comparative survival study annual meeting - fpc … from the 2013 css annua… · comparative...

Comparative Survival Study Annual Meeting Date / time: April 30th 2013 8:30 AM to 1:30 PM Place: Water Resources Center

4600 SE Columbia Way Vancouver, WA 98661

We ask that you please hold your questions until either of the two question and answer timeslots. Each presentation will have slide numbers for referencing back.

Time (Minutes) Presenter

CSS 2012 Annual Report 08:30 Introduction to CSS (30) Jack Tuomikoski 09:00 Upper Columbia River Chinook and Steelhead (15) Robin Ehlke 09:15 Snake River Fall Chinook (20) Jerry McCann 09:35 Questions / Discussion (15)

09:50 Break (15)

CSS 2013 Workshop

10:05 Introduction and Background (20) Howard Schaller

10:25 Scenario Development and Prospective Models (45) Margaret Filardo & Charlie Petrosky

11:10 Experimental Management Choices (20) Steve Haeseker 11:30 Questions / Discussion

To end at approximately 1:30 PM

Introduction to Comparative Survival Study

Jack TuomikoskiCSS Annual Meeting 30th Apr, 2013

2

2012 Annual Report

Workshop (March 7th-8th, 2013)

• GOALS:Review a draft design for a management experiment to

increase the amount of voluntary spill at FCRPS projects

Provide recommendations to strengthen the proposed experiment

Opportunity for leading researchers and professionals involved in the assessment of dam operations on various components of salmon and steelhead survival rates to share and compare recent results.

20 attendees from agencies and universities

• Facilitated by ESSA Technologies Ltd.

2012-2013 Overview

3

Initiated in 1996 by states, tribes & USFWS to estimate survival rates at various life stages

• Designed to assess hydrosystem operations on state, tribal, and federal fish hatcheries and LSRCP

• PATH – “can transportation . . . compensate for the effect of the hydrosystem?”

• NPCC has established the need to collect annual migration characteristics including survival

• NOAA biological opinions require research, monitoring and evaluation

Management-oriented large scale monitoring• Observational study• Aligned with basin wide monitoring needs (RME)

Background

4

Background

GOALS 1.Quantify the efficacy of transportationDevelop a more representative control group

2.Compare survival rates within and across species

3.Establish long term data set

5

Background

CSS data is derived from PIT tags

• Tagged specifically for CSS

• Cooperative marking between CSS and other research studies reduce costs/handling, eliminate duplication

• Groups marked for other studies

6

Background

Collaborative scientific process was implemented for study design and to perform analyses

CSS project independently reviewed and modified a number of times• Draft report typically posted – Aug 31st• ISAB, ISRP and other entities

7

History of ISAB/ISRP Reviews of CSS

1997 – ISAB First review

1998 – ISAB Extend to other species & life history types (Steelhead)nonparametric bootstrap approach

2002 – ISRP Additional evaluate bootstrap, compare with likelihood methods, Monte Carlo simulator evaluation

8


2003 – ISAB Review of flow augmentation“understanding of the relation betweenreach survival, instantaneous mortality,migration speed, and flow”

2006 – ISAB Review of 2005 CSS report1) “finer scale analyses of the relationships

between survival and specific operationalactions or environmental features”

2) Develop a ten year summary report

9


2007 – ISAB/ISRP Review CSS “10-year” report1) continue coordination

cost savings/ avoid redundancy

2) Evaluate if PIT tag SARs are less than run reconstruction SARs

2009 – ISAB Tagging ReportCompare CSS SARs with Run Reconstruction SARs

>2009 ISAB annually reviews CSS reports

10

DESIGN• WDFW, CRITFC, USFWS, ODFW, IDFG

IMPLEMENTATION & TAGGING• FPC: Logistics, coordination• PTAGIS: Raw Data; FPC: Reports, Estimates

DATA PREPARATION & ANALYSIS• CSS Oversight Committee• Fish Passage Center

REGIONAL REVIEW• Draft on BPA & FPC websites• Regional Public Review; ISAB, ISRP, FPAC, NMFS,

etc.

