Utility of Expert‐Based Knowledge for Predicting Wildlife‐Vehicle Collisions

Hurley, Michael; Rapaport, Eric; Johnson, Chris J.

doi:10.2193/2008-136

Cited by 30 publications

(25 citation statements)

References 25 publications

(27 reference statements)

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“…For example, ecologists have used expert knowledge to provide the quantitative basis for predicting the location and quality of wildlife habitat (e.g., Johnson and Gillingham (2004): caribou habitat in British Columbia, Canada; Smith et al (2007): habitat of the ground dwelling Julia Creek dunnart in Queensland, Australia), species occurrences (e.g., Yamada et al (2003): sambar deer in Victoria, Australia; Rothlisberger et al (2009): fish stocks in the Great Lakes, North America), and species behavior (e.g., Hurley et al (2009): vehicle collisions with moose in British Columbia, Canada; Pullinger and Johnson (2010): caribou movement corridors in British Columbia, Canada).…”

Section: ) Supplementmentioning

confidence: 99%

“…Depending on the geographic specificity of the context, a research team may seek experts with knowledge of a particular region or area (Elbroch et al 2011), particularly when expert knowledge may not transcend sitespecific processes (Doswald et al 2007, Hurley et al 2009). For many studies, a range of expertise may be needed to address different phases of the work.…”

Section: Identifying Selecting Recruiting and Retaining Expertsmentioning

confidence: 99%

“…Each has strengths and weaknesses. For example, the analytical hierarchy process has often been used to solve applied ecological problems (e.g., Clevenger et al 2002, Doswald et al 2007, Hurley et al 2009) and allows formalization of the decision-making process, including relative scaling of parameters and checking of the logical consistency of values. Using this method, experts order a set of hypothesized categorical variables, which they may have collectively adjusted based on their expert knowledge.…”

Section: Identifying Selecting Recruiting and Retaining Expertsmentioning

confidence: 99%

“…However, these experts will not perfectly agree with each other and may even contradict one another, which can be referred to as source conflict (Smithson 1999). Sometimes this amongexpert uncertainty is acknowledged (Iglesias and Kothmann 1998, Drescher et al 2008, Hurley et al 2009), though most expert knowledge studies do not partition the different sources of uncertainty and only report aggregated expert knowledge. Nevertheless, among-expert disagreement can provide valuable information about the characteristics of the studied system (Uusitalo et al 2005), research needs (Morgan et al 2001) and about the nature of expert knowledge itself (Iglesias andKothmann 1998, Drescher et al 2008).…”

Section: Bias Uncertainty and Aggregation Of Expert Knowledgementioning

confidence: 99%

“…While conceptual problems about the meaning of group judgment persist (Garthwaite et al 2005), in practice, knowledge aggregation often has to occur for practical reasons and can be achieved in various ways (Clemen and Winkler 1999). However, only by maintaining discrete expert perspectives is it possible to fully understand uncertainty in expert knowledge and potentially partition that uncertainty based on parameters such as geography and institutional background (Doswald et al 2007, Hurley et al 2009, Kappel et al 2011, type of ecological process (Drescher and Perera 2010a) or degree of expertise (Fazey et al 2006, Czembor and Vesk 2009, Hurley et al 2009). This is not to say that group-based approaches for eliciting knowledge are necessarily ineffective or always generate biased information.…”

Section: Bias Uncertainty and Aggregation Of Expert Knowledgementioning

confidence: 99%

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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: ) Supplementmentioning

confidence: 99%

Section: Identifying Selecting Recruiting and Retaining Expertsmentioning

confidence: 99%

Section: Identifying Selecting Recruiting and Retaining Expertsmentioning

confidence: 99%

Section: Bias Uncertainty and Aggregation Of Expert Knowledgementioning

confidence: 99%

Section: Bias Uncertainty and Aggregation Of Expert Knowledgementioning

confidence: 99%

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Toward rigorous use of expert knowledge in ecological research

et al. 2013

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Fuzzy cognitive mapping as a tool to assess the relative cumulative effects of environmental stressors on an Arctic seabird population to identify conservation action and research priorities

