Transcript

Survival Estimation Using Survival Estimation Using Estimated Daily Detection Estimated Daily Detection

ProbabilitiesProbabilities

Benjamin P. SandfordBenjamin P. Sandford

Fish Ecology DivisionFish Ecology Division

NOAA FisheriesNOAA Fisheries

NOAA Fisheries

• Steve Smith – statistical development and programming

• Steve Achord and PTAGIS – data

• COE and BPA - funding

NOAA Fisheries

AcknowledgementsAcknowledgements

General ProblemGeneral Problem

CJS may not be the best survival estimation technique in certain circumstances:

1) Concurrent temporal changes in detection and survival probabilities;

NOAA Fisheries

General ProblemGeneral Problem

CJS may not be the best survival estimation technique in certain circumstances:

1) Concurrent temporally dynamic detection and survival probabilities;

2) Cohort has small sample size but additional data available to estimate detection probability; or

NOAA Fisheries

General ProblemGeneral Problem

CJS may not be the best survival estimation technique in certain circumstances:

1) Concurrent temporally dynamic detection and survival probabilities;

2) Cohort has small sample size but additional data available to estimate detection probability; or

3) Daily detection probabilities needed for non-survival estimation purposes, such as migration timing estimation.

NOAA Fisheries

Specific ExampleSpecific Example

Study: PIT-tagging wild chinook salmon parr.

NOAA Fisheries

Specific ExampleSpecific Example

Primary objective: Migration timing distribution passing Lower Granite Dam.

NOAA Fisheries

0

50

100

150

200

250

300

350

400

450

Specific ExampleSpecific Example

Challenge: Small sample size.

NOAA Fisheries

0

1

2

3

Specific ExampleSpecific Example

Challenge: Variable PIT-tag detection probability.

NOAA Fisheries

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Specific ExampleSpecific Example

NOAA Fisheries

Detection distribution inappropriate as index of passage distribution.

Daily detection probabilities needed to properly expand detection distribution into passage distribution.

ConceptConcept

Dam 1 detected distribution for Dam 2 detected day.

NOAA Fisheries

Days at Dam 1

Detected N

Day at Dam 2

Detected N

ConceptConcept

Estimated Dam 1 undetected distribution for Dam 2 detected day

Assumption: same distribution.

NOAA Fisheries

Day at Dam 2

Days at Dam 1

Estimated U

Detected U

ConceptConcept

Repeat and sum.

NOAA Fisheries

Days at Dam 1 for first day at

Dam 2

Estimated U

Detected N

Days at Dam 1 for last day at

Dam 2

+…

+…

=

=Days at Dam 1

ConceptConcept

Estimated detection probability for day at Dam 1.

NOAA Fisheries

Det. N

Day at Dam 1

Day at Dam 1

Est. UDet. N

Day at Dam 1

+ (1 – Tran. Prop.)

ConceptConcept

Estimated passage number for day at Dam 1.

NOAA Fisheries

=Estimated detection probability for day at Dam 1

Detected N’

Day at Dam 1Estimated N’

Day at Dam 1

ConceptConcept

Estimated survival to Dam 1.

NOAA Fisheries

Release Number

Estimated N’

All Days at Dam 1

Sum( )

Schaefer MethodSchaefer Method

NOAA Fisheries

t

j j

ijjm

muiu

1 ..ˆ

Estimated undetected at LGR on day i.

Schaefer MethodSchaefer Method

NOAA Fisheries

t

j j

ijjm

muiu

1 ..ˆ

Estimated undetected at LGR on day i.

Schaefer MethodSchaefer Method

NOAA Fisheries

t

j j

ijjm

muiu

1 ..ˆ

Estimated undetected at LGR on day i.

Schaefer MethodSchaefer Method

NOAA Fisheries

t

j j

ijjm

muiu

1 ..ˆ

Estimated undetected at LGR on day i.

Schaefer MethodSchaefer Method

NOAA Fisheries

)1(ˆˆ

1..

.

iii

ii

Tum

mP

Estimated detection probability at LGR on day i.

Schaefer MethodSchaefer Method

NOAA Fisheries

)1(ˆˆ

1..

.

iii

ii

Tum

mP

Estimated detection probability at LGR on day i.

Schaefer MethodSchaefer Method

NOAA Fisheries

i

ii

P

nN

ˆˆ

Estimated passage number at LGR on day i.

Schaefer MethodSchaefer Method

NOAA Fisheries

i

ii

P

nN

ˆˆ

Estimated passage number at LGR on day i.

Schaefer MethodSchaefer Method

NOAA Fisheries

R

N

S

s

i

i 1

ˆ

ˆ

Estimated survival to LGR.

Schaefer MethodSchaefer Method

NOAA Fisheries

R

N

S

s

i

i 1

ˆ

ˆ

Estimated survival to LGR.

Schaefer MethodSchaefer Method

NOAA Fisheries

Adjustments in the passage distribution tails:

- No “detected at LGR” fish: Use LGR to LGO travel time.

- Estimates of 0 or 1: Use spill regression.

- Minor effect on overall estimates.

Schaefer MethodSchaefer Method

NOAA Fisheries

Variance and 95% confidence intervals: Use Bootstrap.

Standard Error estimate: Standard Error of bootstrapped estimates.

95% confidence intervals: 25th and 975th values of the ordered bootstrap estimates.

Wild Chinook Parr Example - OverallWild Chinook Parr Example - Overall

NOAA Fisheries

YearReleaseNumber

EstimatedPassageNumber

EstimatedSurvival

StandardError

95%Lower

Conf. Int.

95%Upper

Conf. Int.

1993 14478 2283 15.8% 0.7% 15.3% 18.2%

1994 12747 2401 18.8% 0.8% 17.6% 20.6%

1995 24417 3289 13.5% 0.3% 12.9% 14.3%

1996 6835 1411 20.6% 1.2% 19.1% 24.0%

1997 5634 1173 20.8% 1.8% 18.6% 25.8%

1998 6225 1516 24.4% 1.0% 23.0% 26.8%

1999 12922 2575 19.9% 0.8% 18.5% 21.7%

2000 13390 2374 17.7% 0.7% 16.7% 19.6%

2001 6526 1276 19.5% 0.6% 18.5% 20.7%

2002 14399 2066 14.3% 0.8% 13.3% 16.4%

Total 117573 20363 17.3%

Average 18.5% 0.9% 17.4% 20.8%

Wild Chinook Parr Example - 1999Wild Chinook Parr Example - 1999

NOAA Fisheries

StreamRelease

Number

EstimatedPassageNumber

"Daily"Estimated

Survival

CJSEstimated

Survival Difference

Bear Valley Creek 820 131 16% 20% -4%

Big Creek 960 156 16% 14% 2%

Cape Horn Creek 270 56 21% 23% -2%

Elk Creek 700 162 23% 23% 0%

Herd Creek 959 210 22% 19% 3%

Lake Creek 545 79 14% 20% -5%

Lower Big Creek 467 218 47% 38% 9%

Loon Creek 1029 286 28% 33% -5%

Marsh Creek 769 218 28% 23% 5%

Salmon River South Fork 998 143 14% 12% 2%

Secesh River 936 136 15% 14% 0%

Sulfur Creek 443 72 16% 15% 2%

Valley Creek 1001 174 17% 19% -1%

Total 9897 2041 21% 20% 1%

Lower Big Creek - 1999Lower Big Creek - 1999

NOAA Fisheries

10%

20%

30%

40%

Dete

cti

on

Prob

ab

ilit

y

CJS = 33% “Daily” = 28%

Det

ecti

on P

rob

abili

ty


Top Related