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Hugh Possingham- Why Monitor the Environment
 

Hugh Possingham- Why Monitor the Environment

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Professor Hugh Possingham is currently the Director of the Ecology Centre at The University of Queensland. Hugh has over 290 publications, 5300 Web of Science citations and a lab of 32 students and ...

Professor Hugh Possingham is currently the Director of the Ecology Centre at The University of Queensland. Hugh has over 290 publications, 5300 Web of Science citations and a lab of 32 students and staff. Work from his lab helped stop land clearing ("the Brigalow Declaration") in Queensland and NSW securing at least 1 billion tonnes of CO2.

"We generally assume that all monitoring is good. However there are numerous examples of people monitoring things to extinction and monitoring with no clear objective. Hugh Possingham will present a completely different way of looking at environmental monitoring - using decision science thinking. This approach enables us to work out how much of our precious budget should be spent monitoring, if any! The problem with existing monitoring, aside from doing too little, is that ecologists have been trained within a classical null hypothesis testing framework - great for pure science, rubbish for solving environmental problems."

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    Hugh Possingham- Why Monitor the Environment Hugh Possingham- Why Monitor the Environment Presentation Transcript

    • The Environment Institute Where ideas grow Hugh Possingham „Why Monitor the Environment? - A Decision Science Approach‟
    • How much and why should we monitor? Monitoring is an optimisation problem first and a statistical problem second Hugh Possingham, lab and friends The Ecology Centre and Centre for Applied Environmental Decision Analysis – a CERF Read www.aeda.edu.au/news The University of Queensland Australia the ecology centre university of queensland australia www.uq.edu.au/spatialecology h.possingham@uq.edu.au
    • Who pays for all the work? • Australian Research Council grants (19), UQ, UofA, Australian Federal Government Environment Department (CERF), TNC, PEW, CI, state govts (several), local governments, mining companies, TWS, WWF, BA, CRCs, + innumerable minor grants
    • Some “straw men” of applied monitoring/data collection • We need to monitor all conservation interventions with sufficient power to detect significant effects • I have just monitored frog species Y to extinction • We need to learn about how the system works = science • Count first, ask questions later • Getting more data on biodiversity is always a good investment Balmford A. & Gaston K.J. (1999). Why biodiversity surveys are good value. Nature, 398, 204-205
    • Heretical views • Most monitoring programs have no clearly stated objectives and hence can‟t be optimised (Joseph et al. 2010, Optimal monitoring for conservation) • Surveillance monitoring is a waste of time • (Nichols, J. D., and B. K. Williams. 2006. Monitoring for conservation. Trends in Ecology & Evolution 21:668-673.) • All monitoring for conservation should be based in a decision-making framework Possingham, H. P., Andelman, S. J., Noon, B. R., Trombulak, S. and Pulliam, H. R. 2001. Making smart conservation decisions. In: Research priorities for conservation biology. Eds. Orians, G. and Soule, M. Island Press
    • Monitoring costs money that could be used for solving the problem = managing Optimal allocation to monitoring 100% Expected Efficiency outcome of from manage- managing Net gain ment if efficient 0% 0% 100% 0% Percentage of budget spent on management 0% 100% Percentage of budget spent on monitoring
    • Monitoring marine reserves Control Impact Before After Big fish? More fish?
    • Monitoring marine reserves Control How many times do we have to reject the Impact null hypothesis that fishing does not kill fish? Or dead fish grow? Before After Big fish? More fish? What marine reserve monitoring could we do that would influence future decisions?
    • “Classical” approach to optimal monitoring – alpha = 5% Blue monitoring strategy Statistical power Predetermined level of power Purple we want monitoring strategy Fixed budget Investment in monitoring strategy
    • “Classical” approach to optimal monitoring – alpha =Why? 5% Blue monitoring strategy Statistical power Predetermined level of power Purple we want monitoring strategy Why? Fixed budget Investment in monitoring strategy
    • How much monitoring should we do for management/policy? The answer requires an objective. 7 reasons to monitor (Joseph et al.) 1. Audit the to see if actions taken or legislative requirements met or make donors happy 2. State-dependent management – (e.g. setting fisheries quotas, acting to save a threatened species) 3. To learn for learning‟s sake 4. Active adaptive management – optimal management accounting for the benefits of learning 5. Inform the public and/or politicians of an issue so policy and allocations may change 6. Serendipity, so many breakthroughs have come from just looking 7. People like it and do it for free
