Improving recovery planning for threatened species through Bayesian belief networks

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Improving recovery planning for threatened species through Bayesian belief networks Alejandro Ortega-Argueta* University of Queensland, School of Integrative Systems, Gatton Campus, Queensland 4343, Australia

A R T I C LE I N FO

A B S T R A C T

Keywords: Endangered species recovery Policy evaluation Program effectiveness Modeling Conservation planning

Threatened species management is a priority in global conservation. Despite many international and national initiatives, this strategy faces challenges posed by a wide range of institutional and organizational factors that influence planning and effective implementation. Empirical research of this issue is scarce given the complexity of addressing management issues. Systems analysis and participatory modeling were applied in this study to construct a conceptualization of a management system in the context of Australian governmental programs. This allowed examination of the structure, key elements and dynamics in order to address two research questions: a) which management factors have most influence on implementation of recovery planning? and b) what modifications could be made to improve recovery planning effectiveness? The methods employed comprised stakeholder interviews, expert workshops and qualitative and quantitative analyses to estimate management performance and effectiveness. The management system model was constructed using a Bayesian belief network to assess the most influential factors: a) coordination among federal, state and territory agencies, b) inconsistency of strategies and programs across jurisdictions, c) management of threatened species on private land, d) incorporation of science into recovery planning, e) prioritization schemes of conservation action and f) funding for plan implementation. Recovery planning effectiveness could be improved by establishing mandatory monitoring and review reports, creating a national forum on threatened species, designing an appropriate insurance regime for volunteers and establishing a national management information system.

1. Introduction Threatened species recovery is a global priority for major conservation programs (IUCN-SSC, 2017). Yet, the effectiveness of threatened species programs is rarely the subject of comprehensive appraisal, given the complexity of the hundreds of species involved, participation of multi-stakeholder groups and interaction with issues related to ecological, policy, institutional, managerial and sociopolitical aspects (Goble et al., 2006; Ferraro et al., 2007). According to some studies, the effectiveness of implementation of recovery planning may depend on legislative issues (Seabrook-Davison et al., 2010), decision-making processes (Troyer and Gerber, 2015), the structure and performance of the organization in charge (Eckerberg et al., 2015), leadership and communication (Martín-López et al., 2009) and financial constraints (Ferraro et al., 2007), among others. A summary of previous research showing the many aspects that influence threatened species recovery is included in online Appendix A. Within the complexity of threatened species management, the

production and exchange of technical information for decision-making are critical to guide policy. Research shows that technical information is inadequately adopted by policy makers for several reasons, including lack of political priority, restrictions to collaboration between scientists and managers, rushed decision-making and the preference of managers for their own judgments based on experience and values (Cook et al., 2010; Rose et al., 2018). This lack of information exchange between scientists and managers is known as the ‘research-implementation gap’ (Arlettaz et al., 2010). Information transfer is critical for various aspects of threatened species recovery, including planning, decision-making, funding allocation, project implementation and evaluation. Rather than a unidirectional path, information transfers may be conceived more accurately as a ‘space’, a process in which policy-makers demand knowledge and researchers, in collaboration with stakeholders, develop research to prove management hypotheses. Knowledge can be understood as a co-production of information among several stakeholders, influenced by values, cultural context, beliefs, mental models and experiences (Newell et al., 2014). Such processes of interaction, debate

⁎ Present address: Departamento de Conservación de la Biodiversidad, El Colegio de la Frontera Sur (ECOSUR), Unidad San Cristóbal, Carret. Panamericana y Periférico sur s/n, Barrio de María Auxiliadora, San Cristóbal de las Casas, Chiapas 29290, Mexico. E-mail address: [email protected].

https://doi.org/10.1016/j.biocon.2019.108320 Received 31 March 2019; Received in revised form 23 October 2019; Accepted 28 October 2019 0006-3207/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Alejandro Ortega-Argueta, Biological Conservation, https://doi.org/10.1016/j.biocon.2019.108320