FINAL REPORT• Posted on BPA & FPC websites

The CSS is a joint project of the state & tribal fishery managers and the USFWS

TEMPORAL COVERAGE

11

Snake RiverLonger Time SeriesMore groups developed

~20 stocks sp/su/fall Chinook,

steelhead, sockeye

Middle Columbia RiverBegin in 2000 [BOA detect] ~ 9 stocks

Upper Columbia RiverBegin in 2000 [BOA detect] ~8 stocks

12

Spatial Coverage: Hatchery Chinook

13

Spatial Coverage: Wild Chinook

14

Spatial Coverage: Snake River Hatchery Steelhead

15

Spatial Coverage: Wild steelhead

SR

LGR

MCN

BON

FRESHWATER

ESTUARY

OCEAN

Smolt Survival Rearing Habitat Actions

Hydro-system Actions

SLGR-MCN

SMCN-BON

16

LGSLMNIHR

JDA

LGR

BON

FRESHWATER

ESTUARY

OCEAN

Adult Success


Wild

Hatchery

Har

vest

Man

agem

ent

Estuary Habitat Actions

Hydro-system ActionsTransportation

Effects

17

MCNIHR

LGR

BON

FRESHWATER

ESTUARY

OCEAN


Har

vest

Man

agem

ent

Estuary Habitat Actions

Hydro-system ActionsTransportation

Or Bypass

effects

SNAKE RIVER SARS

18

BON

FRESHWATER

ESTUARY

OCEAN

Mid and Upper Columbia R. SARS

Regional Monitoring &

EvaluationJDAMCN

19

20

Long term consistent information collaboratively designed and implemented

Information easily accessible and transparent• CSS PIT-tags accessed by any PTAGIS users, including fisheries

managers, researchers, and academics.

Long term indices (identify bottlenecks) :• Travel Times• In-river Survival Rates• In-river SARs by route of passage• Transport SARs• Adult success, conversion

Comparisons of SARs• Transport to In-River• NPCC Regional SAR goal• By geographic location• By hatchery group• Hatchery to Wild• Chinook to Steelhead

Management questions: hydropower operations, hatchery evaluations, habitat evaluations

What does CSS provide for the region?

21

Snake River Chinook SARs Hatchery Chinook

5 spring 3 summer

Summer > Spring

Highly correlated 1999, 2000, 2008 > 2%

22

Snake River Chinook SARs Hatchery Chinook

5 spring 3 summer

Summer > Spring

Highly correlated 1999, 2000, 2008 > 2%

Wild Chinook Similar results

23

11.5% SARs > 2 77% SARs < 2 11.5% SARs = NS

CSS Results: Chinook SARs

24

Less correlated than Chinook stocks

2008* Highest in time series

Snake River Steelhead SARs

25

Snake River Steelhead SARs

15% SARs > 2 50 % SARs < 2 35% SARs = NS

26

Used to evaluate transportation program (SR stocks)

Ratio of Transported ÷ InriverSARs

Snake River TIR

FRESHWATER

OCEAN

27

TIR is related to in-river survival

As in-river survival increases, TIR decreases

When in-river surv ~ 57%, transport will not be beneficial (for wild stocks)

TIR vs. in-river surv.

28

The success rate for transported was 90% of that for their in-river counter parts (on average)

Transported steelhead strayed about 4.5% and in-river strayed at 0.4% (11:1) Deschutes and John DayThis is a large out-of-basin population as compared to

total natural spawner abundance◦ Hatchery strays identified as limiting factor to recovery of John Day

and Deschutes River stocks (NOAA 2009 Mid C. St. Recovery Plan)

Transported hatchery Chinook strayed about 0.7% and in-river strayed 0.03% (23:1)Columbia above SR confluence

Snake River Adult Success and Straying

29

Developed as monitoring tool and to inform harvest managementUpdate in 2012 report, 7 additional stocks (16 total)

and one more year of data

Age at maturity and jack percentage of Chinook in Snake River and Middle/Upper Columbia were influenced by both stock and year factorsA common sibling model across all stocks may not

perform well

No strong association between age at maturity and transport history for Snake River stocks

Age at Maturity for Chinook

1

SARs and Juvenile Metrics of Upper Columbia Stocks

Robin Ehlke

CSS Annual Meeting Apr 30th 2013

CSS ObjectivesUpper Columbia

Establish long term survival estimates over the full life-cycle of upper Columbia stocksDevelop Smolt to Adult Return rates

(SARs) from the upper-most damDevelop estimates of ocean survival ratesUse additional mark groups as they come

available

2

Upper Columbia Mark GroupsFive Basin-Specific Groups

• Wenatchee BasinHatchery spring Chinook (Leavenworth)Wild ChinookSteelhead (hatchery/wild Cross)