Rooney

Daniel

Hedd

et al. 2023

Ecol Sol and Evidence

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In the Arctic, chemical contaminants, shipping, oil pollution, plastic pollution, changing habitats in relation to climate change and fisheries have been identified as environmental stressors to seabirds such as Fulmarus glacialis (northern fulmar; qaqulluk; ᖃᖁᓪᓗᖅ), but rarely have these stressors been considered within a cumulative effects framework in this species which is currently showing a declining populations trend. As a novel tool to understand cumulative effects within a conservation context, we applied a fuzzy cognitive mapping (FCM) approach that allows experts to arrange key factors and their interrelationships, organizing their understanding of the components of a complex issue into a graphical representation; a ‘cognitive map’. This process was grounded in local environment concerns as documented in several Nunavut‐specific reports and discussions, and worked with western‐trained seabird experts with knowledge of northern fulmar populations to assess the inter‐related environmental threats to fulmars as a way to combine these stressors in a cumulative effects framework and identify conservation actions and knowledge gaps. We found strong agreement that the main stressors affecting northern fulmar populations in Canada include pollution (11% total influence (TI)), shipping activities (16% TI), hunting and fishing (18% TI) and mining/oil and gas exploitation activities (22% TI). The indirect influence of threats on northern fulmar population size (57% TI) exceeded the total direct influence (43% TI), emphasizing the value of cognitive mapping in cumulative effects assessment for a more holistic understanding of interacting stressors. Participants expressed substantial uncertainty regarding the strong relationships leading from the concepts, commercial fishing activity in the BBDS and the North Atlantic fisheries activity, indicating that these potential stressors require more research. Similarly, uncertainty was expressed about the potential effects of zodiac traffic, ship strikes of northern fulmar, number of oil spills and magnitude of oil spills on northern fulmar. By characterizing individual factors as manageable or not, we determined that stressors are largely manageable with enforcement of existing policies (58% TI)—importantly, fishing activities were both highly influential on fulmars and deemed manageable, which will inform ongoing co‐management planning in the region.

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A simple framework for a complex problem? Predicting wildlife–vehicle collisions

Visintin

Ree

McCarthy

2016

Ecology and Evolution

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SummaryCollisions of vehicles with wildlife kill and injure animals and are also a risk to vehicle occupants, but preventing these collisions is challenging. Surveys to identify problem areas are expensive and logistically difficult. Computer modeling has identified correlates of collisions, yet these can be difficult for managers to interpret in a way that will help them reduce collision risk. We introduce a novel method to predict collision risk by modeling hazard (presence and movement of vehicles) and exposure (animal presence) across geographic space. To estimate the hazard, we predict relative traffic volume and speed along road segments across southeastern Australia using regression models based on human demographic variables. We model exposure by predicting suitable habitat for our case study species (Eastern Grey Kangaroo Macropus giganteus) based on existing fauna survey records and geographic and climatic variables. Records of reported kangaroo–vehicle collisions are used to investigate how these factors collectively contribute to collision risk. The species occurrence (exposure) model generated plausible predictions across the study area, reducing the null deviance by 30.4%. The vehicle (hazard) models explained 54.7% variance in the traffic volume data and 58.7% in the traffic speed data. Using these as predictors of collision risk explained 23.7% of the deviance in incidence of collisions. Discrimination ability of the model was good when predicting to an independent dataset. The research demonstrates that collision risks can be modeled across geographic space with a conceptual analytical framework using existing sources of data, reducing the need for expensive or time‐consuming field data collection. The framework is novel because it disentangles natural and anthropogenic effects on the likelihood of wildlife–vehicle collisions by representing hazard and exposure with separate, tunable submodels.

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Utility of Expert‐Based Knowledge for Predicting Wildlife‐Vehicle Collisions

Cited by 30 publications

References 25 publications

Toward rigorous use of expert knowledge in ecological research

Toward rigorous use of expert knowledge in ecological research

Fuzzy cognitive mapping as a tool to assess the relative cumulative effects of environmental stressors on an Arctic seabird population to identify conservation action and research priorities

A simple framework for a complex problem? Predicting wildlife–vehicle collisions

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