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. 2018 Jan 23;8(1):1469.
doi: 10.1038/s41598-018-19354-6.

Capturing expert uncertainty in spatial cumulative impact assessments

Alice R Jones  1 , Zoë A Doubleday  2 , Thomas A A Prowse  2   3 , Kathryn H Wiltshire  4 , Marty R Deveney  4 , Tim Ward  4 , Sally L Scrivens  2 , Phillip Cassey  2 , Laura G O'Connell  5   6 , Bronwyn M Gillanders  7
Affiliations

Capturing expert uncertainty in spatial cumulative impact assessments

Alice R Jones et al. Sci Rep. .

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.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Schematic of framework for calculating cumulative impact and accounting for ‘knowledge-based uncertainty’. Experts were asked to give ‘effect scores’ (between 0–8) for each combination of threat (in this example it is prawn trawl fishing) and ecosystem (here subtidal soft sediment), and for three uncertainty scenarios:’best-case’, ‘most-likely’ and ‘worst-case’. These effect scores were then used in a calculation of impact that accounts for the spatial intensity of each threat and the locations of overlap between a threat and an ecosystem. This process was repeated for each threat that occurs to each ecosystem and the resulting impact layers were summed to generate ecosystem-specific cumulative impact maps. This entire process was carried out for all eight ecosystems, and was repeated three times, once for each uncertainty scenario, to account for the experts’ ‘knowledge-based uncertainty’ around the cumulative impact scores. Maps were produced using R statistical software (version 3.3.1; https://www.r-project.org) and the packages raster, rgdal, sp and rasterVis.
Figure 2
Figure 2
Density curves of the ‘most-likely’ (unscaled) cumulative impact score for each ecosystem in Spencer Gulf. The curves are generated using the cumulative impact scores from all grid cells classified as each ecosystem type.
Figure 3
Figure 3
(a) Overview map of Australia with Spencer Gulf region shown by rectangle. Scaled cumulative impact maps for (b) benthic and (c) pelagic ecosystems in Spencer Gulf, based on the ‘most-likely’ scenario expert scores. Annotations: SG = Spencer Gulf, GSV = Gulf St Vincent, AD = Adelaide. Boxed areas in map (b) show location of population centres or industrial areas, finer-scale maps for these areas are supplied in the Supplementary material (Supplementary Figure S6). Maps were produced using R statistical software (version 3.3.1; https://www.r-project.org) and the packages raster, rgdal, sp and rasterVis.
Figure 4
Figure 4
Summary plots of spatial cumulative impact scores from simulations that accounted for knowledge-based uncertainty. For each of 1000 simulations some random error was added to the expert effect scores for each ecosystem and threat combination and the adjusted scores were used to calculate cumulative impact. (a) Using the expert’s self-assessed uncertainty data, simulated values were drawn from a beta distribution centred on the most-likely score and bounded by the best-case and worst-case scores (Eqs 4 and 5). For (b) and (c), simulated random errors were drawn from a uniform distribution based on (b) our expert self-assessed uncertainty method (Eq. 3) and (c) assumed uncertainty method proposed by Stock and Micheli (Eq. 4). In all panels the bold horizontal line shows the average cumulative impact score for each ecosystem class from 1000 simulations, box extents show the standard deviation of this average and vertical lines extend to the minimum and maximum average cumulative impact per ecosystem recorded across all 1000 simulations.
Figure 5
Figure 5
Maps showing the grid cells that were consistently the ‘most impacted’ (≥80th percentile, pink) or ‘least impacted’ (≤20th percentile, green) across all three effect score scenarios (best-case, most-likely and worst-case) for (a) benthic and (b) pelagic ecosystems. Tan coloured areas show pixels that were not consistently in either the upper or the lower percentile groups (i.e. had scores between the 20th and 80th percentile in at least one scoring scenario). Maps were produced using R statistical software (version 3.3.1; https://www.r-project.org) and the packages raster, rgdal, sp and rasterVis.
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