    • How much monitoring should we do for management/policy? The answer requires an objective. 7 reasons to monitor (Joseph et al.) 1. Audit the to see if actions taken or legislative Boring requirements met or make donors happy 2. State-dependent management – (e.g. setting fisheries quotas, acting to save a threatened species) 3. To learn for learning‟s sake Irrelevant 4. Active adaptive management – optimal management accounting for the benefits of learning 5. Inform the public and/or politicians of an issue so How much is enough? policy and allocations may change 6. Serendipity, so many breakthroughs have come from ? just looking 7. People like it and do it for free Great
    • 2 State Dependent Management – how much monitoring? Counting moose or kangaroos (Hauser et al. 2006, Mansson et al.) Survey roughly Survey well Happiness Quota Count Reality Number of moose or kangaroos Hauser CE, Pople AR, Possingham HP. 2006. Should managed populations be monitored every year? Ecological Applications 16:807-819.
    • 4 Active adaptive management The holy grail of applied ecology – where we try to gain knowledge only in so far that the benefit of that knowledge gain is expected to outweigh the costs of fiddling with the system and learning about how it works
    • Bridled Nailtail Wallaby (Onychogalea fraenata) Endangered
    • B A
    • B 1/2 A 2/3
    • Enter Reverend Thomas Bayes and the incredible beta distribution . Thomas Bayes (pronounced: beɪz), (c. 1702 – 17 April 1761) was a British mathematician and Presbyterian minister, known for having formulated a specific case of the theorem that bears his name: Bayes' theorem, which was published posthumously.
    • Treatment A: 2/1 Treatment B: 1/1 The chance of survival Treatment A Treatment B Likelihood of probability 0.1 0.08 0.06 0.04 0.02 0 0 0.2 0.4 0.6 0.8 1 Probability
    • Do what is best for the poor little Treatment A: 24/18 wallabies Treatment B: 1/1 The chance of survival Treatment A Treatment B Likelihood of probability 0.1 0.08 0.06 0.04 0.02 0 0 0.2 0.4 0.6 0.8 1 Probability
    • No, I am a scientist, randomised sequential clinical trial Treatment A: 80/70 Treatment B: 90/50 The chance of survival Treatment A Treatment B Likelihood of probability 0.1 0.08 0.06 0.04 0.02 0 0 0.2 0.4 0.6 0.8 1 Probability
    • No, I am a scientist, randomised sequential clinical trial Treatment A: 80/70 Treatment B: 90/50 The chance of survival Treatment A Treatment B Don‟t worry, I just Likelihood of probability 0.1 0.08 discovered treatment C 0.06 which is a lot better 0.04 0.02 than A or B, stop the trial 0 0 0.2 0.4 0.6 0.8 1 Probability
    • Answer • There is an optimal state dependent allocation of wallabies to treatments that is a compromise between doing what is best now and reducing uncertainty so we make better decisions in the future = perfectly optimal active adaptive management Rout T.M., Hauser C.E. & Possingham H.P. (2009). Optimal adaptive management for the translocation of a threatened species. Ecol. Appl., 19, 515-526 McCarthy M.A. & Possingham H.P. (2007). Active adaptive management for conservation. Conserv. Biol., 21, 956-963
    • 5 A tricky objective Keep the public and/or politicians happy, or provide them with enough information to drive actions
    • Another new problem: How much monitoring do we need to keep the masses/politicians happy? Many people cranky More rigorous Public’s approach level of discontent Publicise with the casual monitoring observations investment People who are never happy Few Level of funding people that is legislated for Amount of investment in cranky monitoring strategy
    • 6 Serendipity Can this be quantified hence optimised?
    • Thoughts • Many things should not be monitored because the costs outweigh the benefits • Monitoring is first and foremost an optimisation problem. Statistics is part of the mechanics but should not proceed without being nested in a decision theory problem • Ecological stats is taught in the context of pure science not applied science which is why we are in a mess • Is monitoring a political displacement activity intended to keep scientists busy? • How much data do we need to convince the masses that everything is bad/ok? Is some data more compelling than other data? • Is there an optimal amount of surveillance? • What should I tell TNC to do?
    • Some more of our papers on optimal monitoring and information gain • How long should I monitor a fix stock before fixing the reserve size? – Gerber, L. R., M. Beger, M. A. McCarthy, and H. P. Possingham. 2005. A theory for optimal monitoring of marine reserves. Ecology Letters 8:829-837 • Monitor or manage? – uses POMDPs – Chades I., McDonald-Madden E., McCarthy M.A., Wintle B., Linkie M. & Possingham H.P. (2008). When to stop managing or surveying cryptic threatened species. PNAS, 105, 13936-13940 • More recent papers by McDonald-Madden et al.
    • Before you monitor • Stop, Think • Maybe monitor less, better and longer • Work out what you might do with the information that could alter future actions (even public opinion) and increase the chance of delivering a net conservation outcome relative to other forms of expenditure • Place it in a decision theory or forecasting context and work out how long it will take and how much it will cost – can you afford it? Maybe you should act with what you know now? Read Decision Point (monthly): www.aeda.edu.au/news
    • Read Decision Point: www.aeda.edu.au/news
