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

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 2012-13, 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

A river that needs help Water resource development -> long-term ecological decline Millennium drought (2002-2010) -> $12.9 billion over 10 years for a national agreement on water use in the Murray-Darling Basin www.mdba.gov.au https://www.flickr.com/photos/mundoo/ Sam Nicol et al. Complementary Measures

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

… 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

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

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

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

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 (100-1000km) Golden perch Micro (1-99m) Flathead galaxias Still water (lentic) Sam Nicol et al. Complementary Measures

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

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

The final BBN Sam Nicol et al. Complementary Measures

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

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

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

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

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

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

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

Thank you Sam Nicol Conservation Decisions Team CSIRO Land and Water sam.nicol@csiro.au www.mdba.gov.au. Image: David Kleinert Sam Nicol et al. Complementary Measures