Eliciting Expert Knowledge in Conservation Science

By Tara G. Martin, Mark A. Burgman, Fiona Fidler, Petra M. Kuhnert, S. Low-Choy, M. McBride, Kerrie L. Mengersen. Conservation Biology, 2012

794 citations recorded by the source corpus.

Subfields: Conservation, Geography, Planning and Development, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/681f6e6f0f754c5f6a73a2eafb05c564ca4ae235

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In Conservation

W. J. Sutherland

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Citations in the corpus

Structured elicitation of expert judgments for threatened species assessment: a case study on a continental scale using email (2012, background): “Within ecology, the uptake of structured methods has been gaining traction (see Choy, O’Leary & Mengersen 2009; Kuhnert,Martin&Griffiths 2010; Burgman et al. 2011a;Martin et al. 2012 for recent reviews).”
A comparison of threats, vulnerabilities and management approaches in global seagrass bioregions (2012, background): “Expert knowledge is fundamental to conservation decision making (Burgman 2005, Martin et al 2012), especially when actions are required before uncertainties can be resolved (Sutherland 2006, McBride and Burgman 2011).”
How do you find the green sheep? A critical review of the use of remotely sensed imagery to detect and count animals (2018, methodology): “Expert judgement can also improve automated techniques by, e.g. identifying sources of systematic error (Burgman, 2015; Martin et al., 2012; McBride & Burgman, 2012).”
The Delphi technique in ecology and biological conservation: applications and guidelines (2015, background): “…has evolved considerably and is considered particularly suitable for addressing multifaceted issues, especially when information is limited or conflicting (O’Faircheallaigh 2010; Martin et al. 2012; McBride et al. 2012) or for combining different types of evidence (Sutherland et al. 2013).”
The magnitude of global marine species diversity. (2012, background): “Estimates can be substantially improved by combining empirical data with expert judgment [19].”
Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses (2019, background): “σσαα ~ Cauchy(0, ν) ∈ [0,∞] This prior is as above for regression and ANOVA models. McElreath (2015) suggests setting ν = 1.”
Levers and leverage points for pathways to sustainability (2020, background): “Clearly, limitations remain, and such processes are constrained by the underlying knowledge being assessed (Martin et al., 2012).”
Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist (2017, positive): “…can ask an expert, or a panel of experts, to provide an estimate for the hyperparameters based on knowledge of the field; see, for example: Bijak and Wisniowski (2010); Fransman et al. (2011); Howard, Maxwell, and Fleming (2000); Martin et al. (2012); and Morris, Oakley, and Crowe (2014).”
A practical guide to structured expert elicitation using the IDEA protocol (2018, background): “These include difficulties experts face in expressing judgements in quantitative terms (Martin et al., 2012a), the cost and logistics associated with faceto- face elicitations of more than one or two experts (Knol et al., 2010; Kuhnert et al., 2010), as well as challenges experienced by experts in…”
Quantifying extinction risk and forecasting the number of impending Australian bird and mammal extinctions (2018, background): “Thus, integrating the opinions of multiple experts is essentially an exercise in model averaging (Symonds and Moussalli 2011) and produces better results than can be obtained from a single expert (Martin et al. 2012).”
The spectre of uncertainty in management of exploited fish stocks: The illustrative case of Atlantic bluefin tuna (2014, neutral): “stakeholders [49, 50].”
Linking Behavior to Vital Rates to Measure the Effects of Non‐Lethal Disturbance on Wildlife (2015, positive): “Some links in our mechanistic framework (Figure 1) might not be directly quantifiable and may only be possible to inform using expert elicitation methods (Martin et al. 2012).”
Stop Jumping the Gun: A Call for Evidence‐Based Invasive Predator Management (2017, methodology): “Two tools that are increasingly being used to inform decision making in conservation science and are particularly well suited to a “learning by doing” approach are expert elicitation (Martin et al. 2012) and Bayesian networks (BNs) (Nyberg & Marcot 2006).”
An overview of methods to evaluate uncertainty of deterministic models in decision support (2015, neutral): “Of these approaches, using field-observation data is in many cases straightforward, and expert elicitation has been covered by excellent reviews, e.g. by O'Hagan (2012) and Martin et al. (2012).”
Understanding and predicting the combined effects of climate change and land‐use change on freshwater macroinvertebrates and fish (2014, methodology): “…and best practice when observed data thresholds are absent (e.g. Bashari, Smith & Bosch 2008; Choy, O’Leary & Mengersen 2009) and expert judgment is a credible source of information for ecological applications and conservation management (e.g. Choy, O’Leary & Mengersen 2009; Martin et al. 2012).”
Mental models for conservation research and practice (2019, methodology): “In a BBN, the links between the model variables, or nodes, are elicited using probabilities that are typically derived using expert elicitation (Martin et al., 2012), but can also be obtained through methods such as the four-step Delphi method (see McBride et al., 2012).”