• Entiat-Methow aggregateWild Chinook

• Wenatchee-Entiat-Methow aggregateWild Steelhead

Three Groups marked at Rock Island Dam• Yearling Chinook, subyearling Chinook,

steelhead• All three are hatchery/wild aggregates

3

Upper Columbia Juvenile and Adult Metrics

Juvenile passage metrics, travel time, instantaneous mortality and survival from Rock Island to McNary DamSmolt to Adult Return ratesIncorporated detection capability at Rocky

Reach DamReport analyses of passage metrics and

SARs relative to environmental variables

5

Smolt to Adult Return

Upper Columbia Smolts from McNary Dam to Bonneville Dam• MCN to BON SARs do not include or account for

juvenile mortality occurring through the Upper Columbia to McNary Dam

• For this reason the MCN to BON reported SARs are biased high

• As an example, for Wenatchee the SARs would be ~ 58% of reported if RIS to MCN juvenile survival were taken into account

6

Steelhead survive slightly better than Chinook

Typically both species’ survival is less than 60%.

A large component of life-cycle is not represented in MCN to BON SARs

7

Rock Island to McNary Juvenile Survival

Juvenile Passage RIS-MCN Metrics/Environmental conditions Fish Travel Time

• Faster with higher flow and with Julian date Instantaneous Mortality

• Decreased for Chinook as spill levels increased at Wanapum and Priest Rapids

• Increased for steelhead with increase in Julian date

Reach Survival• Increased with higher flow and spill

8

Wild and Hatchery Chinook SAR MCN to BON

Entiat/Methow R. Wild Chinook SARs averaged 1.35% (0.5%-3%) Enter Columbia River upstream of Rocky Reach Dam

Wenatchee River Wild Chinook SARs averaged 1.62% (0.8%-3%)Enter the Columbia River upstream of Rock Island Dam

Leavenworth Hatchery Chinook SARs averaged 0.58%Enter the Columbia River upstream of Rock Island Dam

All GroupsExceeded 2% in 2008Do not include Upper Columbia reach2010 data does not include 3-salt fish

9

Wild and Hatchery Chinook SAR MCN to BON

Entiat/Methow R. Wild Chinook SARs averaged 1.35% (0.5%-3%) SARs less when calculate from Rocky Reach back to Bonneville Dam

Wenatchee River Wild Chinook SARs averaged 1.62% (0.8%-3%)Enter the Columbia River upstream of Rock Island Dam

Leavenworth Hatchery Chinook SARs averaged 0.58%Enter the Columbia River upstream of Rock Island Dam

All GroupsExceeded 2% in 2008Do not include Upper Columbia reach2010 data does not include 3-salt fish

10

Wild and Hatchery steelhead SAR MCN to BON

Wild SteelheadSARs averaged 3.97% Aggregate mark group -Wenatchee, Entiat and Methow stocksUpper Columbia reach not included

Hatchery SteelheadSARs averaged 2.16%Wenatchee basin Upper Columbia reach not included

11

Wild and Hatchery steelhead SAR MCN to BON

Wild SteelheadSARs averaged 3.97%SARs less when calculate from Rocky Reach back to Bonneville Dam (Entiat and Methow stocks)

Hatchery SteelheadSARs averaged 2.16%Wenatchee basin Upper Columbia reach not included

12

Chinook and Steelhead SAR RIS to BON

Hatchery/Wild Yearling Chinook SARs averaged 0.3% Bypass inoperable during spring of 2003

– no data

Hatchery/Wild Subyearling Chinook SARs averaged 0.6%

Hatchery/Wild Steelhead SARs averaged 1.17% Bypass inoperable during spring of 2003

– no data

13

16

Reach Survival Comparison of Juvenile Salmon:Snake River to Upper Columbia Stocks (FPC report)

Yearling Chinook

Steelhead

17

Average Percent Spill at Wanapum and Priest Rapids Dams

Smolt Migration Year

Avg.

Per

cent

Spi

ll

18

ConclusionThe Overall Upper Columbia MCN-BON SARs for

2000-2010 Chinook were highly correlated with spring Chinook SARs from the Middle Columbia and with spring/summer Chinook SARs from the Snake River

Indication that upper Columbia stocks have similar responses to shared in-river and ocean life-cycle experiences.

Upper Columbia stocks also showed similar patterns of response to environmental variables when compared to mid Columbia and Snake River Stocks

ConclusionCollaboration and coordination with other Upper

Columbia specific marking efforts increases cost effectiveness and the benefits to the region

Monitoring the effect of hydro system passage on Upper Columbia groups from existing marking is value added for managers

Increase in the number of mark groups/tags and the number of detection sites would strengthen the data.