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which institutional and organizational issues can be considered together in order to examine their relative influence on management outcomes, and to assess the effectiveness of management strategies. Two major methods were applied: systems analysis and participatory modeling (Checkland, 1981; Mahajan et al., 2019). The theoretical system model was based on assessment of major issues in order to understand the behavior of management systems. Participatory modeling assisted through consideration of different management scenarios in order to predict approaches that could produce improved conservation outcomes for threatened species recovery planning. These methods are explained sequentially in the following four sections:

and coproduction of practice-oriented knowledge are defined as research-implementation spaces (Toomey et al., 2016), the topic of this special issue. In this context, there is a lack of comprehensive evidence-based knowledge regarding the effectiveness of most conservation interventions (Bottrill and Pressey, 2012). Managers and conservationists are now much more aware of the need to evaluate the effectiveness of funding investments and program implementation (Laycock et al., 2009; Bottrill and Pressey, 2012). Improved program performance can only be achieved by a clear understanding of the nature of the organizations involved, as well as the policy and management processes (Henson et al., 2018; Scheele et al., 2018). Faced with this complexity, modeling has become an important tool for the appraisal and estimation of aspects that influence the process of threatened species recovery. Previous research involving modeling has used ecological information to predict population survival and extinction in specific species (e.g. VanderWerf et al., 2006), distribution or habitat (e.g. Smith et al., 2007) and the effects of threats (e.g. Pollino et al., 2007). Modeling has also been used to predict management scenarios that could guide decision-making (e.g. Darst et al., 2013). However, the focus has mainly been on ecological aspects, leaving managers with little understanding of management systems – the set of interlinked social and ecological components that influence the implementation of conservation programs (Bottrill and Pressey, 2012). Doubts remain in terms of how to incorporate institutional and organizational aspects into management scenarios and how these aspects could influence the implementation of plans for threatened species recovery. This article addresses the ‘research-implementation spaces’ through participatory assessments of the influence of institutional and organizational factors on the effectiveness of recovery planning. The study is framed in the context of Australian governmental programs. As one of the signatories of the Convention on Biological Diversity, Australia has undertaken efforts towards restoring its threatened biodiversity through recovery planning for more than 20 years. To date, research and independent reports in Australia have accounted for the limited effectiveness of the biodiversity strategies (Cresswell and Murphy, 2017), the extent of progress in improving threatened species status (Bottrill et al., 2011) and protection of critical habitat (Ward et al., 2019), as well as progress made in the abatement of major threats to biodiversity (Kearney et al., 2018). Among other studies, the recent State of the Environment Biodiversity Report (Cresswell and Murphy, 2017) has highlighted the importance of the policy dimension. As in other jurisdictions, recovery planning implementation in Australia is complicated by challenges related to limited institutional and financial capacities (Cresswell and Murphy, 2017), poor planning (Ortega-Argueta et al., 2011, 2017) and limited adoption of science in species listing, recovery planning and decisionmaking (McDonald et al., 2015). Given the continuous decline of many threatened species and the methodological issues that exist in terms of adequate evaluation of conservation planning, gauging the effectiveness of threatened species recovery planning is paramount for policy improvement. The aim of this study was to identify the most influential issues in threatened species recovery planning and to assess implementation in order to determine the most effective management strategies. I addressed two research questions: a) which management factors have most influence on implementation of recovery planning? and b) what modifications could be made to improve recovery planning effectiveness? Although the study was carried out in the specific context of governmental programs of Australia, the issues explored and lessons drawn are universal and potentially applicable across the international spectrum in support of conservation planning.

