Predictive invasion ecology and management decisions under uncertainty

5779dc3bf8c8a0c9dadb7ff95c67e9e3?s=47 Corey Chivers
February 01, 2013
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Predictive invasion ecology and management decisions under uncertainty

McGill Biology Graduate Student Association Organismal Seminar Award Talk.

5779dc3bf8c8a0c9dadb7ff95c67e9e3?s=128

Corey Chivers

February 01, 2013
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Transcript

  1. 3.
  2. 4.
  3. 5.

    Prediction is very hard, Prediction is very hard, especially about

    the future especially about the future -Niels Bohr, Danish physicist (1885-1962) -Niels Bohr, Danish physicist (1885-1962)
  4. 6.

    Outcome A Outcome B Outcome C Outcome E Outcome F

    Do A Do B Do C Do D Do Nothing t = 0 t = 1 Time In a changing world, not making a decision has consequences, intended or otherwise.
  5. 7.

    How do we make the best use of the data

    and theory that we have to make predictions which take into account (potentially large) inherent uncertainties?
  6. 8.
  7. 9.
  8. 10.
  9. 11.
  10. 12.

    What information do I have? What can I go out

    and observe? Data The Process
  11. 13.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Data The Process
  12. 14.

    All models are wrong, All models are wrong, But some

    are useful. But some are useful. -George E.P. Box -George E.P. Box
  13. 15.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Data The Process
  14. 16.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes Data The Process
  15. 17.
  16. 18.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes Data The Process
  17. 19.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes How well can we recapture patterns and processes? (parameter estimation, model discrimination, & derived variables) Data The Process pseudo-data
  18. 20.
  19. 22.
  20. 23.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes How well can we recapture patterns and processes? (parameter estimation, model discrimination, & derived variables) Data The Process pseudo-data
  21. 24.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes How well can we recapture patterns and processes? (parameter estimation, model discrimination, & derived variables) Data The Process pseudo-data
  22. 25.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes How well can we recapture patterns and processes? (parameter estimation, model discrimination, & derived variables) Data The Process pseudo-data
  23. 26.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes How well can we recapture patterns and processes? (parameter estimation, model discrimination, & derived variables) Does it fit the real data? Data The Process pseudo-data
  24. 27.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes How well can we recapture patterns and processes? (parameter estimation, model discrimination, & derived variables) Does it fit the real data? Data The Process pseudo-data
  25. 28.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes How well can we recapture patterns and processes? (parameter estimation, model discrimination, & derived variables) Does it fit the real data? Test Hypotheses Make forecasts (Forward Simulation) Data The Process pseudo-data
  26. 29.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes How well can we recapture patterns and processes? (parameter estimation, model discrimination, & derived variables) Does it fit the real data? Test Hypotheses Make forecasts (Forward Simulation) Optimize Decisions Scenario Analysis Data The Process pseudo-data
  27. 30.
  28. 31.

    Invasive forest insects 1. International trade has many externalities 2.

    Total damages of existing pests 3. Estimate the probability of new high impact pest A. Guilds: which pathways? B. Economic sectors: who pays the costs?
  29. 32.

    • Base line information lacking – Compile all known non-indigenous

    forest pests – Identify short list of intermediate damaging pests – • National economic estimates lacking – In depth analysis of the most damaging pests – 3 guilds (borers, sap suckers, foliage feeders) – 3 economic cost sectors (government, households, market) Emerald Ash Borer Hemlock Woolly Adelgid Gypsy Moth
  30. 33.

    ( ) ( ) ) ( ) | ( )

    ( | | Pr 1 ϑ ϑ ϑ ϑ ϑ P c f P P M = m m       ∝ ∝ ∏ c c If we knew the cost of each pest, we can fit our models using the simple likelihood function. 0 1 2 3 4 5 6 7 8 0 2 4 6 8 Cost ($) Frequency of pests
  31. 34.

    0 1 2 3 4 5 6 7 8 0

    2 4 6 8 Cost ($) Frequency of pests 78 13 1 Pr (ϑ∣d)∝ [∏ i=1 I P(low∣ϑ) x∏ j=1 J P(intermediate∣ϑ) x∏ k=1 K P(high∣ϑ) ]P(ϑ) What we have are frequencies of species in different impact ranges.
  32. 35.

    The Framework: A) Species Frequencies in 3 categories. B) Which

    Model? C) Model estimation D) Probability distribution of derived variable of interest (total cost, probability of new high impact pest) Aukema JE, Leung B, Kovacs K, Chivers C, Britton KO, et al. (2011) Economic Impacts of Non-Native Forest Insects in the Continental United States. PLoS ONE 6(9): e24587
  33. 36.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes How well can we recapture patterns and processes? (parameter estimation, model discrimination, & derived variables) Does it fit the real data? Test Hypotheses Make forecasts (Forward Simulation) Optimize Decisions Scenario Analysis Data The Process pseudo-data
  34. 38.

