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Aiello-Lammens: Global Sensitivity Analysis for Impact Assessments.
1. Global Sensitivity Analysis for
Impact Assessments
Matthew Aiello-Lammens
H. Resit Akçakaya
Stony Brook University
Ecological Society of America 2013
2.
3. Integration of Sea-Level Rise model
(SLAMM) and Population Viability Analysis
(RAMAS GIS)
•Land cover
•Geology
•Local accretion and
erosion rates
•SLR / climate change
scenario
Current species
distribution
Sea-level Rise
Model (SLAMM)
Population size
through time
Land cover
change
through time
Demographic Model
(RAMAS GIS)
Current demographic data
Habitat Suitability
Model (MaxEnt)
Habitat suitability
through time
4. Integration of Sea-Level Rise model
(SLAMM) and Population Viability Analysis
(RAMAS GIS)
•Land cover
•Geology
•Local accretion and
erosion rates
•SLR / climate change
scenario
Current species
distribution
Sea-level Rise
Model (SLAMM)
Extinction risk
Population viability
analysis
Land cover
change
through time
Demographic Model
(RAMAS GIS)
Current demographic data
Habitat Suitability
Model (MaxEnt)
Habitat suitability
through time
5. Results
1.1
Relative value to 2010
1
0.9
0.8
0.7
0.6
0.5
N (No SLR; Ceiling)
0.4
0.3
2010
2020
2030
2040
2050
2060
Year
2070
2080
2090
2100
6. Results
1.1
Relative value to 2010
1
0.9
0.8
0.7
∆ Carrying Capacity
0.6
0.5
N (No SLR; Ceiling)
0.4
0.3
2010
2020
2030
2040
2050
2060
Year
2070
2080
2090
2100
7. Results
1.1
Relative value to 2010
1
0.9
0.8
0.7
∆ Carrying Capacity
0.6
0.5
N (No SLR; Ceiling)
0.4
0.3
2010
N (2m SLR; Ceiling)
2020
2030
2040
2050
2060
Year
2070
2080
2090
2100
38. Density of Probability of ∆ Decline to 50
Density
Unpaired Bootstrap
-0.5
0.0
0.5
∆ Probability of Decline to 50
1.0
39. Density of Probability of ∆ Decline to 50
Unpaired Bootstrap
Density
Mean Difference = 0.074
-0.5
0.0
0.5
∆ Probability of Decline to 50
1.0
40. Density of Probability of ∆ Decline to 50
Density
Paired Simulations
-0.5
0.0
0.5
∆ Probability of Decline to 50
41. Snowy Plover – Sample Size 100 – Probability of Decline to 50
Unif
Frequency
LHS
Probability of Decline to 50 – No SLR
Unif
Frequency
LHS
Probability of Decline to 50 – 2m SLR
42. Snowy Plover – Sample Size 100 – Probability of Decline to 50
Unif
Frequency
LHS
Probability of Decline to 50 – No SLR
Unif
Frequency
LHS
∆ Probability of Decline to 50 (Result of 2m SLR)
44. Current Implementations
Glossy Buckthorn Invasive
Effects of land-use change
Density dependence
models
Passenger Pigeon –
Extinctions
Effects of land-use change
Impact of harvest /
hunting
NA Herps – Impacts of
Climate Change
Effects of climate change
scenarios
45. Acknowledgements:
HR Akcakaya, J Stanton, A Cahill, G Sorrentino, H Ryu, E Kneip,
K Shoemaker, M Aldred, S Sabatino, SERDP Collaborators
Funding:
SERDP and NASA
Hinweis der Redaktion
** ACKNOWLEDGE THE WORK OF MY COLLABORATORS** The Snowy Plover is a Threatened shore-bird that is listed asState Threatened (Florida), USFWS Threatened (West Coast), THREATENED BY Development of nesting areas, Sea Level Rise, and Military training missions (Hence the SERDP funding)
We integrated three stand alone ecological models, using the SLR model as environmental information in our suitability model and the results of the habitat suitability model in the demographic model, accounting for the changing habitat through time
** Looking at my model results, the prospects for snowy plover seem bleak** Here I’m showing you the mean population size over 1000 replicate simulations through time
Here is what the change in carrying capacity looks like
And here is the mean population size through time given that sea-level rise affect
sea-level rise increases risk of decline and decreases the expected minimum abundance
BUT there’s a lot of uncertainty in our model input parameters, or our knowledge of the processes that are producing these patterns
And this uncertainty should be accounted for when we are analyzing our model outputsSo as any good modeler does, I did a sensitivity analysis
One-at-a-time sensitivity analysis
** can we make this more efficient and do a thorough sensitivity analysis on a single computer?** Also, for many of our applications, we want to assess the sensitivity of our models to our uncertainty __GIVEN__ some assumed impact – here climate change impacts on habitat suitability, specifically due to SLR --- so we want to separate our uncertainty in model parameters from the impact –
To compare our different sample sizes, I’m assuming the parameter importance values generated by the sample size 10k simulations represents the TRUE importance values
I then generated replicate sets for each sample size, so that for each sample size 10k simulations were carried out
** __Relative influence__ “is a function of the number of times a variable is selected for splitting, weighted by the square improvement to the model as a result of each split, and averaged over all trees”** I then calculate a simple linear correlation between these two sets of values, and again assuming 10k is __True__, we can think of this correlation value as a measure of “Information Recovery”
** On to the resultsDo we sample the parameter space more efficiently using Latin Hypercube Sampling over uniform random sampling? Here I am showing you 10 of the 100 replicates from sample size = 100 for adult survival and fecundity
No evidence for major differences
** But, for many of our applications, we want to assess the sensitivity of our models to our uncertainty __GIVEN__ some assumed impact – here I’m looking at the impact of SLR on carrying capacity --- so we want to separate our uncertainty in model parameters from the impact –
Run simulations with exactly the same parameter set, and only change parameters related to SLR impact
And we do this for our risk measures as well, ie risk of extinction, risk of decline, and expected minimum abundance
Notice how wide the range is for sea-level rise case versus how narrow the range is for the change
In this example, many more replications are required to determine the relative influence (variable importance) of variables on the relative change measures than when looking at the absolute risk measures.