2.1. Developing a conceptual framework of management systems Systems thinking was selected as an appropriate approach for the assessment of conservation and management strategies, since it aims to help decision-makers resolve problems in complex socio-technical systems (Jackson, 2000; Bennet et al., 2016; Mahajan et al., 2019). In systems thinking theory, the management system is a set of interlinked components related by their common association with an entity that is subject to change through human intervention (Maani and Cavana, 2007). This approach was appropriate given that threatened species management represents a human-influenced system. The conceptual framework was constructed around a management system model where issues identified as having influence over recovery planning implementation were visualized as model components, and linked by cause-effect relationships to a system outcome. In this process, modeling helped to identify theoretically optimum management scenarios. Within the perspective of systems thinking, institutional and organizational factors were perceived as elements of the management system which are essential components of effective management (Ison et al., 1997; Jackson, 2000). The context of ‘institution’ was constructed around the notion of the established norms that govern a social system (Kaplan, 1960); in this case, the ‘rules’ or policies for threatened species management. The ‘organizational’ context was considered as the consciously coordinated social unit and its operative structures developed to deliver the institutional programs (Robbins and Judge, 2007). These two contexts were considered when appraising the influence of management factors on conservation strategies developed by agencies and other stakeholders. Given the scarcity of reliable information regarding the social aspects surrounding threatened species management, the model was developed using a participatory, constructive and iterative approach, enabling the participation and subjectivity of various stakeholders (Bana e Costa et al., 2000). This is particularly useful in circumstances where no purely objective procedure exists for investigating the socioecological aspects of management (Ban et al., 2013). Subjectivity must be acknowledged and incorporated into modeling in a transparent manner. Consequently, the model required the direct involvement of several stakeholders (see section 2.2 for details) in a strategic thinking process, in which issues, concepts and consequences were articulated, often based on influences such as tradition, values, beliefs and experience (Bana e Costa et al., 2000). The model conceptualization followed the ‘soft systems’ methodology, where a number of conceptual models are constructed for comparison with the real world, rather than a single model as in hard methodologies (Checkland and Scholes, 1990; Bryl et al., 2009). Soft systems are thus seen as mental constructs derived from different descriptions of reality that are transformed into conceptual models. These models are designed to provide participation and learning, rather than merely predictions, as is the case with hard models (Jackson, 2000; Bryl et al., 2009). Through modeling I aimed to assess factors that are difficult to measure in quantitative terms and that have often been overlooked by researchers and managers due to the absence of ‘hard data’. The model-building process arranged the components as a conceptual causal map or influence diagram. Causal maps are constructed

2. Methods Systems thinking and modeling offers a suitable framework in 2

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Fig. 1. Conceptual causal map (Jones et al., 2011; Moon et al., 2019) built with components (nodes) of issues (level I), strategies (level II) and requirements (level III) and their linkages (arrows) within the threatened species management system of Australia. Definition of components of the three levels is provided in the Appendix B. Author´s own production.

a computerized graphical model that encodes probabilistic relationships among variables of interest (Cain, 2001; Jensen and Nielsen, 2007). The BBN can integrate uncertainty in the knowledge associated with expert models by using cause-effect relationships represented as conditional probabilities (Jensen and Nielsen, 2007). Experts can specify their subjective beliefs in a probability distribution, prior to analysis of the model. For a detailed explanation of the process for building BBN, see Cain (2001) and Jensen and Nielsen (2007). The BBN approach fitted well with the research needs and qualitative data in terms of supporting modeling and decision-making under conditions of high uncertainty. BBN allows modeling of poorly understood complex systems and processes and facilitates examination of model structure and dynamics. This approach offered particular advantages, including a) accommodating stakeholder data regarding information that did not exist previously, b) accommodating multiple stakeholder opinions about the relationship between variables, (c) facilitating the integration of qualitative and quantitative variables and (d) furthering the understanding of a given problem domain and predicting the consequences of interventions and anticipated scenarios (Cain, 2001; Nadkarni and Shenoy, 2004; Pollino et al., 2007). Moreover, BBNs use a computer platform that allows graphic design of conceptual maps. This is of particular value since perceptions can be translated into explicit images that facilitate understanding and learning (Maani and Cavana, 2007). The computer platform for the BBN was Netica™ (Norsys, 2017).

graphs that represent the cause-effect relationships embedded in experts’ thinking (Nadkarni and Shenoy, 2004; Jones et al., 2011). Components of the model (nodes) were defined in three categories or levels, following a consecutive sequence: 1) key issues or barriers related to implementation of threatened species recovery planning; 2) key strategies necessary to address these issues; and 3) key requirements to enable the agencies/conservation organizations to carry out these strategies (Fig. 1). Definitions of model components are provided in Appendix B. The node at the bottom of the model was formed by the ultimate outcome of the management system: the effective implementation of recovery planning.

2.2. Identifying model variables Two separate stakeholder workshops were held to examine the major institutional and organizational factors that influence the implementation of threatened species recovery plans. In each workshop, a panel of ten stakeholders was formed in order to incorporate the different views regarding the management system and implementation of recovery planning. Information from two states (Queensland and New South Wales) was analyzed separately in each workshop in order to observe a likely specific context of recovery planning related to the existence of federal-state agreements, state-based legislation and local conservation strategies that may differ. Affiliations of each stakeholder panel is described in the Online Appendix B. Workshop attendees who agreed to participate signed an informed consent. All data was aggregated for analysis and individual details were eliminated in order to preserve informant confidentiality. This study was approved under the Ethical Research Committee of the University of Queensland. Information gathering from workshop participants followed an unstructured elicitation technique (modified from the Nominal Group technique), which allows inductive exploration of unfamiliar domains (Nadkarni and Shenoy, 2004). This technique facilitates the capture of the general knowledge and values of stakeholders concerning the domain under evaluation. Thus, identified components of the conceptual map may reflect elements encountered within the stakeholders’ views that ‘cross the line’ of institutional boundaries (Maani and Cavana, 2007). To produce the conceptual map of the model, stakeholders held a discussion that involved negotiation of meaning among partners (Roux et al., 2006; Biggs et al., 2011). This process may assist in gaining a broader but integrated perspective of the social and ecological aspects involved in threatened species management. The processes of elicitation and definition of the model components are included in online Appendix B. After eliciting the nodes and construction of the conceptual model (Fig. 1), these were incorporated into a Bayesian Belief Network (BBN),

