The use of Probability Bounds Analysis for
chracterising and propagating uncertainty in
species sensitivity distributions
Fact sheet - April 2008
Project Background
Salinity presents a major threat to Victoria’s natural
resources, with the area of land affected expected to
double by 2020. A key stage in managing the effects of
increasing salinity is predicting which plants and animals
are likely to be impacted and where this may occur.
A team of researchers from DSE is developing new
approaches to modelling the biological risks of salinity.
Funded by the National Action Plan for Salinity and Water
Quality and the National Heritage Trust, this project aims
to develop decision-frameworks and modelling tools for
risk assessments, provide case studies and examples of
the application of these models, and develop linkages
with similar projects.
Applications
The modelling and decision frameworks developed have
a number of applications:
•
Determining how best to allocate resources for
protecting biodiversity assets from salinity;
•
Derivation of target salinity levels for setting
objectives and environmental standards;
•
Comparison of the relative risks of effects between
different situations or processes;
•
Identification of priority
management works;
•
Estimation of the risks of effects on sensitive or
vulnerable taxa.
One useful approach to predicting the effects of salinity is
to construct a Species Sensitivity Distribution (SSD),
which compares the proportion of species affected by
different salt concentrations (Fig 1). These distributions
can then be used to compare for different places or
scenarios in order to prioritise management actions and
determine the chance of effects.
This study involved the development of new approaches
to SSD modelling using Probability Bounds Analysis.
These approaches were then tested on a number of case
studies.
for
salinity
Proportion of species affected
1
Uncertainty and Probability Bounds Analysis
Uncertainty in ecotoxicological risks assessments arises
from a multiple sources including natural variability in the
sensitivity of organisms, confounding and modifying
factors, systematic measurement error and extrapolation
across spatial and temporal scales. While some previous
SSD approaches have incorporated limited treatment of
uncertainty, alternative methods are available that
characterise and propagate uncertainty more effectively.
areas
0.5
0
0
10000
Salinity (mg/L)
Figure 1. Species Sensitivity Distribution
(black), uncertainty (red) and exposure
distribution (blue) models.
Reference
Dixon W. J. (2007) The use of probability bounds analysis
for characterising and propagating uncertainty in species
sensitivity distributions. Arthur Rylah Institute for
Environmental Research, Technical Report Series No.
163. Department of Sustainability and Environment,
Melbourne, Australia.
Published by the Victorian Government Department of Sustainability and Environment, Melbourne, April 2008
© The State of Victoria Department of Sustainability and Environment 2008
This publication is copyright. No part may be reproduced by any process except in accordance with the provisions of the Copyright Act 1968.
Authorised by the Victorian Government, 8 Nicholson Street, East Melbourne.
ISBN 978-1-74208
For more information contact the DSE Customer Service Centre 136 186 or Dr Bill Dixon at research.ari@dse.vic.gov.au, Arthur Rylah Institute, Department of Sustainability and Environment, PO Box
137, Heidelberg 3084.
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