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Covariation in Productivity of Mid-Columbia Steelhead Populations

Explore covariation in productivity among Mid-Columbia Steelhead populations using spawner-recruit analysis, data on population abundance, recruitment indices, and model comparisons to evaluate growth rates and capacity.

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Covariation in Productivity of Mid-Columbia Steelhead Populations

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  1. Covariation in Productivity ofMid-Columbia Steelhead Populations Brian Pyper & Steve Cramer S.P. Cramer & Associates, Inc. 600 N.W. Fariss Road Gresham, OR 97030 www.spcramer.com

  2. Mid – Columbia Study Area

  3. Background • Mid-Columbia steelhead ESU listed as threatened • NMFS uses four measures to evaluate viable salmonid populations (McElhany et al. 2000): • Population abundance • Population growth rate (productivity) • Spatial structure • Diversity

  4. Background • “Lambda” analysis a key tool used by NMFS to assess productivity (Homes 2001; McClure et al. 2003) • “Lambda” measures population growth rate and extinction risk using time series of escapement data (increasing or decreasing trend?) • Model is not mechanistic • Assumes no density dependence in spawner-recruit dynamics

  5. Spawner-recruit analysis • Examined spawner-recruit data for 8 populations (Cramer et al. 2005) • Estimated intrinsic growth rates and capacity • Compared 4 spawner-recruit models: • Density independent model • Ricker model • Beverton-Holt model • Hockey-stick model • Used simulations to examine potential bias

  6. Data • Dam counts of natural-origin spawners : • Deschutes • Yakima • Umatilla • Redd counts (index) for 5 John Day subpopulations: • Upper and Lower Mainstem • South, Middle, and North Forks • Recruitment indices based on available harvest and age-structure data

  7. Population abundance of natural-origin steelhead in the Middle Columbia ESU, 1978-2004

  8. Population abundance of natural-origin steelhead in the Middle Columbia ESU, 1978-2004

  9. Population abundance of natural-origin steelhead in the Middle Columbia ESU, 1978-2004

  10. Covariation in recruitment • Escapement indices correlated (Avg. r = 0.63) • Suggests shared influence of freshwater or marine conditions on survival • Suggests limited measurement error • Next step: Fit spawner-recruit models …

  11. Fits of the spawner-recruit models to the North Fork data set of the John Day population (DI = density-independent model, RK = Ricker model, HS = logistic hockey-stick model, and BH = Beverton-Holt model). 10 1:1 DI 8 6 Recruit Index HS BH 4 85 RK 88 2 87 86 0 0 2 4 6 8 Spawner Index

  12. Model comparisons • Used the AIC model-selection criterion • Beverton-Holt and Hockey-stick models “best” across data sets • But many unstable fits and unreasonably high estimates of intrinsic growth rate (alpha) Range in Alpha (Recruits per spawner) Beverton-Holt: 5.5 to 72.9 Hockey-stick: 2.4 to 20.8 Ricker: 2.6 to 5.2

  13. Model comparisons • Ricker model stable with biologically reasonable estimates of growth rate (alpha) • Ricker fits much better than Density- Independent model for all 8 data sets • Note: Estimates of capacity similar across forms (Ricker, Beverton-Holt, Hockey-stick) • Density Independent model assumes no limit to capacity

  14. Fits of the Ricker and Density-independent models JD North Fork Deschutes 10 10000 8 6 6000 85 86 87 4 85 88 2 88 2000 86 87 0 0 0 2 4 6 8 0 2000 4000 6000 8000 10000 Recruit Index Umatillla Yakima 3000 3000 2000 2000 87 88 85 85 87 86 1000 1000 86 88 0 0 0 1000 2000 3000 0 500 1000 1500 2000 2500 Spawner Index

  15. Fits of the Ricker and Density-independent models JD Upper Mainstem JD Lower Mainstem 15 15 85 10 10 85 88 86 5 5 87 87 88 86 0 0 0 5 10 15 0 2 4 6 8 10 12 14 Recruit Index JD South Fork JD Middle Fork 20 15 15 85 88 10 85 87 10 86 5 87 5 86 88 0 0 0 5 10 15 20 0 5 10 15 Spawner Index

  16. 14 12 10 8 Ricker Alpha (Recruits/Spawner) 6 ` 4 2 0 Upper Lower South Fork Middle North Fork Deschutes Umatillla Yakima Mainstem Mainstem Fork Ricker estimates of intrinsic growth rate (alpha) Average = 3.4 recruits per spawner

  17. Ricker estimates of intrinsic growth rate (alpha) Average = 3.4 recruits per spawner 14 Average for Density- Independent models = 1.4 Recruits/Spawner 12 10 8 Ricker Alpha (Recruits/Spawner) 6 ` 4 Ricker 2 0 Upper Lower South Fork Middle North Fork Deschutes Umatillla Yakima Mainstem Mainstem Fork

  18. Ricker estimates of capacity: unfished equilibrium spawner abundance (S*) 10,000 Ricker S* 8,000 6,000 Spawner Abundance ` 4,000 2,000 0 Deschutes Umatillla Yakima

  19. Ricker estimates of capacity: unfished equilibrium spawner abundance (S*) 10,000 Ricker S* 8,000 Recent 5-yr geometric mean 6,000 Spawner Abundance ` 4,000 2,000 0 Deschutes Umatillla Yakima

  20. Ricker estimates of capacity: John Day 20 Recent 5-yr geometric mean Ricker S* 15 Redds per Mile 10 5 0 Upper Lower South Fork Middle Fork North Fork Mainstem Mainstem

  21. Influence of 1985 – 1988 brood years: Density dependence or poor ocean survival? JD Upper Mainstem JD Lower Mainstem 15 15 85 10 10 85 88 86 5 5 87 87 88 86 0 0 0 5 10 15 0 2 4 6 8 10 12 14 Recruit Index JD South Fork JD Middle Fork 20 15 15 85 88 10 85 87 10 86 5 87 5 86 88 0 0 0 5 10 15 20 0 5 10 15 Spawner Index

  22. Influence of 1985 – 1988 brood years • Removed years and re-fit Ricker models • Similar results – still get strong evidence of density dependence (P < 0.01) for 8 data sets • Consistent estimates of growth rate (alpha)

  23. 3 1985 -1988 2 Other years 1 Log [recruits per spawner] 0 -1 -2 0 1 2 3 4 5 Standardized Spawner Index Combined data (spawner index standardized so median = 1 for each data set)

  24. 3 2 1 Density-independent Log [recruits per spawner] 0 -1 Ricker -2 0 1 2 3 4 5 Standardized Spawner Index Combined data (spawner index standardized so median = 1 for each data set)

  25. Potential problems with spawner-recruit analyses • Possible bias in Ricker parameters related to: • Short data sets • Measurement errors • Autocorrelation • Harvest rates • Estimates of parameters uncertain • Strong concern for NMFS (McElhany et al. 2000) • Can use simulations to assess potential bias

  26. Simulations • Simulated spawner-recruit data with same characteristics as Mid-Columbia data • True alpha = 3 • High autocorrelation • Low harvest rates • Assumed measurement error in age structure and escapement estimates (CV = 30%) • Estimated Ricker parameters for each simulated data set to assess potential bias

  27. Results (500 simulations) True value = 3.0 Median estimate = 3.2 60 50 40 30 Number of Simulations 20 10 0 1.0 2.0 3.0 4.0 5.0 6.0 Estimate of Ricker alpha

  28. Simulations results • Bias in Ricker parameters was minimal (10 to 20%) for range of conditions typical of Mid-Columbia steelhead data sets • Primary reason was low harvest rates (20% across most years) • Significant bias expected for harvest rates = 40% or greater across years

  29. Summary • Widespread evidence of density dependence in Mid-Columbia steelhead data sets • Consistent estimates of intrinsic growth rates (avg. = 3.4 recruits per spawner) • No evidence that one or more populations experienced relatively poor productivity • “Lambda” only useful as a red-flag indicator • Intrinsic growth rates suggest resilience to short-term increases in mortality

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