2.3. Populating the model Two model maps were originally constructed, one from each state (Queensland and New South Wales), for comparison. While each model map represented a different context, they shared similar elements and structure. The two mental models reflected the framework, values and beliefs associated with the stakeholder´s worldviews (Biggs et al., 2011), and were similar in many respects. One way that shared mental models are developed is through iterations of interaction that enable the co-construction of a common representation of a given issue, creating a basis for shared understanding (Schusler et al., 2003). On this basis, the two model maps were combined into a single conceptualization which was sent to the individual stakeholders from workshops, who were then asked to revise the final model map through several iterations, refining the graphic structure and relationships in the model. The next phase was elicitation of probabilities for the BBN, through individual face-to-face interviews, with eight stakeholders from the workshops (four from each state). Equal representation of all different affiliations was sought among the participants. They provided opinions about the relative importance of model components (probabilities) and 3

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insurance regime, (4) resource kits, (5) incorporation into the curriculum, (6) teacher training and (7) a national forum on threatened species. These additions to the management system are represented in the model as grey colored nodes (Fig. 2).

their likely influence on implementation of recovery planning. The model calculates a numerical score that represents the probability of successful implementation given the influential factors. Thus, each stakeholder was asked to provide probabilities of effective implementation, given the influences of key issues and strategies (see the online Appendix B for details). Consensus of opinions is not required (Cain, 2001) since differences were resolved by equally weighting each stakeholder’s probability estimate using the geometric mean of all estimates as the final value (Bosworth et al., 1999).

3.2. Implementation drivers of most influence Sensitivity analysis helped with ranking key components of the model in order of importance as influential drivers of the effective implementation of recovery planning. The rank order expressed for issues was: 1) legislation and policy, 2) resource allocation, 3) community engagement and 4) scientific input. In the next level, analysis assessed the relative influence of all fifteen strategies together. Government revenue, allocation mechanism, adaptive approach, national strategic planning and incentives were the five most influential strategies of the management system (Table 1). The results showed that the strategies have little individual impact (average of 0.6 % per strategy) in terms of increasing the probability of effective implementation. However, the cumulative effect of various improved strategies together was a 10 % higher probability of effective implementation, relative to the current scenario (Fig. 3). Improved strategy planning could consider all alternative interventions that would act to achieve the best management outcomes. Fig. 3 shows that the maximum expected improvement in the probability of effective recovery plan implementation is 47 %, which is nearly 10 % above the current ‘real world’ management situation. Half of this maximum expected gain was achieved through improvement of the first five strategies, comprising one third of all proposed strategies.

2.4. Evaluating the model and assessing management scenarios The next step was assessment of several management scenarios, which was conducted by setting different conditions for the model given certain arrangements of variables and measuring the resulting changes in the output node (Cain, 2001). Three potential management scenarios were established for the estimates: (1) the current management condition, (2) a management condition improved by 50 % and (3) a management condition improved by 100 % (see the online Appendix B for details). The current management condition was established with the acknowledgment that some governmental and non-governmental programs and strategies are in place, and were already contained in the model. The two improved scenarios were estimated on the assumptions of enhanced institutional and organizational capacities of the agencies and organizations involved. The model not only assisted with examination of the effects of change in the system but also indicated the causes of these changes. The model does not make decisions, but presents information in the form of a diagram and its outcomes that explicitly illustrates the effect of a given choice (Cain, 2001). Sensitivity analysis was a final step and assisted in identification of the relative influence of model components on the outcomes (Turban et al., 2005). These analyses were conducted for each of the four subsystems of the model (mutual information method), aiming to assess the relative importance of the strategies in order to identify the most influential. The sensitivity analysis methods employed are explained in the online Appendix C.