Recent increase in USFWS marked hatchery steelhead and Chinook will be available for future years (Winthrop, Entiat) 19

The End

20

Snake River Fall Chinook SARs

from PIT groups with low or no Holdover Bias

Presenter: Jerry McCann

CSS Annual Meeting Apr 2013

22

Background CSS was requested to develop estimates of

subyearling fall Chinook SARs.

CSS approach compares SARs for transported fish to fish undetected in-river (C0) at transport dams.

Holdover fish can bias estimates of C0 SARs.

3

Subyearling Fall Chinook PIT-tag Releases

WildResearchProduction

What is a holdover?

Subyearling Migration - fish migrate past dams in year of release/emergence

Holdover – or yearling migration - fish migrate past some dams as yearlings – the year after release/emergence

40

50

60

70

80

90

100

4/7 5/17 6/26 8/5

Avg

Leng

th (m

m)

PROD_SNAKE

PROD_CLWR

40

50

60

70

80

90

100

4/7 5/17 6/26 8/5

Avg

Leng

th (m

m)

PROD_SNAKE

PROD_CLWR

SUR_SNAKE

WILD_SNAKE

5

Release date and length at release for PIT tagged Ch0 in 2009

Later releases at shorter lengths = Higher Holdover Probability

40

50

60

70

80

90

100

4/7 5/17 6/26 8/5

Avg

Leng

th (m

m)

PROD_SNAKE

PROD_CLWR

SUR_SNAKE

WILD_SNAKE

SUR_CLWR

WILD_CLWR

6

Proportion Holdovers by release date for PIT tagged Ch0 in 2009

Later releases at shorter lengths = Higher Holdover Probability

SARs on Fall Chinook

Present SAR data• Present SARs estimates by study category for release

groups from 2006 to 2009 with low or no holdovers

Results of Simulations to calculate potential bias to SARs due to holdover fish• C0 population may be biased low by unmonitored

passage during winter or holdover passage

7

Approach to bias calculation

Used holdover detections exiting hydro-system at Bonneville Dam to calculate holdover population.• Determined the number of Bonneville detections as holdovers

(i.e. possibly passed LGR during shutdown or as yearlings). • Expanded those yearling detections based on detection

probability at Bonneville Dam to a Population.• Expressed Bonneville population in LGR equivalents using a

range of survival estimates (0.25 to 0.75)

Add proportion unmonitored winter passage (i.e. passing LGR to BON during shutdown).• Use late season travel times to IHR to simulate potential

unmonitored winter passage LGR to BON.

8

Results of Simulations – projected potential C0 SAR bias due to holdovers

9

Migration year 2006 Migration year 2007

Migration year 2008 Migration year 2009

Conclusions from Simulations

Most groups particularly production releases have very low potential for holdover bias and are suitable for CSS methodology.When large numbers of late season

migrants and early season holdovers are observed at LGR, CSS approach not presently appropriate due to potential bias.

10

Results of SAR (with jacks) estimates for migr years 2006 to 2009

11

Comparison of Transport vs Inriver SARs (no jacks) for migr years 2006 to 2009

12

Big Canyon Creek Prod

Captain John’s Rapid Prod

Snake River Surrogates

Snake River Wild

Transport Inriver SARs ratios (TIRs) for migr years 2006 and 2008

13

Conclusions

Bias due to holdovers is not a concern for most subyearling production releases and Snake River wild PIT-groups in years analyzed.

SAR estimates can be reliably made using CSS methodology

SARs by study category (Transport and In-river) can be reliably estimated and be compared.

14

Howard Schaller

CSS Annual MeetingApril 30, 2013

Introduction & Background for Comparative Survival Study Workshops and Outcomes

1

CSS Workshops• Workshop 2004

– Direct & delayed effects of the hydrosystem on salmon & steelhead survival

Workshop July 2011 – Factors influencing marine survival for Snake River

populations– Build tools to evaluate FCRPS operations

Workshop March 2013 – Management experiment to evaluate increased

voluntary spill

2

3 4 5 6 7 8 dams

Decline in Snake R. Chinook & steelhead associated with dams…

3

Dramatic changes in outmigration conditions

with dams…

marine conditions were not static…

Need to account for both

4

Challenges and management objectives

Multiple factors operating at same time• Capitalize on temporal patterns of variation• Capitalize on spatial patterns of variation• Consistency from multiple lines of evidence

NPCC SAR objectives of 4% average, 2% minimum

5

ApproachWeight of evidence Multiple lines of evidence for relative importance of major factors influencing survival rates

CSS SARs (Chin & Sthd)

SARs (run rec. ‐ Snake Chin & Sthd)

Spawner:recruit (Snake & John Day Chin)

3 4 5 6 7 8dams

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Environmental Contrast

Life cycle

Life stage

Precision & Spe

cificity

6

Estimate hydrosystem and ocean effects using reference populations

Estimate hydrosystem and ocean effects without using reference populations

Examine multiple lines of evidence –consistent results?

