DOI: 10.5061/dryad.wm37pvmsf
title: Data from: Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation

description: 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 (&gt;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. 
description: Software requirements: R and/or ArcGIS/QGIS
description: # 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 (&gt;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. ## Description of the data and
 file structure 8 raster files (.tif) are provided, including 2 species
 (marked as Aust or Paph in the title, for 4 Ecosystem services: 1\. = Food
 provision, 2. = Water Quality regulation, 3. = Nitrogen removal, 4. =
 Sediment stabilisation. Each raster file has 3 bands: Band 1 =
 Original/Baseline ES predictions Band 2 = Conservative ES prediction from
 the sensitivity analysis (as the bottom 5th percentile from the predicted
 range in the sensitivity analysis) Band 3 = Difference between Band 1 and
 Band 2 ## Sharing/Access information Original ES models were published in: