An Approach for Developing Biological Reference Points for Steelhead Population in Lower Comlubia.ppt

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An Approach for Developing Biological Reference Points for Steelhead Population in Lower Comlubia.ppt Powered By Docstoc
					 Bryce Glaser
Dan Rawding
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 philosophy
     should include quantitative analysis
     risk to persistence
     fishery stability or maximization of catch
     uncertainty
   Background/Available Data
   Approach & Analysis
   Initial Results/ Model Performance
   Development of BRP
   Escapement Goals for LCR Steelhead
   Summary/Implications
   Questions
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
 Hatchery program
   Chambers Cr winters (Puget Sound origin)
   Skamania summers (Washougal origin)
   Local broodstocks (Cowlitz, Kalama, Abernathy)
 Relative Reproductive Success (RRS) to smolt
   Chambers (6%) measured in Forks Creek,
   Skamania (30-35%)Kalama & Clackamas River,
   Wild Broodstock (>80% -adult stage Hood & Kalama)
 Mainstem Columbia River - Mixed Stock
   commercial fisheries- managed for < 2% incidental
   Stock composition of steelhead catch in both
    commercial and Treaty fisheries (above BON) is
   Sport fisheries have been operated under wild
    steelhead release since 1984
Adult Trapping   Mark/Re-sight via Snorkeling

 Redd Surveys      Juvenile Trapping
Smolt Trapping with concurrent Adult Escapement data
LCR Steelhead Challenges
 Short 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
 Standardized 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
 Hierarchical modeling (meta-analysis) using
  different spawner-smolt-relationships (SRR)
Hierarchical Modeling
 Borrow strength from other curves - from
  those with more data
 Estimates shrink towards the mean, which
  yields improved precision of individual BRP
 Compromise between individual and fully
  pooled estimates
 Reduces overfitting of individual curves
 Allows individual curves to be fit in cases,
  where there are few data points, outliers, etc.
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
 Barrowman, 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.
Smolts per Square Kilometer


                              Wild Equivalent Spawners per Square Kilometer
 Deviance 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.
Model Performance

           •Yellow Line- drainage area only
              •Fitting a curve with no S/R data
           •Pink Line – Individual estimate
Basin Model w/95%CI Superimposed over PNW Population outside LCR





                Wild Equivalent Spawners per Sq. KM
                      Biological Reference Points

B = spawners needed to produce 50% of asymptotic smolt estimate
S* = inflection point in curve, spawners needed to seed habitat
MSP = spawners needed to produce maximum smolt production
K = smolt capacity estimate
Productivity = slope of curve at origin; est. of population resiliency.
•Seeding Levels = 0.4 to 1.7
Wild Equiv.Spawners per KM^2
•Smolt Capacity = 43 to 55 smolts per
• Productivity = 66 to 137 smolts
per KM^2
Historic Escapement Goals
 Best 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 LCR
    Lucas and Nawa (1985) recommended 1400 steelhead spawners for
     the Wind River

Hierarchical Modeling Approach
  Using BRP - ~500 spawners for the Wind River
   (using HS model)
  Summary & Implications
 BRP 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
 Basin model potentially useful outside LCR except very
  small tributaries (OR coast)
 Next Steps – model improvement by incorporating
  steelhead distribution and/or GIS attributes
  Summary & Implications
 In 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
     increase habitat capacity because we are seeding habitat.
     and increase wild stock productivity by decreasing pHOS
•Multiple funding sources
    •NOAA through Mitchell Act,
    •Bonneville Power Administration
    • WDFW
   •Asotin – Mark Schuck (WDFW)
   •OR Coast – Eric Suring (ODFW)
   •Snow Ck – Randy Cooper (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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