Adaptive management: making it happen through participatory systems analysis

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  • & ResearchPaper

    Adaptive Management: Making itHappen Through Participatory SystemsAnalysis

    Carl Smith1,3*, L. Felderhof2,3 and O. J. H. Bosch1,3

    1School of Natural and Rural Systems Management, The University of Queensland, Australia2Queensland Parks and Wildlife Service, Atherton, Queensland, Australia3Cooperative Research Centre for Tropical Savannas Management, Australia

    Participatory systems analysis was used to construct system models of the operatingenvironment for fire management in conservation reserves in north Queensland, Australia.The aim of the study was to identify stumbling blocks to the adaptive management of fireand to test whether this could be done using participatory methods and a systemsmodelling tool called Bayesian Belief Networks (BBN). Results from the case studyindicate that the participatory system analysis approach provides a co-learning environ-ment that captures the collective (corporate) knowledge of the factors influencing plan-ning, implementing, monitoring and reviewing outcomes, thus allowing critical successfactors (CSFs) influencing the success of adaptive management to be identified. BBNprovided the scaffolding for piecing together this knowledge, allowing managers tostructure complex problems and conduct dynamic sensitivity and scenario analysis toidentify where intervention or investment can significantly improve the practice ofadaptive management within a natural resource management (NRM) agency. Copyright# 2007 John Wiley & Sons, Ltd.

    Keywords systems thinking; Bayesian Belief Networks; fire management; natural resourcemanagement


    Adaptive management has been advocated formany years as a way of managing naturalresources in the face of uncertainty and varia-

    bility, especially for systems where the outcomesof management decisions are difficult to predict(Holling, 1978; Bosch et al., 2003). The term refersto a systematic process for improving manage-ment policy and practice by learning from theoutcomes of previous operational policies andpractices (British Columbia Forest Service, 2000).The process is cyclic (Figure 1), with plans forachieving natural resource management (NRM)

    SystemsResearch andBehavioral ScienceSyst. Res.24, 567^587 (2007)Published online inWiley InterScience(

    *Correspondence to: Carl Smith, School of Natural and Rural SystemsManagement, The University of Queensland, Gatton 4343, Queens-land, Australia.E-mail:

    Copyright # 2007 John Wiley & Sons, Ltd.

  • objectives developed using current knowledge,and monitoring undertaken to track the successof implemented management actions. Reviewingmanagement outcomes contributes new knowl-edge, which can then be used to refine manage-ment plans for future implementation.

    The benefits of adaptive management aretwofold. First, it allows for decisions and actionto be based on experience and second, itengenders a culture of continuous improvementby consciously reflecting on previous manage-ment outcomes. Adaptive management makessense intuitively, however, it is not automaticallyundertaken, either by individual land managersor organizations responsible for NRM. This isbecause the activities of individuals and organ-izations are influenced by their priorities and theresources (time, skills, equipment, informationand personnel), policies and procedures thatmake up their operating environment. Hence theoperating environment of an individual ororganization is paramount to the success ofadaptive management.

    The factors critical to achieving each step in theplanning, implementing, monitoring and review-ing cycle need to be in place for adaptivemanagement to progress from an idealisticframework to practical application. However,to determine these is often a difficult task for anindividual or an organization, due to the

    multitude of interdependent factors that combineto determine the success of each step. Individualand organizational idiosyncrasies also mean thatthese success factors and their relationships willdepend on the unique operating environment inwhich adaptive management must take place.

    This paper demonstrates the potential forusing participatory systems analysis to assistorganizations with implementing adaptive man-agement. We illustrate how systems analysisinvolving stakeholders can be applied to identi-fying adaptive management stumbling blocksand target management interventions. A casestudy conducted with the Queensland Parks andWildlife Service (QPWS), Australia, is used as anexample.


    Systems designed by human beings are purpo-seful systems (Jackson, 2003). In other words,they are designed or managed for a purpose, toachieve particular outcomes. Organizations andother human-managed systems, such as con-servation reserves, are good examples of purpo-seful systems. However, in NRM, there are manyindividuals with an interest in such systems(stakeholders) and eachwill have amental modelof the system and its purpose depending on theirindividual understanding, experience, educationand values. This means that among stakeholdersthere can be a multitude of views about thepurpose of NRM systems and the factors thataffect these purposes.

