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

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.