    • 2 Trading type I and type II errors Mapstone (1995), Field et al. (2004) Truth Species OK Species declining Species Type II OK Great Data Species Type I Great declining Field, S. A., A. J. Tyre, N. Jonzén, J. R. Rhodes, and H. P. Possingham. 2004. Minimizing the cost of environmental management decisions by optimizing statistical thresholds. Ecology Letters 7:669-675
    • 2 Trading type I and type II errors Mapstone (1995), Field et al. (2004) Truth Species OK Species declining Species Type II OK Great Data Species Type I Great declining Field, S. A., A. J. Tyre, N. Jonzén, J. R. Rhodes, and H. P. Possingham. 2004. Minimizing the cost of environmental management decisions by optimizing statistical thresholds. Ecology Letters 7:669-675
    • History: Gum sites Bob Howe, David Paton, Drew Tyre, Tim and Patrick Three 20min 2ha Stringybark counts - c160 sites sites from 1999 to now the ecology centre university of queensland australia www.uq.edu.au/spatialecology h.possingham@uq.edu.au
    • The canary of the Statistically significant decline in stringybark canaries. All is not well for Scarlet Robins in stringybark. This is not surprising as there is ample local and national evidence that Not stat significant decline in gum woodland this species is going downhill steadily.
    • 1. Specify project objectives No 3. Implement research to identify 2. Do I know the threats and management options? threats and/or management options. Yes 5. Do I know which 4. Does my choice of Yes management option Yes 6. Use decision analysis to evaluate management action depend on the is best given each options for monitoring the state of the state of the system? state of the system? system. No No 8. Implement this management option. 7. Is my best management option clear? Yes No monitoring recommended. No 1. Use decision analysis to evaluate management options. Implement best 9. Do I have sufficient time to make No management option from this analysis. changes to management? No monitoring recommended. Yes 1. Monitor and manage within an active adaptive management 1. Do we have the resources to Yes framework to determine the best implement active adaptive management option over time. management? No 1. Monitor and manage within a passive adaptive management Yes framework. 1. Use decision analysis to evaluate Use decision analysis to identify initial options for monitoring the performance management option. of my management options. Has an effective monitoring option emerged? No 1. Use decision analysis to evaluate management options. Implement best management option from this analysis. Figure 1: Decision tree for deciding when to monitor No monitoring recommended. to improve conservation management.
    • 1. Specify project objectives No 3. Implement research to identify 2. Do I know the threats and management options? threats and/or management options. Yes 5. Do I know which 4. Does my choice of Yes management option Yes 6. Use decision analysis to evaluate management action depend on the is best given each options for monitoring the state of the state of the system? state of the system? system. No No 8. I.mplement this management option. 7. Is my best management option clear? Yes No monitoring recommended. No 1. Use decision analysis to evaluate management options. Implement best 9. Do I have sufficient time to make No management option from this analysis. changes to management? No monitoring recommended. Yes 1. Monitor and manage within an active adaptive management 1. Do we have the resources to Yes framework to determine the best implement active adaptive management option over time. management? No 1. Monitor and manage within a passive adaptive management Yes framework. 1. Use decision analysis to evaluate Use decision analysis to identify initial options for monitoring the performance management option. of my management options. Has an effective monitoring option emerged? No 1. Use decision analysis to evaluate management options. Implement best management option from this analysis. Figure 1: Decision tree for deciding when to monitor No monitoring recommended. to improve conservation management.
    • a) 1 1 Half protection rate 0.95 0.99 Protection rate Retention in Landscape Representation in PAs 0.9 0.98 Double protection rate Number of species 0.85 0.97 0.8 0.96 saved as a function of 0.75 0.95 0.7 Half protection rate years spent collecting 0.94 Protection rate 0.65 0.93 Double protection rate 0.6 protea data 0 2 4 6 Survey Period 8 10 0.92 0 2 4 Survey Period 6 8 10 c) 1 d) 1 0.95 0.98 Retention in Landscape Data on habitats Representation in PAs 0.9 0.96 0.85 and proteas 0.8 0.94 0.92 0.75 Half habitat loss rate 0.9 Half habitat loss rate 0.7 Habitat loss rate Habitat loss rate 0.65 0.88 Double habitat loss rate Build Double habitat loss rate Get more 0.6 0.86 reserve 0 2 4 6 8 10 0 2 4 6 8 10 data? Survey Period Survey Period system (Grantham H.S., Wilson K.A., Moilanen A., Rebelo T. & Possingham H.P. (2009). Delaying conservation actions for improved knowledge: how long should we wait? Ecology Letters, 12, 293-301) – similar concept in Gerber et al. 2004.
    • The Environment Institute Where ideas grow Hugh Possingham For more information about this event or other events, please visit our website at www.adelaide.edu.au/environment