3.3. Optimum conservation and management strategies Table 2 summarizes the theoretically optimum management strategies for improving threatened species recovery planning. Mandatory monitoring and review reports are highlighted as the only strategy currently absent from the current management system. 3.4. Predicting management scenario outcomes

3. Results Analyzing the stakeholder estimates, the probability for effective implementation of recovery planning under the current scenario was 37.4 % (all strategies at their current condition, which was the worstcase scenario). After changing the fifteen strategies to a management condition improved by 50 %, the probability of effective implementation increased to 42.8 %. When the strategies were improved by 100 %, the probability of effective implementation reached 59.9 %. An assessment to separately test the effect of expert opinions is included in online Appendix F. The analyses indicated that the management system cannot be improved by isolated interventions, but rather requires a combination of several strategies to increase the effectiveness of recovery planning implementation. Recovery strategies implemented in an isolated manner achieved only a minimal improvement in the outcomes. The results also showed that the key strategies for improving recovery planning are related to the four subsystems, as observed in the top ten ranked strategies (Table 1). It was also observed that the most necessary management interventions not only relate to management at the governmental level (e.g. improving government revenue and establishing a national strategic planning), but also to the participation of landholders and local communities (e.g. improving incentives), and to enhancing the incorporation of knowledge and feedback (e.g. adaptive approach).

3.1. The threatened species management model Four major issues were identified in the management system: (1) scientific input, (2) resource allocation, (3) community engagement and (4) legislation and policy. These major issues were incorporated into the model forming four subsystems (branches) in which strategies and requirements were associated, all around the central outcome node (Fig. 2). Definitions of the model components are provided in online Appendix D. With the support of workshop participants, I defined a final model structure comprising forty-eight nodes: one outcome node, four issues nodes, fifteen strategy nodes and twenty-eight requirements nodes. According to the workshop participants, the model mostly contained components (85 % of nodes) already in operation in the ‘real-world’ of Australian governmental management. The model therefore included issues, strategies and requirements that are already featured in current Federal and Queensland and New South Wales state programs (Fig. 2). In online Appendix E, I show separate model maps produced to facilitate a closer visualization of four subsystems. According to stakeholders, all fifteen proposed strategies are currently in operation, at different levels, within the federal and state agencies. New elements in the model (15 % of nodes) were added exclusively at the layer of requirements. Twenty-one requirements are being already used by the agencies (75 % of total proposed requirements). However, seven new requirements (25 %) were identified as necessary to improve management: (1) a monitoring and evaluation system, (2) national management information system, (3) appropriate

4. Discussion 4.1. Managerial aspects of threatened species recovery This study presents information on how to address the policy 4

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Fig. 2. Bayesian belief network of the management system for threatened species recovery planning in Australia. Nodes contain the issues, strategies and requirements necessary for improving the effectiveness of recovery plans. Grey-colored boxes are the proposed new components of the existing management programs of the federal and state agencies (Queensland and New South Wales). Author´s own production. Table 1 Ranking of proposed recovery strategies by sensitivity analysis based on their relative influence on the implementation of recovery planning of threatened species in Australia. Author´s own production. Recovery strategies

Mutual information

Rank

Government revenue Allocation mechanism Adaptive approach National strategic planning Incentives Non-government revenue Mandatory monitoring reports Inter-state consistency Targeted research and monitoring Enforceability Integration of science Other financial sources Project collaboration Awareness programs School programs

0.00014 0.00011 0.0001 0.00008 0.00006 0.00005 0.00005 0.00004 0.00004 0.00004 0.00003 0.00003 0.00002 0.00001 0.00001

1 2 3 4 5 6 6 7 7 7 8 8 9 10 10

Fig. 3. Cumulative impact of strategies on the probability (%) of effective implementation of recovery planning. Strategies are accommodated in order of rank as expressed in Table 1. The curve starts at a probability of 37.4 %, where all strategies were at the current management condition. The most influential strategies have greater proximity to the Y-axis: 1) government revenue, 2) allocation mechanism, 3) adaptive approach, 4) national strategic planning, 5) incentives, 6) non-government revenue, 7) mandatory monitoring reports, 8) inter-state consistency, 9) targeted research & monitoring, 10) enforceability, 11) integration of science, 12) other financial sources, 13) project collaboration, 14) awareness programs and 15) school programs. The growth in impact reached the asymptote by 15 strategies.

