CLLAMM Futures - CLLAMM technical briefing

HEADLINE TO BE PLACED IN THIS SPACE CLLAMM Futures: Modelling ecosystem responses to future scenarios July 21st, 2009 Peter Fairweather & Rebecca Lester Flinders University

Aims • To develop an ecosystem response model to predict future condition in the Coorong • To model various possible future scenarios: • how will climate change & management affect the ecosystem states of the Coorong? • Today: • a brief overview of approach & some of the key findings

An ecosystem response model for the Coorong

Variables included Biological Environmental • Bird abundances (UoA, DEH) • Flow parameters (MDBC) • Commercial fish CPUE • Volumes, rates (SARDI) • Lags from previous years • Fish abundances (SARDI) • Hydrology (CSIRO) • Macrobenthos abundances • Level above AHD, Depths (FU) • Tidal influence, Lags • Macrophyte prevalence • Meteorological (BoM) (UoA) • Water quality (CSIRO, DEH) • Salinity, [Nutrient], Turbidity

Alternating data sets approach Biological Environmental 1. Identify clusters 2. Identify differing conditions 3. Confirm distinct states 4. Evaluate new cases 5. Characterise states

Ecosystem States of the Coorong True False CLLAMMecology Hypersaline basin Marine basin

What do these states look like? Estuarine/marine Degraded marine CLLAMMecology

Environmental characteristics MM + NL SL DM EM M UM HH AH UH DH Low n Days since flow Flow volume Salinity Tidal influence [TKN] na [P] na Turbidity na ♦ Very low ♦ Low ♦ Med ♦ High ♦ Very high na = no data available

Biological characteristics MM + NL SL DM EM M UM HH AH UH DH Low n Fishing birds Shorebirds Waterfowl Estuarine fish Marine fish Benthic inverts na Ruppia na na na ♦ Very low ♦ Low ♦ Med ♦ High ♦ Very high

How is this modelling different? • Multivariate, not focussed upon a univariate response • Algorithm defined both states & thresholds • Used these to define transitions for predictions • Wanted to model perceived decline • Designed to include transitional states • Dataset did not allow equilibrium to be identified ∴ removed assumption of stability from model

Management implications of ecosystem state approach • Simplifies definition of ecosystem condition per se • Allows management at ecosystem scale • not just indicator species or single parameters • not based on preconceptions • More flexible to manage for states than a focus on a single species or parameter

Scenario analyses

Approach MDB SY Hydrodynami c model Ecosystem state model Water benefits Evaluation of Ramsar obligations scenarios Other measures of impact

Scenario modelling • Climates from CSIRO Sustainable Yields for MDB project, benchmark = 2030 • Flows from MDBA via Ian Webster’s hydrodynamic model • 20 scenarios done, e.g.: • Baseline = historic climate + average in/outflows • Median Future = moderate climate • Dry Future = extreme climate • Extreme + 40cm SLR = extreme climate + SLR • Modelled 12 sites X 114 years = 1368 site-yr CLLAMMecology

Changing ecosystem states CLLAMMecology

How to read distribution of ecosystem states (e.g. Baseline) Sites on the y-axis Each site-year is colour coded Key to colours Years on the x-axis Training Blues & yellows data period are “healthy” while greens & purples/reds are “unhealthy” CLLAMMecology

What does an ugly future look like? e.g. Dry Future CLLAMMecology

Effect of climate & sea-level rise Scenario name Key to colours used = state name How to read this graph Blues & yellows are “healthy” while greens & reds are Percent of total “unhealthy” site-years in each CLLAMMecology state

Effect of climate & extraction levels

Effect of The Living Murray initiative

Ordination of 20 scenarios 2D Stress: 0.04 Effect of Median Future, +40 cm SLR Alternative model Median Future, +20 cm SLR Sea-level rise Dry Future, +40 cm SLR The Living Murray Dry Future, +20 cm SLR Extraction level Historic Natural Climate change Median Natural Baseline Dry Natural Historic TLM off Dry TLM off Dry Future Median TLM off Historic TLM on Baseline Dry TLM on Median Future Dry Future, -10 cm SLR Median TLM on Median Future, -10 cm SLR MM Dredging Max USED Flows Alt baseline

Summary of trends across scenarios • Baseline = a mix of healthy & degraded states • Future climates invoke more degraded states e.g. Baseline = 6%, Median Future = 11%, Dry Future = 46% • Sea-level rise alters mix but still ~45% • Degraded states involve little flow, higher salinities & a much reduced range of biota • ‘a future heading south’ = conditions like the South Lagoon become more common in both lagoons CLLAMMecology

Key findings • Climate change has potential to dramatically affect ecosystem states in the Coorong • e.g. more degraded states: Baseline = 6%, Median Future = 11%,Dry Future = 46% • Extraction of water is exacerbating the problem • Even small amounts of environmental water can alleviate the worst effects of climate change • Nothing as good as water over the barrages

