Post on 15-Jan-2016
description
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Collaborative Systemwide Monitoring and Evaluation Project (CSMEP)
Presentation to PNAMP Steering Committee
August 28, 2008
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CSMEP Analyses - Tools
CBFWAESSA Technologies Ltd.
State AgenciesIDFGODFWWDFW
Federal AgenciesNOAAUSFWSEPADFO
Tribal AgenciesCRITFCNez Perce TribeColville TribesYakama NationUmatilla Tribes
ConsultantsEco Logical ResearchQuantitative ConsultantsPERWEST
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Status and Trends
abundanceproductivity (age structure)spatial structurespatial diversity
population, MPG, ESU scales
Harvest
ESU scales
Hatchery
Hatchery: Wild (straying)relative productivity
program,population,system scales
Hatchery: WildAge structure
Hydrosystem
population, MPG, ESU,system scales
Habitat
watershed,population,scales
• upstream/downstream• estimate• estimate of total survival• mainstem survival• SARS
• onboard monitoring• landed catch monitoring• creel surveys
• dam monitoring
abundanceproductivityspatial structurespatial diversity
• redd counts• weirs• carcasses• MRC• juvenile traps• other methods
PIT Tags
PIT T
ags PIT Tags
PIT
Tag
sP
IT T
ags
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Status and Trends M&E
Purpose: Assess viability of the population and evaluate overall management strategies.
4H Impacts Actions Monitoring
Harvest √
Hydro √
Habitat √
Hatchery √
Status and Trends will tell you what the population is doing but not why.
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1) Monitoring Data S&W Assessments (S&T)
SOTR Reporting
2) Salmon Viability Model
3) Integrated Costs Database Tool
Project Specific Planning/Budgeting
CSMEP Analyses/Tools
CSMEP Analyses/Tools
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Status & Trends Monitoring
Priority Question: Are salmon viable using IC-TRT criteria?
Related Decision: Has there been sufficient improvement in the status of a salmon population/ESU to justify delisting and allow removal of ESA restrictions?
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What if we make the wrong decision?
De-listing when not warranted
Risk to populations
Not de-listing when warranted Missed fishing opportunities Lost land use opportunities Unjustified cost of ESA protections
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How likely to make the correct viability decision with different levels of monitoring intensity?
Data Needs:• Abundance• Productivity• Spatial structure• Diversity
Objective: Create a tool to help managers evaluate alternative monitoring designs
Approach: Evaluate existing monitoring data and develop a model to explore the ability of monitoring to correctly assess salmon population viability
Technical Recovery Team
viability criteria
Viability Status:• Not Viable• Maintained• Viable• Highly Viable
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Salmon Viability Simulation Model
(CSMEP S&T group, D. Pickard, C. Jordan, C. McGrath)
1st step towards model: try to quantify variability/uncertainty in the quality of monitoring data:
• CSMEP S&W assessments• Summary of the statistical properties of different methods for
estimating fish performance measures. D. Pickard - ESSA Report
• Current research on sampling variability by Dan Rawding (WDFW) and Claire McGrath (USFS)
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Monitoring activities used to assess viability
x = monitoring occurs in at least one Major or Minor Spawning Area; a = PIT-tags scanned at weirs; b = CWT recoveries of hatchery adults at hatchery weirs.
MPG L. Snake Grande Ronde River
Data need Method/Description Aso
tin
Tuc
anno
n
Jose
ph
Wa
llow
a
LGra
nde
Ron
de
U G
rand
e R
ond
e
A1 census weir (number) 1 1 A2 weir w/MR (number) 1 1 2 A3 weir w/o MR (number) 1
Abundance of adults
A4 MR survey, no weir B1 Index-multi x x Abundance and distribution
of redds B2 Index-once x x x C1 Tags (CWT, PIT) a a b a,b C2 Hard parts, scales x x x x C3 Length at age
Age structure of spawners
C4 Basinwide estimate D1 Marks , weirs (number) 1 1 2 3 D2 marks, remote sense Origin of spawners D3 marks, carcasses E1 Carcass survey Sex ratio of spawners E2 Weirs (number) 1 1 2 3 F1 Juvenile trap (number) 1 1 2 3 F2 Electrofish F3 Snorkel survey--random F4 Snorkel survey--fixed
Abundance and spatial distribution of juveniles/smolts
F5 Presence/absence Survival of juveniles/smolts G1 mark-recapture x x
H1 Juvenile trap x x Age structure of juveniles/smolts H2 other in-river sampling
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Summaries of the quality of Status Quo Monitoring (feeding the viability model)
PopulationRedd count
type Weir
Proportion of spawning area
covered by weirAbundance
AssumptionsSpatial
coverage
Assess diversity metrics
Asotin Creek two-time census ground
yes 100% Unbiased, high precision Good Good
Tucannon River multiple ground-census
yes 70% Unbiased, high precision Good Good
Minam River multiple ground-census
no na unbiased, medium precision
Good Good
Little Salmon River none yes 50% Overall: biased, medium precision
None Poor
SF Salmon River mainstem
