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Covariation in Productivity of Mid-Columbia Steelhead Populations S.P. Cramer & Associates, Inc. 600 N.W. Fariss Road Gresham, OR 97030 www.spcramer.com.

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Presentation on theme: "Covariation in Productivity of Mid-Columbia Steelhead Populations S.P. Cramer & Associates, Inc. 600 N.W. Fariss Road Gresham, OR 97030 www.spcramer.com."— Presentation transcript:

1 Covariation in Productivity of Mid-Columbia Steelhead Populations S.P. Cramer & Associates, Inc. 600 N.W. Fariss Road Gresham, OR Brian Pyper & Steve Cramer

2 Mid – Columbia Study Area

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

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

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8 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

9 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

10 Population abundance of natural-origin steelhead in the Middle Columbia ESU,

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13 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 …

14 DI RK 1:1 BH HS Spawner Index Recruit Index 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).

15 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

16 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

17 Fits of the Ricker and Density-independent models JD North Fork Deschutes Umatillla Yakima Spawner Index Recruit Index

18 JD Upper Mainstem JD Lower Mainstem JD South Fork JD Middle Fork Spawner Index Recruit Index Fits of the Ricker and Density-independent models

19 Ricker estimates of intrinsic growth rate (alpha) Average = 3.4 recruits per spawner Upper Mainstem Lower Mainstem South Fork Middle Fork North Fork DeschutesUmatilllaYakima Ricker Alpha (Recruits/Spawner) `

20 Ricker estimates of intrinsic growth rate (alpha) Average = 3.4 recruits per spawner Upper Mainstem Lower Mainstem South Fork Middle Fork North Fork DeschutesUmatilllaYakima Ricker Alpha (Recruits/Spawner) ` Ricker Average for Density- Independent models = 1.4 Recruits/Spawner

21 Ricker estimates of capacity: unfished equilibrium spawner abundance (S*) 0 2,000 4,000 6,000 8,000 10,000 DeschutesUmatilllaYakima Ricker S* ` Spawner Abundance

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

23 Ricker estimates of capacity: John Day Upper Mainstem Lower Mainstem South ForkMiddle ForkNorth Fork Recent 5-yr geometric mean Ricker S* Redds per Mile

24 JD Upper Mainstem JD Lower Mainstem JD South Fork JD Middle Fork Spawner Index Recruit Index Influence of 1985 – 1988 brood years: Density dependence or poor ocean survival?

25 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) Influence of 1985 – 1988 brood years

26 Combined data (spawner index standardized so median = 1 for each data set)

27 Combined data (spawner index standardized so median = 1 for each data set)

28 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 Potential problems with spawner-recruit analyses

29 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 Simulations

30 Results (500 simulations) Estimate of Ricker alpha Number of Simulations True value = 3.0 Median estimate = 3.2

31 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 Simulations results

32 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 Summary


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