Key messages • Climate change has the potential to devastate the Coorong • at current extraction levels • But relatively small amounts of water will mitigate the worst • e.g. TLM & other similar initiatives • Other interventions are less effective • but may be necessary before flows return

Conclusions • No substitutes for barrage flows • Climate change does not have to destroy Coorong ecosystems • extraction levels play a much bigger role • Additional fresh water is needed • River Murray must be the major source

HEADLINE TO BE PLACED IN Thank you THIS SPACE Acknowledgements • CLLAMMecology Research Cluster & CSIRO CLLAMM researchers (esp. for th datasets) • CSIRO Collaboration Fund • DEH, DWLBC, SA MDB NRM Board • FR3cE

Spare slides for questions

HEADLINE TO BE PLACED IN Recent rainfall THIS SPACE

Ecosystem states • An explicit link sought between physical & biological data • driven by management requirements • Includes transitional states • no emphasis on stability • during decline & (potentially) recovery • Built as a state-&-transition model • usually defined by expert opinion • data-driven definition of each is also possible

Defining states & transitions 1. Identify 2. Identify 3. Confirm clusters differing distinct states •Group cases conditions •Tests relationship •Become preliminary •Find environmental b/w biota & states transitions environment •Group average •Test model stability •Identifies distinct cluster analysis •CART analysis states •ANOSIM analysis 4. Evaluate new 5. Characterise cases states Biological data •Classifies test cases •Environmental & •Tests predictive biological Environmental capacity characteristics data •CART & ANOSIM •Indicator species analyses •SIMPER, BVStep, other analyses

Long v Short time frames Long term Short term • Annual time step • Quarterly time step • 10 sites, 1999 – 2007 • 12 sites, 2005 – 2007 • 3 distinct states (86%) • 6 distinct states (66%) • Long-term analyses did not pick up recent deteriorations in condition • Long- & short- term models could be combined (61%) • One model with 8 distinct states

Key biota of marine grouping • Estuarine/marine • Unhealthy marine • High abundances of • Good numbers of yellow- mulloway, flounder, black eyed mullet, hardyheads, bream some marine/estuarine • Good numbers of fishing species birds, small waders & teal • Good numbers of fishing • Diverse benthic fauna birds, moderate numbers of shorebirds • Marine • Diverse juvenile • High abundances of black invertebrate fauna bream & salmon • High numbers of fishing • Degraded marine birds & small waders • Too few cases to • High number of large categorise using SIMPER polychaete worms

Key biota of hypersaline grouping • Healthy hypersaline • Unhealthy hypersaline • Diverse waders, many • Some tolerant marine fish waterfowl, esp teal • High numbers of banded stilt, • Few fish or invertebrate many shorebirds data • Few invertebrates • Average hypersaline • Degraded hypersaline • Few fish present • Many hardyheads • High numbers of waterfowl, • Some waterfowl, few other some fishers birds • Many chironomids, few • Almost no invertebrates other inverts

Community composition – birds, fish and macrophytes Standardise Samples by Total Resemblance: S17 Bray Curtis similarity 01 2D Stress: 0.17 Murray Mouth 01 North Lagoon 04 South Lagoon 06 06 01 02 02 00 00 07 01 00 00 00 04 02 02 00 01 07 04 07 03 03 0205 03 07 020502 07 06 04 00 05 04 0000 01 06 04 06 07 04 0006 03 02 05 07 01 03 03 07 04 04 05 02 0603 05 06 07 05 01 05 04 03 06 02 03 06 04 00 05 07 02 02 01 04 05 01 00 01 01 05 07 06 06 07

Hypersaline basin vectors Improved in both Improved water levels but more days without flow Deviation in water level from + 0 Baseline - Fewer days without flow but lower Deterioration in both levels - 0 + Deviation in days without barrage flow from Baseline a) Historic Natural & Median Natural, b) Median Future, c) Dry Future & Median Future, +20cm SLR, d) Dry Natural, e) Median Future, -10cm SLR, f) Median Future, +40cm SLR, g) Dry Future, -10cm SLR, h) Dry Future, +20cm SLR, i) Dry Future, +40cm SLR, j) Historic TLM off, k) Historic TLM on, l) Median TLM off, m) Median TLM on, n) Dry TLM off, o) Dry TLM on.

More key messages • In the absence of barrage flows • dredging is essential • SLSRS would have a big short-term impact • channel works + pumping = best option for South Lagoon states • additional South East water would have a longer lasting impact • But none are as effective as barrage flows

CLLAMM Futures - CLLAMM technical briefing

by University of Adelaide on Jul 20, 2009

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CLLAMM Futures - CLLAMM technical briefing — Webinar Transcript