Using expert judgment to estimate marine ecosystem vulnerability in the California Current

Abstract

As resource management and conservation efforts move toward multi-sector, ecosystem-based approaches, we need methods for comparing the varying responses of ecosystems to the impacts of human activities in order to prioritize management efforts, allocate limited resources, and understand cumulative effects. Given the number and variety of human activities affecting ecosystems, relatively few empirical studies are adequately comprehensive to inform these decisions. Consequently, management often turns to expert judgment for information. Drawing on methods from decision science, we offer a method for eliciting expert judgment to (1) quantitatively estimate the relative vulnerability of ecosystems to stressors, (2) help prioritize the management of stressors across multiple ecosystems, (3) evaluate how experts give weight to different criteria to characterize vulnerability of ecosystems to anthropogenic stressors, and (4) identify key knowledge gaps. We applied this method to the California Current region in order to evaluate the relative vulnerability of 19 marine ecosystems to 53 stressors associated with human activities, based on surveys from 107 experts. When judging the relative vulnerability of ecosystems to stressors, we found that experts primarily considered two criteria: the ecosystem's resistance to the stressor and the number of species or trophic levels affected. Four intertidal ecosystems (mudflat, beach, salt marsh, and rocky intertidal) were judged most vulnerable to the suite of human activities evaluated here. The highest vulnerability rankings for coastal ecosystems were invasive species, ocean acidification, sea temperature change, sea level rise, and habitat alteration from coastal engineering, while offshore ecosystems were assessed to be most vulnerable to ocean acidification, demersal destructive fishing, and shipwrecks. These results provide a quantitative, transparent, and repeatable assessment of relative vulnerability across ecosystems to any ongoing or emerging human activity. Combining these results with data on the spatial distribution and intensity of human activities provides a systematic foundation for ecosystem-based management.

A Model Approach for Estimating Colony Size, Trends, and Habitat Associations of Burrow-Nesting Seabirds

“…Consistency between the top threats volunteered by experts and the top threats revealed using vulnerability scores from these same experts can be low (Halpern 2007, Teck et al 2010, Payne et al 1992Lichtenstein and Slovic 2006) and suggests the importance of a more quantitative approach. There are many approaches for addressing uncertainty (e.g., Teck 2010, Iannuzzi et al, 2009, Halpern et al 2007, Garthwaite et al 2005, Cooke and Goossens 2004, Morgan 2003, Cleaves 1994 and we suspect that a Bayesian belief network approach (e.g., Garthwaite et al 2005) Iannuzzi et al, 2009).…”

“…There are many approaches for addressing uncertainty (e.g., Teck 2010, Iannuzzi et al, 2009, Halpern et al 2007, Garthwaite et al 2005, Cooke and Goossens 2004, Morgan 2003, Cleaves 1994 and we suspect that a Bayesian belief network approach (e.g., Garthwaite et al 2005) Iannuzzi et al, 2009). o Integrate expert based threat ranking with quantitative information (e.g., Teck et al 2010Iannuzzi et al, 2009 to provide a systematic foundation for ecosystembased management • There are modeling approaches that help both identify and rank threats and are discussed in the Modeling section and the concluding paragraphs of this Chapter of the Update.…”

Characterizing the vulnerability of intertidal organisms in Olympic National Park to ocean acidification

“…The definition of these core elements, however, is not consistent across studies or ecosystems. In some instances, adaptive capacity and sensitivity are treated as the same value (Busch et al, 2016;Hare et al, 2016); in others, sensitivity is derived from habitat (Okey et al, 2015) or ecosystem (Teck et al, 2010) type. Exposure represents a similar issue and has been quantified through expert opinion , Regional Ocean Modeling Systems (ROMS) (Marshall et al, 2017), or some combination of both applied to a fixed area of ocean habitat (Halpern et al, 2008;Okey et al, 2015;Hodgson et al, 2016).…”

“…There is good reason to study organisms of high economic importance or anticipated sensitivity to better understand the overall impact of OA on marine systems, but there is also a need for resource managers to better understand the effects of OA for the specific regions they manage. Examples of place-based analyses exist for determining vulnerability derived from habitats (Okey et al, 2015), state delineated fisheries (Mathis et al, 2015), and marine ecosystems (Teck et al, 2010) to better forecast OA effects for a region of interest. These evaluations take into account details of local geography to identify factors that may compound inherent organism risk.…”

Critical review of methodology and application of risk ranking for prioritisation of food and feed related issues, on the basis of the size of anticipated health impact

“…A number of possible (modelling) approaches on how to construct, and possibly aggregate, individual preferences have been published (French et al, 2009;Lichtenstein and Slovic, 2006). A novel MCDA approach that employs discrete preference data and applies probabilistic inversion to quantify, with uncertainty, the weights in the model has been developed by Neslo (2011), and has been successfully applied to several fields, amongst them, nanotechnology enabled food products (Flari et al, 2011), environmental policy making (Teck et al, 2010), ranking preferences of regulatory actions to ensure safety of products and applications based on emerging sciences and technologies (Flari and Chaudhry, 2012), and ranking of emerging zoonoses .…”