bryce glaser dan rawding (wdfw). biological reference points (brp) ≠ escapement goals brp are...
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Bryce Glaser Dan Rawding
(WDFW)
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Biological Reference Points (BRP) ≠ Escapement Goals BRP are quantitative
Spawners at Maximum Sustainable Yield (MSY)Spawners needed to seed habitat Based on current data not on future expectations
Escapement goals are from policy-technical interaction and ideally are based on fish management philosophyshould include quantitative analysisrisk to persistencefishery stability or maximization of catchuncertainty
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OverviewBackground/Available DataApproach & AnalysisInitial Results/ Model PerformanceDevelopment of BRPEscapement Goals for LCR SteelheadSummary/ImplicationsQuestions
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Lower Columbia Region (LCR)Four summer and fourteen winter steelhead
populations in Washington
Iteroparous with repeat spawner rate of 5% to 15%
Hatchery releases beginning in 1950’s with Mitchell Act program
Different populations have different levels of hatchery influence and broodstock types
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HatcheriesHatchery program
Chambers Cr winters (Puget Sound origin)Skamania summers (Washougal origin)Local broodstocks (Cowlitz, Kalama, Abernathy)
Relative Reproductive Success (RRS) to smolt stageChambers (6%) measured in Forks Creek,Skamania (30-35%)Kalama & Clackamas River, Wild Broodstock (>80% -adult stage Hood &
Kalama)
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HarvestMainstem Columbia River - Mixed
Stock Fisheriescommercial fisheries- managed for < 2%
incidental catch Stock composition of steelhead catch in
both commercial and Treaty fisheries (above BON) is unknown
Sport fisheries have been operated under wild steelhead release since 1984
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Adult Trapping Mark/Re-sight via Snorkeling
Redd Surveys Juvenile Trapping
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Smolt Trapping with concurrent Adult Escapement data
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LCR Steelhead ChallengesShort data series and high measurement
error for redd counts (coefficient of variation ~ 30%)
Standard salmon spawner to adult recruit relationships do not account for iteroparity
Ocean survival has varied over 10-fold in the LCR introducing much variation in adult recruits
Different proportions of hatchery spawners with limited measurements of RRS
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ApproachStandardized spawners into wild equivalents
using appropriate RRS estimates to discount hatchery spawners to the smolt stage
Standardized SR data into fish density (fish per square kilometer of drainage area)
Developed spawner to smolt relationships to reduce environmental variation caused by 10-fold changes in marine survival and lack of mainstem Columbia River catch estimates by stock
Autocorrelation is not an issue using spawner and smolts
Hierarchical modeling (meta-analysis) using different spawner-smolt-relationships (SRR)
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Hierarchical ModelingBorrow strength from other curves -
from those with more dataEstimates shrink towards the mean,
which yields improved precision of individual BRP
Compromise between individual and fully pooled estimates
Reduces overfitting of individual curvesAllows individual curves to be fit in
cases, where there are few data points, outliers, etc.
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Common set of steelhead spawner to smolt relationships
Spawner to smolt functions come from a random sample of S/R distributions that can be hierarchically modeled.
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AnalysisBarrowman, N.J., R.A. Meyers, R.
Hilborn, D.G. Kehler, and C.A. Field. The variability among populations of coho salmon in maximum reproductive rate and depensation. Ecological Applications 2003:784-793. (used km available)
Smolts and spawners per sq. km of drainage area, with spawners adjusted for RRS data
Bayesian hierarchical analysis using WinBUGS with Lognomal error
Vague priors similar to Barrowman so the results are data driven not prior driven
Checked convergence with Brooks-Gelman-Ruben (BGR) statistics.
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HierarchicalBHRHS
Wild Equivalent Spawners per Square Kilometer
Sm
olts per Square K
ilometer
Grays
0
30
60
90
120
150
0 2 4 6
Mill
0
30
60
90
120
150
0 2 4 6
Abernathy
0
30
60
90
120
150
0 2 4 6
Germany
0
30
60
90
120
150
0 2 4 6
Coweeman
0
30
60
90
120
150
0 2 4 6
Kalama
0
30
60
90
120
150
0 5 10 15 20
Cedar
0
30
60
90
120
150
0 2 4 6
Trout
0
30
60
90
120
150
0 2 4 6
Wind
0
30
60
90
120
150
0 2 4 6
NF Toutle
0
30
60
90
120
150
0 2 4 6
EF Lewis
0
30
60
90
120
150
0 2 4 6
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ResultsDeviance Information Criteria (DIC) is a
Bayesian analog for Akiake Information Criteria (AIC)
Using DIC for model selection BH and HS models were preferred over Ricker model.