dimension of threatened species management via a modeling exercise with stakeholder participation. This approach provided potential solutions to many of the challenges faced in conservation planning, such as the involvement and consultation of stakeholders, anticipation of the effects of a suite of interventions and consideration of jurisdictions responsible for implementation, among others (Bottrill and Pressey, 2012; Adams et al., 2018). The process of model coproduction and stakeholder discussion of the most critical issues clearly identified management directions that could support decision-making and planning. This was conducted through validating the existing conservation strategies currently employed by the federal and state governments, as well as the addition of new strategies for adoption by managers. In this way, this study explored the ‘research-implementation spaces’ in the real context of the main policy instruments that govern the threatened species management in Australia. The stakeholder workshops helped to integrate a great variety of perspectives and to establish a starting point for understanding the complexity of planning processes. This is highlighted since there is often disagreement regarding how to define the effectiveness of

conservation planning (Bottrill and Pressey, 2012). Managers and experts could thus adopt a similar exercise to assess the anticipated effects of their conservation strategies and to evaluate their effectiveness. Improvement in management response is a necessity, given the fact that the impact of most pressures is high and increasing, and the overall status of biodiversity is in decline (Cresswell and Murphy, 2017; Kearney et al., 2018). The policy aspects of conservation are rarely recognized in research and even less research has been translated into policy-making (Toomey et al., 2016). While some studies have been conducted on the institutional and organizational dimensions of threatened species management (e.g., Wallace, 2003; Ruckelshaus and Darm, 2006), scant attention has been paid to participatory modeling approaches in the evaluation of management effectiveness. This study revealed that there are existing management procedures 5

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Table 2 Summary of issues and theoretically optimum management strategies for improvement of threatened species recovery planning, according to experts from Queensland and New South Wales in Australia. Items in bold are the innovative strategies and requirements necessary to improve the existing national recovery planning scheme. Major issues 1. Legislation and policy

2. Resource allocation

3. Community engagement

4. Scientific input

Optimum recovery strategies

Necessary requirements/resources

the national scope of strategic planning • Improve mandatory monitoring and review reports • Establish inter-state consistency of conservation programs • Improve and instruments law enforcement • Enhance governmental revenue • Increase and make better use of priority and funding • Develop allocation mechanisms non-governmental revenue • Increase • Enhanced incentive mechanisms incorporation of the adaptive approach into • Improve recovery planning targeted research & monitoring of threatened • Establish species

and amend legislation regarding listing and recovery planning • Audit recovery plan guidelines • Improve • Establish a national forum on threatened species decision-support tools • Employ • Fundraising partnerships with public and private sectors • Cooperative public support for threatened species recovery • Increase conservation agreements • Private-land instruments for fostering conservation on private lands • Market-based insurance regime for volunteers involved in recovery • Appropriate activities a national management information system • Establish improved communication and information-sharing mechanisms to • Seek enhance scientist-manager dialogue • Knowledge-gap identification

et al., 2002; Bottrill and Pressey, 2012). Secondly, there was agreement among stakeholders that the management system requires a careful review of policy and management instruments and re-shaping of institutional structures in order to improve the allocation of resources and achieve greater participation of society, rather than merely investing more resources and effort into research. Such issues have also been highlighted in previous studies (Darst et al., 2013; Scheele et al., 2018). In the recovery plans of Australia, it was found that a substantial effort has been prescribed to research (33 % of actions) over other priority aspects of management, such as policy enforcement, community engagement and education (Ortega-Argueta et al., 2011). The reasons for this disconnection between management priorities and conservation action may be related to the fact that most management plans are prepared exclusively by ecological scientists who, given their training, often fail to consider the social dimensions of conservation and focus their attention exclusively on ecological aspects (Martin et al., 2012; Ban et al., 2013). This weakness has been highlighted by other studies (e.g., Knight et al., 2011; Catalano et al., 2019), that found conservation efforts suffering from an imbalance in knowledge and skills between ecological scientific research and social, organizational and value-related aspects. The analysis of managerial aspects also revealed the complexity of threatened species recovery, through observation of minimal variation in the individual influence of strategies on implementation. This means that no single strategy alone can cause a significant change in the effectiveness of recovery planning. Indeed, implementing a group of at least five strategies is necessary in order to achieve substantial changes. Another important finding is that the maximum expected probability of achieving effectiveness, following improvement of all fifteen strategies by 100 %, is around 47 % (Fig. 3). This remains a very low probability for effective implementation of recovery planning, and may reflect the inherent complexity of managing threatened species as evidenced in previous research (e.g. Bottrill et al., 2011; McDonald et al., 2015). Historically, the management capacity of Australian federal and state governments alone has been inadequate in the face of the magnitude of the problem, as observed by the negative trends of most threatened species and aggravation of ecological impacts (Cresswell and Murphy, 2017). Multi-agency coordination and collaboration with conservation organizations, community groups and other members of society are essential to support the efforts of governments and enhance the management capacity necessary to address the risk of extinction presented by hundreds of species. A national-scale monitoring and evaluation scheme of threatened species management remains a necessity, as stated in previous research and independent reports (e.g. Ortega-

