1. Estimating the impacts of complementary measures on fish abundance in the Murray-Darling Basin
Sam Nicol, Martin Mallen-Cooper, Lee Baumgartner, Paul Brown, Danial Stratford, Angus Webb
Land and water

2. The Murray-Darling Basin
Drains 1/7 of Australia’s land area.
Contains the three longest rivers in Australia
Economy
Australia’s most important agriculture region
Produces 1/3 of our food
65% of all Australia’s irrigation farms: stone fruit; rice; cotton; grapes; hay; livestock; milk
In , irrigated agriculture worth $6.7 billion
Environment
30,000 wetlands and rivers
16 Ramsar-listed wetlands
367 birds
85 mammals
46 fish
53 frogs
149 reptiles
People
2 million people rely on the river for agriculture, tourism and other industries.
An additional 1.3 million people depend on its water resources, including Adelaide.
Cultural significance (both Aboriginal and European)
Sam Nicol et al. Complementary Measures

3. A river that needs help
Water resource development -> long-term ecological decline
Millennium drought ( ) -> $12.9 billion over 10 years for a national agreement on water use in the Murray-Darling Basin
Sam Nicol et al. Complementary Measures

4. The Murray Darling Basin Plan
Coordinated approach to water management across the Basin States
Determines how much water can be extracted without impacting natural environments.
Makes water available to the environment
Direct purchase of water entitlements or investment in improved infrastructure.
Sam Nicol et al. Complementary Measures

5. … But providing water alone isn’t enough
Major non-flow related threats to the basin’s flora and fauna include:
Habitat loss, degradation, fragmentation
Feral and pest animals
Weeds
Pollution and eutrophication
Inappropriate water delivery (temperature, timing, flow)
Sam Nicol et al. Complementary Measures

6. Complementary Measures
Non-flow based actions that help to achieve environmental outcomes of the Basin Plan
Examples:
Carp biocontrol
Screening of irrigation offtakes
Habitat restoration projects (e.g. re-snagging)
Cold water pollution mitigation
Fish stocking to re-establish populations
Fishways/fish ladders
Feral or pest control programs
Photo: Ian Cresswell
Sam Nicol et al. Complementary Measures

7. Which complementary measures are most beneficial?
How can we estimate relative ecological benefits of complementary measures?
Limited data, lots of expert experience.
Uncertainty is important
-> Bayesian Network
Sam Nicol et al. Complementary Measures

8. Defining “benefit”
Basin Plan has three ecological themes:
Native fish
Waterbirds
Native vegetation
an increase in diversity, population size, age classes, or distribution of native fish species; an increase in waterbird abundance, an increase in native vegetation condition or extent…
Sam Nicol et al. Complementary Measures

9. Functional groups: Native fish
Functional groups determined by hydraulics and spatial scale.
Habitat hydraulic conditions
Spatial scale of recruitment
Example species
Flowing water (lotic)
Meso (100m-99km)
Murray cod
Native fish
Macro ( km)
Golden perch
Micro (1-99m)
Flathead galaxias
Still water (lentic)
Sam Nicol et al. Complementary Measures

10. Expert elicitation 1: Conceptual Modelling
3 fish experts created a conceptual model focused on cause-and-effect relationships
Sam Nicol et al. Complementary Measures

11. Expert elicitation 2: Node probabilities
Example elicitation question, showing the question, the scenario descriptions, and where to put elicitation estimates
Minimum set of questions elicited (43 questions). Interpolated using the method of Cain (2001)
Estimates averaged over all experts.
Beta distributions derived from 4-point method of Salomon (2013).
Salomon Y (2013) Unimodal density estimation with applications in expert elicitation and decision making under uncertainty. PhD Thesis, University of Melbourne, Australia.
Cain (2001) Planning improvements in natural resources management: Guidelines for using Bayesian networks to support the planning and management of development programmes in the water sector and beyond. CEH Wallingford, UK.
Sam Nicol et al. Complementary Measures

12. The final BBN
Sam Nicol et al. Complementary Measures

13. Ranking complementary measures
Evaluate sensitivity of “regional abundance” output node to complementary measure nodes using Mutual Information in Netica.
Measure the proportion of information shared between complementary measures and the regional abundance node.
No input node had high explanatory power– individual CMs have a limited effect on abundance by themselves.
A combination of favourable conditions could lead to significant benefits from CMs.
Sam Nicol et al. Complementary Measures

14. Ranking complementary measures
Experts completed a direct ranking exercise to rank CMs; this was compared to BBN ranking: some discrepancies.
Draft results for Discussion Only.
Not for Prioritising Complementary
Measures.
Conditional Probability Method (CPM)
Post-CPM review by Experts
– Expected Rankings (non-CPM)
Complementary Measure
Rank
Expert 1
Expert2
Expert 3
Fish stocking 1 5
Install fishways 2 4
Screening of irrigation offtakes 3
Habitat restoration (physical template)
Mitigate cold water pollution
Carp biocontrol 6
Control of aquatic pests
Sam Nicol et al. Complementary Measures

15. Uncertainty between experts (no consensus)?
Why the discrepancy?
Uncertainty between experts (no consensus)?
The model structure is incomplete?
Wrong/ incomplete objective?
Sam Nicol et al. Complementary Measures

16. Did experts have different opinions?
Re-run the analysis, leaving out one expert each time and compare the rankings. Summarise with RMSD from BBN.
Consistent with all experts’ ranks.
Suggests experts were generally consistent when answering the questions.
Rank
Complementary Measure
without Expert 1
without Expert 2
without Expert 3
RMSD
Normalised RMSD 1
Fish stocking 2
Install fishways
-1.63
-0.59
0.68
1.07
0.35 3
Screening of irrigation offtakes
-1.02
-0.49
1.75
1.20
0.63 4
Habitat restoration (physical template)
-0.30
-0.38
1.99
1.18
1.81 5
Mitigate cold water pollution
-0.10
0.03
0.02
0.06
0.32 6
Carp biocontrol
-0.04
0.01
0.19
Control of aquatic pests
Higher RMSD indicates greater between-expert variability
Sam Nicol et al. Complementary Measures

17. Is the model structure complete?
The model may be incomplete– may not capture the importance of context and external factors. E.g. fish stocking will increase abundance, but may have unwanted genetic effects.
Missing nodes?
Sam Nicol et al. Complementary Measures

18. Incomplete objective?
Relative benefit depends on many factors, not just abundance
… an increase in diversity, population size, age classes, or distribution of native fish species…
Solution:
1) Multiple output nodes
2) Multi-criteria decision problem
Ecological benefit/dis-benefit
Spatial scale of benefit
Time to receive long-term benefit
Scientific confidence
Uniqueness
Dependence of CM on flow
Dependence on other CMs
Dependence on ongoing maintenance
Additional output nodes
Additional output nodes
BBN
Sam Nicol et al. Complementary Measures

19. Where to next?
Benefits of Complementary Measures are hard to capture adequately
Objective is complex and currently ill-defined
BBNs are a useful approach, but is the elicitation burden too high?
Simpler approach may work– e.g. direct ranking against Basin Plan objectives, but lose the ability to predict and provide uncertainty bounds.
A first attempt complete, but still lots of work to do!
Sam Nicol et al. Complementary Measures

20. Thank you Sam Nicol Conservation Decisions Team CSIRO Land and Water
Image: David Kleinert
Sam Nicol et al. Complementary Measures