7

Spatial contrast

Spawner-recruit & SAR data

Snake R. Chinook populations survived ¼ to 1/3 as well as reference populations since FCRPS completion

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

2000 2002 2004 2006 2008 2010

% SAR

Chinook SARs

John Day

Snake

3 dams vs. 8 dams

8

‐5

‐4

‐3

‐2

‐1

0

1

2

1950 1960 1970 1980 1990 2000 2010

S‐R residuals

John Day

Snake

Temporal Analyses• Influence of river & ocean conditions on survival rates

– Employ long time series:• Pre & post Snake River dam completion• Survival rates for different life stages (S-R residuals, SAR,

marine survival)• Variables for ocean conditions• Variables for river conditions

• For different life stages & species – contrast the set of ocean & river conditions that explain variation in survival rates (temporal)

• Temporal/spatial contrasts to estimate FCRPS impacts – mortality at later life-stages (delayed hydrosystem mortality)

9

California Current brings cold polar water from the north keeping coastal temperatures cool

Broad scale:• Pacific Decadal Oscillation

Near shore:•Coastal Upwelling•Spring Transition•Near shore Temp.

Candidate Ocean Variables

10

Candidate River VariablesWater travel time (Lewiston – BON Dam):

•2 days pre-dam•10-40 days (19 day ave.) post-dam

In-river migrants now pass through:

•up to 8 powerhouses•depending on spill & surface weirs

Collection and transport:•25% - 99% of smolts transported , 1977

11

0.0

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0.6

0.8

1.0

1960 1970 1980 1990 2000 2010

Prop

ortio

n tran

sported

Migration Year

Multiple Regression – long-term data setsSpawner-recruit residuals, SARs & marine survival

showed similar results

Reduced survivalis associated with:

• Warm PDO• Reduced upwelling

• Increased # powerhouse passages (reduced spill)

• Slow WTT• Increased

proportion transported

12

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Model Scenario Development

Presenter: Margaret FilardoCSS Annual Meeting

April 30, 2013

Workshop Tasks

• Develop reasonable estimates of the amount of water that could be spilled (spill caps) at each of the hydroprojects on the Lower Snake and Columbia rivers– using present project configuration; and, – existing TDG waivers/rule changes.

• Develop scenarios for alternate spill programs for modeling exercises.

2

Spill Benefits

• Historic data has consistently shown a juvenile survival advantage.

• Spill is the only mitigation measure provided that can be provided in every flow year.

• Spill can be provided without impact to reservoir elevations.

4

Entrainment of Air

5

Risk Based Spill Program

Survival benefits of spill > potential TDG related

mortality

7

Objectives

• Identify any reasonable physical or biological limitations at each project.

• Determine the spill caps that could be used in the estimation of spill under the various total dissolved gas scenarios.

• Identify representative water years and estimates of spill to develop input for spill variables used in the prospective modeling.

8

Objective 1 Physical Limitations

• Adult Passage Restrictions:– Little Goose - 30% spill limitation– Ice Harbor – 45 Kcfs daytime spill limitation – Bonneville Dam – 110 kcfs spill limit – Adult passage success at various spill levels

• Minimum generation requirements.– Incorporated

9

Objective 2 Determination of Gas Caps

• Source of Gas Cap Spill Levels

– Information developed by the COE for the real time distribution of involuntary spill

10

Objective 3Representative Water Years

• Model the spill scenarios under a range of flow conditions: – low, medium and high.

• Used hourly flow and spill data from 2009, 2010 and 2011.

11

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The Dalles DamRunoff Volume 1929-2012

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15

Development of Scenarios Common Assumptions and Applications

• Used actual hourly flows. • Minimum generation flows as specified in the 2008

Biological Opinion.• Hydraulic capacity of each project equal to flow with all

units operating at the upper end of the 1% efficiency range based on the 2012 FPP.