and mechanisms, established by the federal and Queensland and New South Wales governments, that are still considered by stakeholders as valid for effective recovery planning implementation, as well as some innovative strategies and instruments that could act to improve the performance of management programs. This is an important finding that may help to revise current threatened species policies and management, and to make adjustments according to the proposed model and its outcomes. Nevertheless, several management gaps remain that require attention. The issues identified in the model that influence implementation coincide with those reported in previous studies in the Australian context and elsewhere (besides the non-conservation literature): a lack of systematic and consistent national-level information for most threatened species, threats and conservation actions, limited habitat designations, insufficient funding and enforcement, poor coordination of policy implementation across jurisdictions and at national scale, inadequate governance and legislative support, lack of monitoring and evaluation of major conservation programs, a need for greater public support and community engagement and strengthening the management of private lands, among others (e.g. Laurian et al., 2004; Ferraro et al., 2007; Mooers et al., 2010; Seabrook-Davison et al., 2010; Ortega-Argueta and Contreras-Hernández, 2013; McDonald et al., 2015; Kearney et al., 2018; Ward et al., 2019). Gaps identified in the model by stakeholders were addressed by the identification of newly formulated components. Considering the scarcity of resources and personnel within government agencies, the management system could be substantially improved with relatively few additions. Some considerations that should be taken into account are described below. Firstly, the management system shows the complexity of issues and interventions on which managers must make decisions. While the modeling exercise helped to predict scenarios and potential outcomes, in practice, managers have few possibilities to fully explore the interventions and their effectiveness. Decision-makers face challenges in terms of the effective allocation of scarce resources among the most valuable and cost-effective management activities (Brazill-Boast et al., 2018; Gerber et al., 2018). Selecting appropriate strategies and actions to achieve conservation can be complex (Salafsky et al., 2002), especially in this context where managerial aspects are obscure and make anticipation of decisions difficult (Ban et al., 2013). Transparency in decision-making can make a difference in terms of separating the right decisions from political discretion (Mooers et al., 2010). Through systems modeling, it was possible to anticipate, in a transparent manner, the impacts of key institutional and organizational factors that have been identified as important in terms of effective management (Wallace 6

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In order to reduce uncertainty and subjectivity, the model can be strengthened by consecutively revising performance and outcomes, and integrating lessons gleaned from practice (Nyberg et al., 2006; Newton et al., 2007). The modeling approach could therefore be translated into an iterative exercise where management hypotheses can be tested and more realistic scenarios can be explored in terms of developing new strategic programs and improving management interventions. Monitoring and evaluation are critical for effective management, and feedback from strategy implementation could be integrated into the model to correct weaknesses and reinforce recovery planning as part of an adaptive management cycle (Nyberg et al., 2006; Newton et al., 2007).

Argueta et al., 2011; Cresswell and Murphy, 2017). This gap may influence the inability of most management agencies to assess the effectiveness of conservation management investments (Cresswell and Murphy, 2017). One major aspect identified is the need for improved stakeholder participation in the policy cycle of threatened species management. Bayesian models represent an appropriate tool for incorporating participatory analysis (Bosch et al., 2007; Henriksen et al., 2007). Participation of stakeholders in a systematic process of collection, discussion and analysis of scenarios allows exploration of diverse perspectives and values, acknowledging uncertainty and building a shared understanding of management systems (Knight et al., 2019; Moon et al., 2019). Since many threatened species management programs require the establishment of collaborative arrangements between the government and other sectors (e.g. management on private and community lands), participatory management planning is important to foster dialogue, trust and knowledge coproduction, and to help conflict resolution and promote involvement for designing improved strategies, gaining policy legitimization and creating commitments for collaboration (Darst et al., 2013; Young et al., 2013).