    In managing purposeful systems, it is import-ant to accommodate the different world views ofthe stakeholders involved so that any proposedmanagement interventions are informed by abreadth of available experience, and acceptableto those who will need to implement changes orlive with the consequences of their implementa-tion. By combining a broad range of tools andtechniques developed in the field of systemsthinking, with participatory methods thatinvolve stakeholders, participatory systemsanalysis aims to provide a way of analyzingmanagement problems within purposeful sys-tems.

    Figure 1. Generalized adaptive management cycle (afterBosch et al., 2003)

    Copyright 2007 JohnWiley & Sons,Ltd. Syst. Res.24, 567^587 (2007)DOI:10.1002/sres

    568 Carl Smith et al.


  • The term participatory refers to a bottom-upapproach in which stakeholders participate insolving their management problems instead ofbringing in outside experts to solve them. Systemsanalysis refers to the application of systemsthinking to identify the root causes of manage-ment problems, and the potential implications ofmanagement decisions, by identifying the indi-vidual factors that may affect an outcome and thecausal relationships between them. In participa-tory systems analysis, the involvement of stake-holders allows the multitude of factors that mayinfluence outcomes or objectives to be identified,whilst systems thinking provides a mechanismthrough which these stakeholders can interactand discuss their understanding of the manage-ment system and the dependency relationshipsbetween these factors.

    The participatory systems analysis processinvolves setting management objectives, abstractmodelling to explore the effect of decisions orscenarios on management objectives, developingplans for implementing preferred decisions ormanagement interventions, and monitoring thesystem to track management successes and adaptmanagement interventions where necessary(Lynam, 2001). The focus of the paper is on theinitial steps of the participatory systems analysisprocess, that is, setting objectives and abstractmodelling. Examples are given of how modelscan be used to manage the operating environ-ment for the success of adaptive management.


    The success of adaptivemanagement depends onindividuals and organizations being able todevelop and implement plans, monitor outcomesand then review success within purposefulsystems. For adaptive management to work,factors critical to the success of each step need tobe put into place, and the stumbling blocks toeach step need to be removed (Figure 2). Theidentification of these critical success factors(CSFs) and stumbling blocks is not straightfor-ward because they will often be unique fordifferent individuals and organizations. There-

    fore, identifying them means unravelling theunique operating environment of an individualor organization, which is where the process ofparticipatory systems analysis can help.

    Each step in the adaptive management cycle isa point at which participatory systems analysiscan be applied. Planning, implementation,monitoring and reviewing objectives can be setwith stakeholders, and abstract models builtaround these to describe success factors and theirrelationships (Figure 3). These conceptualmodels can then be used to assess systemperformance and identify where managementintervention could lead to improved perform-ance. The remainder of this paper uses the resultsof a case study to demonstrate how the initialsteps in participatory system analysis can beapplied to examining adaptive managementwithin an organization responsible for firemanagement.


    In Australia, fire management on conservationreserves is a pertinent issue because the targeteduse of fire is recognized as essential for species

    Figure 2. Adaptive management nested within an operat-ing environment

    Copyright 2007 JohnWiley & Sons,Ltd. Syst. Res.24, 567^587 (2007)DOI:10.1002/sres

    AdaptiveManagement 569


  • conservation (Gill et al., 1981, 1999; Bradstocket al., 2002). However, the complexity of ecologi-cal processes, the inherent variability within andbetween fires, and the numerous permutationsbetween fire, terrain, climate, weather and biotameans that is it very difficult for landmanagers tomake accurate predictions about ecologicalresponses to fire (Whelan et al., 2002). Thuslearning by doing using adaptive managementis promoted as a rational approach for ecologicalfire management.

    As a first step towards implementing adaptivemanagement, a comprehensive fire managementsystem was introduced into the QPWS, Australia(Melzer and Clarke, 2003). The system providedreserve managers with set procedures for firemanagement planning and reporting. It wasintroduced in combination with a customizedgeographic information system for the storage ofdata used in planning and monitoring. Veg-etation monitoring plots also existed in someareas, but plots were not comprehensivelydistributed throughout the reserve system.