• No excess generation (lack of market) spill included. • Includes uncontrolled spill in excess of (HC+Planned spill)• No adult passage constraints on daytime spill amounts,

except for Bonneville.• Spill Caps as determined by the COE for the distribution of

Uncontrolled Spill.16

Scenarios• Started with Actual Hourly Flow and

spill for three representative flow years and applied:– Biological Opinion Spill Levels– Spill to the present TDG standards during

fish migration season of 115% FB and120% TR

– Spill to 120% TDG at the TR monitors– Spill to 125% TDG at the TR monitors

17

Resulting Spring Spill Proportions

18

Application of Scenarios

• Provided average proportion spill per project/time period as needed by the models for prospective analyses.

• Spill scenarios were input to the PIT Tag SAR Model and the Run Reconstruction SAR Model.

• Applied the actual spill and 4 spill scenarios to 3 different flow years.

19

Spill Study Implementation Constraints

• To Implement Spill to 120%

– WA DOE would need to make a modification to the Washington State Rule to eliminate the forebay monitors from compliance monitoring.

– OR DEQ already eliminated the forebay monitors from their waivers.

20

• To Implement Spill to 125%:

– WA DOE would need to make a modification to the Washington State Rule to increase the TDG to 125% and use TR monitors only.

– OR DEQ already eliminated the forebay monitors from their waivers, but would need to issue a waiver to the 125% TDG in the tailrace.

21

Summary of GBT Samples (1995-2012) as a function of TDG

22

Prospective Model Development

Presenter: Charlie Petrosky


In‐river Passage RoutesNon‐powerhouse = Spill (traditional or surface spillway weirs)

Powerhouse = Turbine or juvenile collection/bypass

Submersible traveling screen

Collection channel

(3) Turbine

Forebay

Tailrace

(1) Spillway Reservoir

(2) Juvenile Bypass Systems

Gatewell

Direct survival:spill > bypass > turbine

Direct & indirect survival(delayed mortality):

spill > bypassspill > turbine

2

Spill metrics

• Average spill proportion

• N_Powerhouse – assume fish pass in spill proportionally to amount of water spilled

• N_PHPIT – estimate the proportion of fish passing in spill as a function of spill, flow and surface passage structures

3

Spill metrics

• Average spill proportion – commonly used metric (FPC, CSS, NOAA analyses, Haeseker et al. 2012)

• N_Powerhouse– Expected number of powerhouse passages (turbine + collection/bypass) for defined time period across N‐dams (Petrosky & Schaller 2010)

– N_dams changed over historic period ‐ Snake River fish faced 2 dams in 1956 to 8 dams by 1975

– Can account for variation during development (2 through 8 dams) and post‐development (8 dams)

4

New spill metric

• N_PHPIT– recommendation from 2011 CSS Workshop– expected number of powerhouse passages based on PIT tags– accounts for spill passage efficiency

• spillway passage proportion differs by dam, flow, spill proportion, surface collectors, and species

5

Fish Guidance Efficiency (FGE)

• Proportion of powerhouse fish that are “guided” to collection/bypass systems via screens

• FGE = Bypass / Powerhouse• Powerhouse = Bypass / FGE

• If you have bypass and FGE estimates, then you can estimate powerhouse passage

6

New spill metric

N_PHPIT approach:• Summarize CJS detection probabilities• Develop models for detection probabilities as function of spill, flow, surface passage

• Apply best‐fit model and FGE estimate to estimate spillway passage

• Estimates validated using telemetry data

7

Spill metricsLower Monumental Dam steelhead

0.0 0.2 0.4 0.6 0.8 1.0

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Prop.Spill

Det

ectio

n pr

obab

ility

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Observed

Pre

dict

ed

8


0.0 0.2 0.4 0.6 0.8 1.0

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Prop.Spill

Det

ectio

n pr

obab

ility

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Observed

Pre

dict

ed

FGE = 0.62

9


0.0 0.2 0.4 0.6 0.8 1.0

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Prop.Spill

Det

ectio

n pr

obab

ility

0.0 0.2 0.4 0.6 0.8 1.0

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1.0

Observed

Pre

dict

ed

FGE = 0.62

Detection prob. = f (spill, flow, spillway weir)

