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Predictive invasion ecology and management decisions under uncertainty

Corey Chivers
February 01, 2013
310

Predictive invasion ecology and management decisions under uncertainty

McGill Biology Graduate Student Association Organismal Seminar Award Talk.

Corey Chivers

February 01, 2013
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  1. Predictive invasion ecology and
    management decisions under
    uncertainty
    Corey Chivers
    McGill University

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  2. Ecological methodology for
    decision support
    Linking theory with data
    to make
    Inference,
    Prediction, &
    Decisions.

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  3. 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)

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  4. 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.

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  5. 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?

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  6. What information
    do I have?
    What can I go out
    and observe?
    Data
    The Process

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  7. 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

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  8. All models are wrong,
    All models are wrong,
    But some are useful.
    But some are useful.
    -George E.P. Box
    -George E.P. Box

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  9. 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

    View full-size slide

  10. 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

    View full-size slide

  11. 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

    View full-size slide

  12. 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

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  13. Sampling
    periods

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  14. 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

    View full-size slide

  15. 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

    View full-size slide

  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
    How well can we recapture
    patterns and processes?
    (parameter estimation, model
    discrimination, & derived variables)
    Data
    The Process
    pseudo-data

    View full-size slide

  17. 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

    View full-size slide

  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
    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

    View full-size slide

  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)
    Does it fit the
    real data?
    Test Hypotheses
    Make forecasts
    (Forward Simulation)
    Data
    The Process
    pseudo-data

    View full-size slide

  20. 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

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  21. 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?

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  22. • 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

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  23. ( ) ( ) )
    (
    )
    |
    (
    )
    (
    |
    |
    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

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  24. 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.

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  25. 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

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  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?
    Test Hypotheses
    Make forecasts
    (Forward Simulation)
    Optimize Decisions
    Scenario Analysis
    Data
    The Process
    pseudo-data

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  27. Simulation
    Validation 1
    Can we identify the
    Correct model?

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  28. Simulation
    Validation 2
    Given a model, can we
    recapture the
    parameters, and
    derived quantities of
    interest?

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  29. 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

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  30. 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.

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  31. Not all species are equally likely to establish

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

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  32. 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

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  33. 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.

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  34. 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)

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  35. 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

    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)

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  36. Observing boater choice

    Online survey

    Which lakes did you
    visit?

    How many times?

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  37. Chivers & Leung (2012)

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  38. 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.

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  39. 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?

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  40. Chivers & Leung (2012)

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  41. i=1
    I
    w
    i
    =13
    Low Entropy High Entropy
    Predicted Dispersal Networks

    i=1
    I
    w
    i
    =13

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  42. Chivers & Leung (2012)

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  43. Managment of spreading aquatic
    invasives

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  44. Behavioural
    Model
    The effect of costly
    cleaning impossed
    at a lake on boater
    choice.
    -Redistribute visits?
    -Reduce visits?
    -Both?

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  45. 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

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  46. Counterfactual stated preference

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  47. Can I recapture model parameters from
    the amount and type of data I have?

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  48. Scenario analysis

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  49. 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

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  50. 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

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