    Despite having the ingredients for an adaptivefire management system available, uptake byreserve managers was variable. Resources allo-cated to fire management were competingheavily with other management priorities (such

    as facilities maintenance) and managers wereexperiencing difficulty adapting to the number ofnew systems that were being promoted. In aneffort to identify the CSFs and stumbling blocksto adaptive fire management, QPWS initiated acase study focussed on conservation reserves innorth Queensland.

    The goal of the case study was to unravel theoperational issues across the agencies jurisdic-tions to determine whether there were commonelements that could be addressed, or if issueswere disparate and required individualsolutions. It was recognized that substantialgainsmight be achieved from investing in actionsapparently tangential to fire management (e.g. ITskills). However, such resource allocationoptions were difficult to evaluate. The organiz-ation also sought a method to compare perform-ance between work units and a system forongoing monitoring and charting of perform-ance. Links were subsequently made betweenresearchers and fire management practitioners,which enabled this project to proceed.


    The purpose of abstract modelling was todescribe QPWS fire management operations inthe context of the adaptive management cycle.The model building process consisted of twomain steps: (a) building influence (cause andeffect) diagrams to identify the CSFs related toadaptive management objectives and (b) con-verting these influence diagrams into systemsimulation models for analyzing the adaptivemanagement operating environment (Figure 4).

    Building Influence Diagrams

    Influence diagrams were constructed using theadaptive management cycle as a template fororganizing objectives and CSFs. For QPWS firemanagers, three sources of prior informationallowed an initial set of objectives and CSFs to beidentified (Table 1). These were:

    Figure 3. Participatory systems analysis applied to adap-tive management

    Copyright 2007 JohnWiley & Sons,Ltd. Syst. Res.24, 567^587 (2007)DOI:10.1002/sres

    570 Carl Smith et al.


  • 1. Fire Management Performance Indicators pre-viously developed in consultation with parkrangers and field staff. They covered the gen-eral categories of Planning, Operations,Reporting, Technology, Information, Monitor-ing, Communication and Training.

    2. Fire Management System Documentation outlin-ing QPWS planning and reporting proceduresfor fire management (Melzer and Clarke,2003).

    3. Best Practice Fire Management Workshop out-comes, which were diagrams constructed bypark rangers and field staff in previous work-shops identifying the issues they felt werebarriers to best fire management practiceand the broad cause and effect relationshipsbetween them.

    Based on the prior information, a draftinfluence diagram was constructed for eachadaptive management step. Two workshopswere then held with QPWS staff (both rangersand managers) to review the draft influence

    diagrams. A review process similar to thatoutlined by Cain (2001) was used. This involvedstarting at the objective and systematicallystepping backwards through the influence dia-grams to list those factors influencing each factor(Figure 5). This step-wise process allowed work-shop participants to add, change or removefactors included in the diagrams or modifydependency relationships.

    After the model review process, the influencediagrams for monitoring and reviewing weremerged into one because the workshop partici-pants found it difficult to separate them into twodistinct systems models. The result was threeinfluence diagramsfire management planning,implementation and monitoring-reviewing.

    Building System Models

    To build system models, the revised influencediagrams were converted into Bayesian BeliefNetworks (BBN) (Jensen, 2001) using NeticaTM

    software (Norsys Software Corporation, 1998).Figure 6 is an unpopulated section of theimplementation model, illustrating the depen-dency of Burn Condition Information Deficien-cies on the availability of skills & equipment toassess fuel characteristics and weather data toassess conditions. The probabilities that BurnCondition Information Deficiencies will be in thestateRarely, Sometimes or Frequently are stored in acondition probability table. The empty templatefor this table is given in Table 2. The rows inTable 2 are scenarios constructed from differentcombinations of the input variables. Hence, thefirst row stores the probabilities that BurnCondition Information Deficiencies will be inthe state Rarely, Sometimes or Frequently givenHave Ski...