10


0.0 0.2 0.4 0.6 0.8 1.0

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Prop.Spill

Det

ectio

n pr

obab

ility

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Observed

Pre

dict

ed

0.00.10.20.30.40.50.60.70.80.91.0

0 0.2 0.4 0.6 0.8 1

50K flow80K flow160K flowPetrosky‐SchallerTel. 06, 07, 08, 09

Prop. Spill

Spillway passage

11


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Det

ectio

n pr

obab

ility

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Observed

Pre

dict

ed

0.00.10.20.30.40.50.60.70.80.91.0

0 0.2 0.4 0.6 0.8 1


Prop. Spill

Spillway passage

12


0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Prop.Spill

Det

ectio

n pr

obab

ility

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Observed

Pre

dict

ed

0.00.10.20.30.40.50.60.70.80.91.0

0 0.2 0.4 0.6 0.8 1


Prop. Spill

Spillway passage

Powerhouse = 1 ‐ Spillway13

Factors influencing detection probabilities

Project Flow Spill % Flow * Spill % RSW/TSWLGRLGSLMN

Chinook IHRMCNJDATDA NABON NA

LGRLGSLMN

Steelhead IHRMCNJDATDA NABON NA

14

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

1950 1960 1970 1980 1990 2000 2010Smolt year

Retrospective ‐ # powerhouse passages

N_Powerhouse

N_PH_PIT

15

Spill Metric Summary• N_Powerhouse and N_PHPIT spill metrics are highly correlated

– SPE > 1:1– More deviation between spill metrics in recent years

• Addressed 2011 CSS Workshop recommendations to account for spill efficiency at different flow, spill, & configuration based on detection efficiencies– Recent years, increased spill across flow conditions, including lower Q – Spill at lower Q effective to pull fish away from powerhouse– Presence of spillway weirs

• Prospective analyses of alternative spill scenarios used N_Powerhouse and N_PHPIT

16

Prospective tools• Generate distribution of SARs from retrospective models for range of river and ocean conditions for alternative spill scenarios

• Summarize distributions relative to desired goals

17

010203040506070

0% 1% 2% 3% 4% 5% 6%

BiOp

Alternative Spill

Prospective tools• Summarize distributions relative to desired goals (e.g., population viability)

18

0

10

20

30

40

50

60

70

0% 1% 2% 3% 4% 5% 6%

Undesirable

Prospective tools• Summarize distributions relative to desired goals (e.g., NPCC SAR goals, Recovery)

19

0

10

20

30

40

50

60

70

0% 1% 2% 3% 4% 5% 6%

Desirable

Prospective Models ‐ Long Time Series • Model Coefficients: best models

– Ocean variables• Capture range of conditions for predictions

– River Variables• Water Velocity (WTT) • N_Powerhouse ‐ with increased spill• Proportion Transported with increased spill

• Project SARs for Alternative Spill Scenarios – Range of flow conditions (High, Average, Low)– Range of ocean conditions (Good, Average, Poor)

21

0

0.1

0.2

0.3

0.4

0.5

Biop 115/120 120 125

Prob

ability of SA

Rs below

1%

Probability of Chinook SAR Predictions less than 1%

Avoid Undesirable SARs

22

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Biop 115/120 120 125

Prob

ability of SA

Rs abo

ve 2%

Probability of Chinook SAR Predictions greater than 2%

Achieve Desirable SARs

Prospective models – long time series• Simulations show survival rate increases from BiOp to

alternative spill scenarios:– Chinook life‐cycle survival rates– Chinook and steelhead SARs – All flow levels, especially low flows

• Complimentary analyses to more detailed, CSS PIT tag data sets– Consistent response between short and long time series

• Help measure future spill responses

• Experimental Management focused primarily on CSS PIT tag data sets (next presentation)

23

Presenter: Steve Haeseker


Experimental Spill Management Design

Plan for measuring response to a treatment

‐ Treatment = increase in spill for fish passage

‐ Response = change in survival

‐ Plan = implement CSS monitoring methods

What is experimental design?

2

‐ Large change (perturbation)

‐ High precision of measured response variable

‐ High degree of replication

‐ Minimize and account for confounding factors

Elements of “good” experimental design

3

Which response should we measure?

Response Precision Change

Juvenile travel time High Low

Juvenile survival Low Medium

Ocean survival Medium Medium‐High

Smolt‐to‐Adult Survival High High

4

SARs should be the primary response variable monitored (but not the only one)

Advantages:

‐ Highest precision (estimate is mainly counts)

‐ Highest expected change across spill levels

‐ Easiest to collect consistently

‐ Incorporates freshwater and delayed mortality

Disadvantages:

‐ Requires 2‐3 year lag for adult returns

‐ Influenced by both freshwater and ocean conditions5

What are the magnitudes of expected SARs under the alternative spill operations?

