When experts disagree (and better science won’t help much): Using structured deliberations to support endangered species recovery planning

Gregory, Robin; Long, Graham; Colligan, Mary; Geiger, James G.; Laser, Melissa

doi:10.1016/j.jenvman.2012.03.001

Cited by 50 publications

(82 citation statements)

References 20 publications

(22 reference statements)

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“…For example, experts may be asked to identify the scope of a project, define project objectives, or develop management decisions or conservation actions based on their experience and institutional perspective (Orsi et al 2011). In such cases participant buy-in and collaborative action are the primary objectives and success of the process depends on effective interaction and communication (Gregory et al 2012).…”

Section: Identifying Selecting Recruiting and Retaining Expertsmentioning

confidence: 99%

Toward rigorous use of expert knowledge in ecological research

et al. 2013

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Abstract. Practicing ecologists who excel at their work (''experts'') hold a wealth of knowledge. This knowledge offers a wide range of opportunities for application in ecological research and natural resource decision-making. While experts are often consulted ad-hoc, their contributions are not widely acknowledged. These informal applications of expert knowledge lead to concerns about a lack of transparency and repeatability, causing distrust of this knowledge source in the scientific community. Here, we address these concerns with an exploration of the diversity of expert knowledge and of rigorous methods in its use. The effective use of expert knowledge hinges on an awareness of the spectrum of experts and their expertise, which varies by breadth of perspective and critical assessment. Also, experts express their knowledge in different forms depending on the degree of contextualization with other information. Careful matching of experts to application is therefore essential and has to go beyond a simple fitting of the expert to the knowledge domain. The standards for the collection and use of expert knowledge should be as rigorous as for empirical data. This involves knowing when it is appropriate to use expert knowledge and how to identify and select suitable experts. Further, it requires a careful plan for the collection, analysis and validation of the knowledge. The knowledge held by expert practitioners is too valuable to be ignored. But only when thorough methods are applied, can the application of expert knowledge be as valid as the use of empirical data. The responsibility for the effective and rigorous use of expert knowledge lies with the researchers.

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Section: Identifying Selecting Recruiting and Retaining Expertsmentioning

confidence: 99%

Toward rigorous use of expert knowledge in ecological research

et al. 2013

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“…However, as nicely outlined in a book by Gregory et al (2012a), it often suffices to structure the decision together with the stakeholders to clarify the trade-offs and find an agreement between the parties. This structuring process may then--but need not--be followed by a formal MCDA, whereby modeling and expert knowledge used to predict outcomes are combined with stakeholder preferences.…”

Section: Structured Decision-makingmentioning

confidence: 99%

“…In this paper, we focus on the first three steps of structured decision-making (SDM; Gregory et al 2012a) that are crucial in any decision, but are often neglected (e.g., Belton and Stewart 2003). The following steps are usually carried out (see textbooks, e.g., Belton and Stewart 2003;Clemen and Reilly 2001;Eisenführ et al 2010;Gregory et al 2012a;Keeney 1992;Keeney and Raiffa 1976): (1) clarify the decision context; (2) define objectives and attributes; (3) develop alternatives; (4) estimate consequences; (5) evaluate trade-offs and select alternatives (this is a combination of the decision makers' subjective preferences with the objective consequences of the alternatives); and (6) implement, monitor and review.…”

Section: Structured Decision-makingmentioning

confidence: 99%

“…The following steps are usually carried out (see textbooks, e.g., Belton and Stewart 2003;Clemen and Reilly 2001;Eisenführ et al 2010;Gregory et al 2012a;Keeney 1992;Keeney and Raiffa 1976): (1) clarify the decision context; (2) define objectives and attributes; (3) develop alternatives; (4) estimate consequences; (5) evaluate trade-offs and select alternatives (this is a combination of the decision makers' subjective preferences with the objective consequences of the alternatives); and (6) implement, monitor and review. Many applications of MCDA focus on estimating the consequences of decision alternatives (step 4) and evaluating the trade-offs to select best alternatives (step 5), while the initial structuring steps (1-3) are treated rather superficially.…”

Section: Structured Decision-makingmentioning

confidence: 99%

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Structured decision-making for sustainable water infrastructure planning and four future scenarios