5. Conclusions This research generated policy-relevant information, suggesting that the most influential institutional and organizational factors of recovery planning in Australia are (a) coordination among federal, state and territory agencies, (b) inconsistency across jurisdictions in terms of strategies and programs (c) management of threatened species on private land, (d) incorporation of science into recovery planning, (e) prioritization of conservation action schemes and (f) funding for implementation of plans. The national recovery planning strategy could be improved by (i) establishing mandatory monitoring and review reports, (ii) creating a national forum on threatened species, (iii) designing an appropriate insurance regime for volunteers and (iv) establishing a national management information system. Within the ‘research-implementation spaces’ regarding threatened species management, this study provides background information that could serve as a platform on which to construct improved theoretical models, test hypotheses and generate information about how management systems function and can be improved. Further research in this area should focus on refining the model through successive stakeholder workshops and incorporating new information and model components through adaptive management. Future research could also include investigating influential factors not currently included in the model and developing a better understanding of the interactions between factors. Modeling could also incorporate future scenarios and dynamics in the context of climate change. The exercise could be extended to other jurisdictions elsewhere to compare similar conceptual models and their behavior.

4.2. Applying modeling for threatened species management Bayesian modeling has been used to support decision-making, mostly on ecological parameters (e.g. Conroy et al., 2008); however, social factors have been poorly explored. In the field of conservation planning, little is known about the influence of managerial issues on the effectiveness of conservation programs (Catalano et al., 2019). Despite the extensive research on threatened species, managers and conservationists have little guidance in terms of translating research into meaningful policy-relevant knowledge. While the soft model constructed in this study did not provide reliable predictions, it did improve our understanding of the management system through the visual representation and analysis of its performance. In this sense, one major result was the construction of the model itself, which emerged not from a preconceived concept but from the expertise and diverse perspectives of stakeholders, in a knowledge coproduction process (Wyborn, 2015). The model was also not intended to provide directions for decisionmaking regarding threatened species management, but rather to provide insights about how the organizational and institutional factors may affect the effectiveness of recovery planning. In this regard, the modelers must acknowledge some limitations. First, models contain uncertainty, variation, and propagation of error within and among their variables (Marcot et al., 2006). In one sense, the spread of prior probabilities assigned to the nodes, represent variability or uncertainty (Toivonen et al., 2001). Bayesian models are easily constructed and are therefore double-edged, combining ease of creation, explanation and use with the potential for creating spurious or unreliable models (Marcot et al., 2006). Models require validation, from experts or stakeholders, ensuring that the nodes and relationships represented in the model structure are real and, at best, causal, credible and defensible. Peer review, testing and elimination of competing model structures and participatory validation are necessary steps during the model-construction phases. Another limitation of Bayesian modeling is that estimates of model outcomes are not hard data that can be used to accurately predict management scenarios. Instead, the model was constructed to provide a framework for comparison and learning from the experiences and different perspectives of managers and other stakeholders in a specific context (Checkland, 1981). While the group of experts provided information from real scenarios, given the subjective nature of the data obtained from the model, it cannot be independently validated. Moreover, the great number of biophysical and social variables that intervene in conservation planning make the use of controls unfeasible (Bottrill and Pressey, 2012).

Declaration of Competing Interest The author declare no conflict of interest. Acknowledgments My grateful thanks go to the workshop participants and interviewees who supported this research, including the staff of the Commonwealth Department of the Environment, the New South Wales Department of the Environment, the Queensland Environmental Protection Agency, the World Wildlife Fund Australia, the Wilderness Society and academics from the University of Queensland (UQ), the University of New South Wales and the Australian National University. An anonymity agreement prevents disclosure of their names. M. Hockings, G. Baxter, C. Smith and L. Sinai (UQ) assisted with research design, construction of the model and facilitating the workshops. Funding was provided by the National Council of Science and Technology of Mexico, the UQ and the Wildlife Preservation Society of Australia. I also thank my colleagues for their assistance and advice: M. Hockings, C. López, R. Alderete, M. Hernández (Ecosur), K. MacMillan and A. Hasan (Deep Cortex AI). My gratitude to the editors A. Knight, B. Maas and A. Toomey and two anonymous reviewers who provided constructive comments that improved the manuscript. 7

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