicitly state the problems as well asthe desired solutions for examination by everyoneconcerned. It would help to diffuse the impact ofpurely emotional points of view and bring the de-bate back to a more reasonable, rational arena.More specifically, such a regional assessmentwould provide solid direct ion, through the regionalgoals and desired future conditions, to forest-levelecosystem management planning and implementa-t ion .

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