Lienert

Scholten

Egger

et al. 2015

EURO Journal on Decision Processes

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Water supply and wastewater infrastructures are vital for human wellbeing and environmental protection; they adhere to the highest standards, are expensive and long-lived. Because they are also aging, substantial planning is required. Climate and socio-economic change create large planning uncertainties and simple projections of past developments are no longer adequate. This paper presents the initial phases of a structured decision-making (SDM) procedure which is designed to increase the sustainability of water infrastructure planning and includes various stakeholders in an exemplary Swiss case study. We evaluate the SDM approach critically based on stakeholder feedback, give general recommendations and provide ample material to make it applicable to other settings. We carried out 27 interviews and two stakeholder workshops. We identified important objectives for water infrastructure planning, including all three sustainability pillars and their respective attributes (indicators, benchmarks) to measure how well the objectives are achieved. We then created strategic decision alternatives, including ''business-as-usual'' upgrades of the central water supply and wastewater system as well as semi-to fully decentralized alternatives. To tackle future uncertainty, we developed four socio-demographic scenarios. We used these to test the robustness of decision alternatives in a later Multi-Attribute Utility Theory analysis. Additionally, J. Lienert (&) Á L. Scholten Á C. Egger Á M. Maurer we contribute to the topical discussion of combining scenario planning with multicriteria decision analysis and demonstrate how various scenarios can stimulate creativity when generating decision alternatives. Their internal consistency is ensured by rigorously specifying them using a strategy generation table. Our SDM procedure can be adapted to inform decisions about sustainable water infrastructures in other contexts.

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“…However, other decision problems linked to critical habitat identification may also benefit from the application of a structured decision-making framework. For instance, in the absence of a spatially-explicit population model to estimate the probability of persistence of a species under different management scenarios, a structured decision-making process can help select among a range of alternative population size and distribution targets that have different consequences for species persistence (Gregory et al 2012b). The use of a structured decision-making framework can also help improve the transparency of other potentially controversial critical habitat decisions, such as assessing the benefits or feasibility of critical habitat identification for a particular species, identifying species priorities for critical habitat identification, or determining which sites should be legally designated and protected.…”

Section: Application To Other Critical Habitat Decisionsmentioning

confidence: 99%

Identifying critical habitat for threatened species: concepts and challenges

Camaclang¹

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Critical habitat is defined scientifically as the subset of habitat necessary for the long-term persistence of a given species. Based on this definition, loss of any part of the critical habitat would result in extinction of the species. In the United States, Australia, and Canada, critical habitat of threatened species is protected, to various degrees, under endangered species legislation. Effective protection of critical habitat depends on it being identified accurately. Where there is potential for conflict with landowners and industry stakeholders, accurate critical habitat identifications are more defensible in court, and minimise the opportunity costs of protecting areas that may not be as beneficial to species persistence. However, obtaining the data required to accurately identify critical habitat can take up considerable time and resources that may otherwise be spent on conservation actions. At the same time, delaying protection of critical habitat to improve knowledge can result in further habitat loss. In this thesis, I review key concepts and challenges surrounding the identification of critical habitat, and develop decision tools to assist in deciding when and how to identify it.In Chapter 2, I present a systematic review of critical habitat documentation from the United States, Canada, and Australia to identify the types of data and criteria that have been used to identify critical habitat in the last decade. Contrary to scientific recommendations that long-term species persistence should be used as the criterion for identifying critical habitat, information about the location of species occurrences and particular habitat features were used instead to identify critical habitats for most of the species reviewed. Insufficient data and the desire to avoid potential opposition from landowners are likely to be the main reasons for the use of such approaches to critical habitat identification. Chapter 3 continues with an examination of the merits of the different criteria that could be used to inform critical habitat identification, and the types of errors associated with each. I also considered the potential consequences of the errors, and recommended that more explicit recognition of the potential for errors is important in minimising their negative consequences for species persistence.While the accuracy of critical habitat identification may be improved by collecting more data, delaying protection to do so may result in additional habitat loss if habitats are left unprotected in the meantime. In Chapter 4, I used an optimisation approach to examine this trade-off between the benefits of delaying protection to improve accuracy and the costs of additional habitat loss in the interim. I modelled the change in the proportion of habitat correctly identified over time as a function of both accuracy and habitat loss, and determined the optimal amount of time to spend iii learning that maximises this value. I found that at low rates of habitat loss, slow learning rates resulted in a longer optimal lea...

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When experts disagree (and better science won’t help much): Using structured deliberations to support endangered species recovery planning

Cited by 50 publications

References 20 publications

Toward rigorous use of expert knowledge in ecological research

Toward rigorous use of expert knowledge in ecological research

Structured decision-making for sustainable water infrastructure planning and four future scenarios

Identifying critical habitat for threatened species: concepts and challenges

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