6

Integrating across the four main sources of variability

Seasonality

Four cohorts

Flow

Three flow years

Spill

Four spill levels

Ocean

PDO level

Combinations influences the SAR response

7

Used SAR model to project SARs:

‐ Across four release cohorts

‐ Three flow levels (low, medium, high)

‐ Four spill operations (BIOP, 115/120, 120, 125)

‐ PDO conditions over 1900‐2012

8

Integrating across river and ocean conditions‐ steelhead

0

10

20

30

40

50

60

70

0% 1% 2% 3% 4% 5% 6%

BIOP_Low

Projected SAR

Frequency

9


0

10

20

30

40

50

60

70

0% 1% 2% 3% 4% 5% 6%

BIOP_Low

BIOP_Medium

Projected SAR

Frequency

10


0

10

20

30

40

50

60

70

0% 1% 2% 3% 4% 5% 6%

BIOP_Low

BIOP_Medium

BIOP_High

Projected SAR

Frequency

11

0

10

20

30

40

50

60

70

0% 1% 2% 3% 4% 5% 6%

BIOP_Low


Projected SAR

Frequency

12

0

10

20

30

40

50

60

70

0% 1% 2% 3% 4% 5% 6%

BIOP_Low

115_Low


Projected SAR

Frequency

13

0

10

20

30

40

50

60

70

0% 1% 2% 3% 4% 5% 6%

BIOP_Low

115_Low

120_Low


Projected SAR

Frequency

14

0

10

20

30

40

50

60

70

0% 1% 2% 3% 4% 5% 6%

BIOP_Low

115_Low

120_Low

125_Low


Projected SAR

Frequency

15

Summarized probability distributions for SARs at various levels

‐ Undesirable (< 1%): strong link to viability

‐ Desirable (> 2%): NPCC regional goal

16

0

10

20

30

40

50

60

70

0% 1% 2% 3% 4% 5% 6%

BIOP_Low

115_Low

120_Low

125_Low


Projected SAR

Frequency

Undesirable

17

0

10

20

30

40

50

60

70

0% 1% 2% 3% 4% 5% 6%

BIOP_Low

115_Low

120_Low

125_Low


Projected SAR

Frequency

Desirable

18

Probability

Chinook‐ Undesirable (< 1% SARS)

0%

25%

50%

75%

100%

125 120 115/120 BIOP

Spill Treatment 19

60%Since ‘98: 65%

Probability

Chinook‐ Desirable (> 2% SARS)

Spill Treatment

0%

25%

50%

75%

100%

125 120 115/120 BIOP

20

14%Since ‘98: 10%

How to measure whether change has occurred?

1) Test against expected frequency?

For Chinook, 60% of SARs should be in desirable range with 125% spill

21

2) Compare new SARs against prior SARs?

2a) Compare new SARs against “analog” years with similar flow, spill and/or PDO conditions?

22


3) Compare new SARs against model predictions?

‐WTT, Spill and PDO known before adults return‐Within range of model uncertainty?

23


4) Update and refine model parameters over time to determine whether associations are changing?

5) Use multiple sources of data (FTT, SH, SO, SAR, stock‐recruit residuals, run‐reconstruction SARs) to evaluate whether change has occurred?

24


6) All of the above!!!

25


1) SARs were most critical response to consider

2) Use all five SAR comparisons to assess overall strength of response

Workshop Comments and Recommendations

26

3) Assess how increased spill will affect detection efficiency and the precision of SARs

‐ New spillway detectors will increase detections

4) Improve communication of differences between spill scenarios and terminology for different audiences


27

5) Five years likely insufficient to achieve desired learning given variability in ocean and flow conditions

6) Consider linking duration to next Biological Opinion, implementing when learning will be maximized, developing stopping rules, and assess within‐season experiments


28

Summary:

• Experimental design = a work in progress– CSS Workshop final report with independent reviewer comments ~ Summer 2013

• Definition of spill scenarios for simulations based on what appears technically possible with current FCRPS configuration

• Not part of any existing implementation plan or current BiOp consultation

Summary:• Projections suggest spill to 115/120 or higher would:

– reduce risk of very low SARs (<1.0%)– increase likelihood of SARs >2% (the lower end of NPCC 2‐6% SAR goals)

• Expected benefits to Upper‐ & Mid‐Columbia stocks – These stocks provide for additional monitoring/learning

Summary:

• Simulations are encouraging in terms of:– expected response (conservation benefit)– likelihood of detecting response (learning)

BPA Project 19960200

comparative survival study annual meeting - fpc … from the 2013 css annua… · comparative...

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