These results are consistent with other analysis for yearling anadromous salmonids, that dome shape models (Ricker) do not fit this life history type well.
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Mill
0
30
60
90
120
150
0 2 4 6
Germany
0
30
60
90
120
150
0 1 2 3 4 5
Wind
0
30
60
90
120
150
0 2 4 6
•Yellow Line- drainage area only•Fitting a curve with no S/R data
•Pink Line – Individual estimate
Model Performance
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Wild Equivalent Spawners per Sq. KM
Smolts per Sq km
Snow Cr, WA-Puget Sound (40 sq km)
0
20
40
60
80
0 1 2 3 4 5 6
Asotin Cr, WA-Snake (1166 sq km)
0
20
40
60
80
0 1 2 3 4 5 6
NF Scapoose, OR-LCR (61 sq km)
0
20
40
60
80
0.0 0.5 1.0 1.5 2.0
NF Newhalem, OR-Coast (112 sq km)
0
20
40
60
80
100
0 4 8 12 16 20
EF Trask, OR-Coast (74 sq km)
0
20
40
60
80
100
120
140
0 1 2 3 4 5 6
Mill-Siletz, OR-Coast (33 sq km )
0
20
40
60
80
0 1 2 3 4 5 6 7
Mill-Yaquina, OR-Coast (19 sq km)
0
20
40
60
80
0 1 2 3 4 5 6
Cascade, OR-Coast (14 sq km)
0
20
40
60
80
0 1 2 3 4 5
SF Smith, OR-Coast (65 sq km)
0
20
40
60
80
100
120
140
0 3 6 9 12 15
Basin Model w/95%CI Superimposed over PNW Population outside LCR
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Biological Reference Points
B = spawners needed to produce 50% of asymptotic smolt estimateS* = inflection point in curve, spawners needed to seed habitatMSP = spawners needed to produce maximum smolt productionK = smolt capacity estimateProductivity = slope of curve at origin; est. of population resiliency.
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Estimates of Seeding Levels
0%
20%
40%
60%
80%
100%
0 5 10Wild Equivalent Spawners per Square
Kilometer
Cre
dib
le I
nte
rval
S*
MSP
B
Capacity Estimates
0%
20%
40%
60%
80%
100%
0 50 100 150 200
Smolts per Square Kilometer
Cre
dib
le I
nte
rval
HS
BH
Productivity Estimates
0%
20%
40%
60%
80%
100%
0 100 200 300 400
Smolts per Square Kilometer
Cre
dib
le I
nte
rval
HS
R
BH
•Seeding Levels = 0.4 to 1.7Wild Equiv.Spawners per KM^2
•Smolt Capacity = 43 to 55 smolts per KM^2
• Productivity = 66 to 137 smoltsper KM^2
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Historic Escapement GoalsBest professional opinion
US v. Oregon TAC recommended 1000 steelhead spawners for the Wind River.
Application of Boldt Case (Puget Sound & Washington Coast) Potential Parr Production model to LCRLucas and Nawa (1985) recommended 1400 steelhead
spawners for the Wind River
Hierarchical Modeling ApproachUsing BRP - ~500 spawners for the Wind
River (using HS model)
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Summary & ImplicationsBRP are quantitative
useful in developing Escapement Goals.Hierarchical Model Approach can provide
estimates of BRP even for populations with little or no SR data.
Individual curves are improved when data is available.
Basin model sensitive to RRS and HOS estimates, and when spawners use a low fraction of drainage area (Mill-LCR, NF Scappoose)
Basin model potentially useful outside LCR except very small tributaries (OR coast)
Next Steps – model improvement by incorporating steelhead distribution and/or GIS attributes
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Summary & ImplicationsIn LCR populations – it appears we have been
achieving seeding levels or higher in most years.In LCR - 12/95 (13%) spawner points < S*Reassessment of current Escapement Goals for
LCR steelhead populations is likely warranted.If LCR steelhead recovery requires improvement in
adult abundance: increase habitat capacity because we are
seeding habitat.and increase wild stock productivity by
decreasing pHOS
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•Multiple funding sources •NOAA through Mitchell Act, •Bonneville Power Administration• WDFW
•Data•Asotin – Mark Schuck (WDFW)•OR Coast – Eric Suring (ODFW)•Snow Ck – Randy Cooper (WDFW)
•WDFW•KRT - Coweeman and Kalama•WSPE - Mill, Abernathy, Germany, NF Toutle•Region 5 Fish Mgt - Grays, Cedar, EF Lewis, Wind, & Trout •Many techs and bios who collected 95 spawner and smolt points since 1977.
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