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Data from: Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation

Rullens, Vera1; Stephenson, Fabrice1; Townsend, Michael2; Lohrer, Andrew3; Hewitt, Judi4; Pilditch, Conrad1; Ellis, Joanne1

Published Mar 22, 2024 on Dryad. https://doi.org/10.5061/dryad.wm37pvmsf

Data files

Mar 22, 2024 version files 1.87 MB

Abstract

Spatial assessments of Ecosystem Services (ES) are increasingly used in environmental management and spatial planning, but rarely provide information on the accuracy of predictions. Uncertainty estimates are essential to allow for confidence in the quality and credibility of ES assessments to enable informed decision-making. In marine environments, the need for uncertainty assessments for ES is unparalleled as they are data scarce, poorly (spatially) defined, with complex interconnectivity of seascapes. This study illustrates the uncertainty associated with a principle-based method for ES modelling by accounting for model variability, data coverage, and uncertainty in thresholds and parameters. A sensitivity analysis was applied on ES models for marine bivalves (Austrovenus stutchburyi and Paphies australis) and their contribution to Food provision, Water quality regulation, Nitrogen removal, and Sediment stabilisation. ES estimates from the sensitivity analysis were compared against baseline ES predictions. Spatial uncertainty patterns were analysed for individual ES through bi-plots and multiple ES through spatial prioritisation using Zonation. Results showed spatially explicit differences in uncertainty patterns for ES and between species. Food provision had highest maximum uncertainty (>5 points) but also the largest area of high ES and high certainty conditions. Zonation analysis conducted on baseline and conservative ES values showed overall robust outcomes of top 30% area, but important nuances through shifts in top 10% and top 5% area that allowed for a consistently better representation of ES when accounting for uncertainty. The spatial prioritisation in combination with the ES uncertainty biplots provide tools for spatial planning of individual and multiple ES to focus on area of highest value with highest certainty and can thereby help reduce risk and aid informed decision-making at acceptable confidence levels. This type of information is urgently needed in marine ES assessments and their management, but likewise extends to other environments to improve transparency.