Capturing expert uncertainty in spatial cumulative impact assessments

Abstract

Understanding the spatial distribution of human impacts on marine environments is necessary for maintaining healthy ecosystems and supporting ‘blue economies’. Realistic assessments of impact must consider the cumulative impacts of multiple, coincident threats and the differing vulnerabilities of ecosystems to these threats. Expert knowledge is often used to assess impact in marine ecosystems because empirical data are lacking; however, this introduces uncertainty into the results. As part of a spatial cumulative impact assessment for Spencer Gulf, South Australia, we asked experts to estimate score ranges (best-case, most-likely and worst-case), which accounted for their uncertainty about the effect of 32 threats on eight ecosystems. Expert scores were combined with data on the spatial pattern and intensity of threats to generate cumulative impact maps based on each of the three scoring scenarios, as well as simulations and maps of uncertainty. We compared our method, which explicitly accounts for the experts’ knowledge-based uncertainty, with other approaches and found that it provides smaller uncertainty bounds, leading to more constrained assessment results. Collecting these additional data on experts’ knowledge-based uncertainty provides transparency and simplifies interpretation of the outputs from spatial cumulative impact assessments, facilitating their application for sustainable resource management and conservation.

Uncertainty analysis and robust areas of high and low modeled human impact on the global oceans

“…Although more recent regional-scale studies modeled sensitivity weight errors based on the expert judgment process itself (Doubleday et al 2017;Gissi et al 2017;Jones et al 2018), the range and distribution of such errors for the global map of human impact on marine ecosystems remain unknown, and we used the rather crude uniform error distribution assumed by Stock and Micheli (2016). In a study of Spencer Gulf (Australia), Jones et al (2018) compared the effects of assuming this uniform distribution to a beta distribution derived from experts' best case, worst case, and most likely estimates of weights. They found that the assumed uniform distribution of Stock and Micheli (2016) resulted in an overestimation of the effect of the sensitivity weights compared with the beta distributions.…”

“…There is concern about the accuracy of sensitivity weights derived from expert surveys (Halpern & Fujita 2013). Although recent studies designed expert surveys that allow the estimation of error distributions for sensitivity weights (Doubleday et al 2017;Gissi et al 2017;Jones et al 2018), no estimate of the distribution of sensitivity weight errors was available for the global human impact map. In each generated map, we thus followed Stock and Micheli (2016) and added random errors drawn from a uniform distribution bounded by ±2 units.…”

Where the Ecological Gaps Remain, a Modelers' Perspective

“…The need for clear conceptual frameworks to support clear thinking around cumulative, non-linear and interacting effects grows larger once the dimensions (and scales) of the cumulative effects are expanded. This is evident in the growing interest in a pragmatic means of assessing cumulative impacts of human activities and other stressors on ecosystems (Giakoumi et al, 2015;Holsman et al, 2017;Jones et al, 2018;Stelzenmüller et al, 2018). Despite these limitations, the importance of linking human and ecological processes to predict future dynamics has been recognized for some time-e.g., in urban and agricultural systems (Alberti, 2008)-with advances achieved using agentbased models that couple socio-demographic, ecological, and biophysical models (e.g., Filatova et al, 2013;Fulton et al, 2015).…”

Combined effects of human pressures on Europe’s marine ecosystems

“…A specific CEA may evaluate how several human activities or pressures can act together on the same environment or visualize the combined effect when one pressure occurs simultaneously in many places over a larger area, hence providing guidance to targeted management actions (Stelzenmüller et al 2018). CEAs have been carried out for several marine areas and globally (reviewed by Korpinen and Andersen 2016) and methods evolve continuously with regard to assessment approaches and data quality aspects (e.g., Stelzenmüller et al 2015;Stock and Micheli 2016;Jones et al 2018;Hodgson et al 2019). Despite advances in application and computation, a major limiting factor in spatially referenced CEA is still the availability and accuracy of data (Stelzenmüller et al 2015).…”