    Simulation Validation 2 Given a model, can we recapture the

    parameters, and derived quantities of interest?
  35. 39.

    Results Highest impact are the Borer guild. Costs are born

    primarily by local governments. ~1.7 Billion USD per annum Single most damaging pests in each guild accounts for 25-50% of the total impacts. At current establishment rates ~32% chance of another high impact pest in the next ten years. Aukema JE, Leung B, Kovacs K, Chivers C, Britton KO, et al. (2011) Economic Impacts of Non-Native Forest Insects in the Continental United States. PLoS ONE 6(9): e24587
  36. 40.

    More imports, this time of the fishy sort. • Given

    a proxy measure of propagule pressure, how well can we estimate the risk of establishment? Bradie, J., Chivers, C. & Leung, B. (2013) Importing risk: quantifying the propagule-pressure establishment relationship at the pathway level. in press Diversity and Distributions.
  37. 41.

    • Not all species are equally likely to establish •

    In the absence of species specific lifehistory information, how well can we estimate overal pathway risk?
  38. 42.

    Bradie, J., Chivers, C. & Leung, B. (2013) Importing risk:

    quantifying the propagule-pressure establishment relationship at the pathway level. in press Diversity and Distributions. Effects of unaccounted variability
  39. 43.

    Results At an import level of 100,000 individuals, establishment risk

    of 19% Importing 1 million individuals leads to just under a 1 in 2 chance of establishment. Bradie, J., Chivers, C. & Leung, B. (2013) Importing risk: quantifying the propagule-pressure establishment relationship at the pathway level. in press Diversity and Distributions.
  40. 44.
  41. 45.

    Alternative models of human behaviour • Gravity Model – 'Pull'

    of attractive lakes • Random Utility Model – Rational utility maximizers (Schneider et al. 1998, Leung et al. 2004, 2006) (Moore et al. 2005, Timar and Phaneuf 2009)
  42. 46.

    Alternative models of human behaviour • Gravity Model – 'Pull'

    of attractive lakes • Random Utility Model – Rational utility maximizers PGM T nj =A n W j e D nj −d , n=1,... ,n , j=1,..., J. A n =1/∑ k =1 L W k e D nk −d . U nj =V nj +ϵ nj , n=1,... , N , j=1,... J V nj =   X nj PRUM T nj = expV nj  ∑ k=1 J expV nk  , n=1,... , N , j=1,... , J (Schneider et al. 1998, Leung et al. 2004, 2006) (Moore et al. 2005, Timar and Phaneuf 2009)
  43. 49.

    Figure A1: Simulated trip outcomes in a landscape of lakes

    with induced spatial auto-correlation. Size of circle is proportional to the size of the simulated lake.
  44. 50.

    Figure A3: Generating vs maximum likelihood estimates for the four

    parameters (panels) of the random utility model. The 1:1 line is also plotted for comparison. Figure A2: Generating vs maximum likelihood estimates for the four parameters (panels) of the gravity model. The 1:1 line is also plotted for comparison. Re-capture the parameter values?
  45. 52.

    ∑ i=1 I w i =13 Low Entropy High Entropy

    Predicted Dispersal Networks ∑ i=1 I w i =13
  46. 55.
  47. 56.

    Behavioural Model The effect of costly cleaning impossed at a

    lake on boater choice. -Redistribute visits? -Reduce visits? -Both?
  48. 57.

    1b =  P 1b  P 1b ∏ b=1

    B N 1b N 1b '  1b N 1b ' 1− 1b N 1b −N 1b '  .N 2b ' N 2b  2b N 2b 1− 2b N 2b ' −N 2b  2b = P 2b P 2b  L ,|D= Where: , Policy Lake Non-Policy Lake Behavioural observation model
  49. 61.

    What information do I have? What can I go out

    and observe? What are the hypothesized processes which generated the data? Theory/models Simulate Hypothesized Biological Processes How well can we recapture patterns and processes? (parameter estimation, model discrimination, & derived variables) Does it fit the real data? Test Hypotheses Make forecasts (Forward Simulation) Optimize Decisions Scenario Analysis Data Methodology for decision support pseudo-data
  50. 62.

    Thank you Supervisors: Dr. Brian Leung Dr. Elena Bennett Dr.

    Claire De Mazancourt Dr. Gregor Fussman Lab Mates: Johanna Bradie Paul Edwards Kristina Marie Enciso Andrew Sellers Lidia Della Venezia Erin Gertzen