index (aerial) yes 25% Biased, low precision None Poor
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Alternative designs to explore within viability model(example – a ‘Medium” design)
Population Redd count type WeirProportion of spawning area
covered by weirAbundance
AssumptionsSpatial
coverage
Assess diversity metrics
Asotin Creek 3 pass ground index, 3rd pass is spatial census ground count
no 100% Unbiased, med precision
Good Good
Tucannon River 3 pass ground index + 1pass spatial census aerial count
yes 70% Unbiased, high precision
Good Good
Minam River 3 pass ground index, 3rd pass is spatial census ground count
no na Unbiased, med precision
Good Good
Little Salmon River 3 pass ground index, 3rd pass is spatial census ground count
no 50% Unbiased, med precision
Good Good
SF Salmon River mainstem 3 pass air index, 3rd pass is spatial census aerial count
no 25% Unbiased, high precision
Good Poor
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Model Inputs & Outputs
Model inputs
Strengths & weakness
assessments Spatial coverage
Abundance (CV and bias)
Diversity
Model
Age-structure (smoothing)
Probability of correctly
assessing viability
Model outputs
TRUTH (A, P, SS, D)
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Model Inputs: Spatial Structure & Diversity
•A probability transition matrix is used to determine the probability of correctly classifying the data in each of the 4 risk categories (H, M, L, VL) given the monitoring in place
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Model Input: Abundance & Productivity
Abundance
Productivity
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e.g., Unbiased, low precision
e.g., Unbiased, medium precision
e.g., Biased, medium precision
The monitoring design defines the assumed measurement error (“noise’)
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Measurement error within the model:
• Derived from CSMEP’s S&W assessments and user inputs
• Dependent on level of effort to assess spawners• Bias depends on how spatially representative the
sampling• Dependent on quality of information from each
identified spawning area• Dependent on the number of samples obtained• Dependent on variable ability to ‘get hands on fish’
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Test case: Snake Basin Spring/Summer Chinook Evolutionarily Significant Unit (ESU)
• 32 populations• 5 major population groups (MPGs)• Diversity of current monitoring efforts• State biologists are interested in
modifying monitoring designs
• Objective: Test the ability of alternative monitoring designs to correctly assess viability
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Status quo
0.60
A single run of the simulation
Medium
0.73
High
0.84
Low
0.41
Pr (correct assessments)
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Utility of Salmon Viability Model
• Given that we have:
– A framework for decision making– Estimates of uncertainty in data
• The simulation modeling allows us to:
– Evaluate sensitivity of decisions to quality of monitoring data– Test influence of specific types of monitoring data on
decisions– Managers to evaluate alternative monitoring designs
• Viability model is currently coded in R; now converting to more user friendly format and developing associated user guide to allow managers to explore their own alternative M&E designs
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Integrated Costs Database Tool(CSMEP S&T group, D. Carr - programmer)
• A relational database (MS Access) developed to allow estimation of the cost of integrated monitoring designs
• Combines the costs of equipment, manpower, tagging and analyses required for a suite of survey techniques required across S&T and 4H monitoring
• Also helps identify the particular performance measures that could be captured within a proposed monitoring design
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Interaction of viability and cost estimates
Probability of correct viability decision
Monitoring Cost ($)
0.0
0.5
1.0 probability
Have we achieved acceptable reliability at acceptable cost?
Yes - Stop
No - Redesign
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Objectives by Alternatives Matrix:Status & Trends Designs
Status and Trends Subgroup
.94.87.45.55Pr (making the correct viability assessment for Snake ESU)
Statistical Reliability
2,1007101751,170annual cost of design alternatives (x $1,000)
Cost ($)
GoodFairPoorPoor
ability to make viability assessments for each population in the ESU
Inferential ability (Qualitative)
HighMed Low Status Quo
Design examplesPerformance Measures(Abundance and Spatial
Structure)
Design Objectives
0.840.730.410.60Pr (making the correct viability assessment for ESU populations)
Statistical Reliability
2,1257101751,283annual cost of design alternatives (x $1,000)
Cost ($)
ExcellentVery GoodPoorFair
ability to make viability assessments for each population in the Snake Sp/S Chinook ESU
Inferential ability (Qualitative)
HighMed Low Status Quo
Design alternativesPerformance Measures(Abundance and Spatial
Structure)
Design Objectives
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ICDT User Guide
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Questions?