Effects of sea lion predation on Willamette River winter steelhead viability

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1 Effects of sea lion predation on Willamette River winter steelhead viability Matt Falcy Bryan Wright Matt Falcy

2 Willamette Falls Willamette Falls

3

4 Goal: Quantify threat of extirpation posed by sea lions Method: Population Viability Analysis (PVA) Data Model Model Result Adult abundance Age at maturity Proportion hatchery Harvest Sea lion mortality math Population dynamics programming Population simulator cpu time Probability of extirpation Scenarios with sea lions without sea lions

5 Steelhead Spawner Abundance Base enumeration counted at Willamette Falls (WF) 62% at WF spawn in focal populations (U of I) Winter Steelhead N. Santiam S. Santiam Calapooia Molalla Apportion to populations amount of spawning habitat redd-density surveys multiple imputation for missing observations Abundance Year

6 Sea Lion Predation Observe (surface) feeding events Stratified three-stage cluster sampling design days of week site-shift (block of hours at given site) 30-min observation bouts (3 of 4) Predation events assigned to species observed species composition at window (1, 7, 14 d) Monte Carlo

7 Sea Lion Predation Willamette Falls 150 Expand estimated predation for steelhead run passing before predation monitoring. StW Daily Count Nov 2013 Dec 2013 Jan 2014 Feb 2014 Mar 2014 Apr 2014 May 2014 Jun Willamette Falls StW Daily Count Nov 2014 Dec 2014 Jan 2015 Feb 2015 Mar 2015 Apr 2015 May 2015 Jun 2015 Willamette Falls StW Daily Count Nov 2015 Dec 2015 Jan 2016 Feb 2016 Mar 2016 Apr 2016 May 2016 Jun 2016 CSL Max Count

8 Age data Dr. Scheuerell s integrated population model Year Age 1 Age 2 Age 3 Age 4 Age 5 Age NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1996 NaN NaN NaN NaN NaN NaN 1997 NaN NaN NaN NaN NaN NaN 1998 NaN NaN NaN NaN NaN NaN 1999 NaN NaN NaN NaN NaN NaN 2000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2007 NaN NaN NaN NaN NaN NaN 2008 NaN NaN NaN NaN NaN NaN 2009 NaN NaN NaN NaN NaN NaN 2010 NaN NaN NaN NaN NaN NaN 2011 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2016 NaN NaN NaN NaN NaN NaN

9 Goal: Quantify threat of extirpation posed by sea lions Method: Population Viability Analysis (PVA) Data Model Model Result Adult abundance Age at maturity Proportion hatchery Harvest Sea lion mortality math Population dynamics programming Population simulator cpu time Probability of extirpation Scenarios with sea lions without sea lions

10 Population Dynamics Density-dependence Sea lion predation identical to fishing Multi-model inference Three Ricker models RR tt,pp = αα pp SS tt,pp ee ββ ppss tt,pp εε tt,pp RR tt,pp = αα pp SS tt,pp ee ββ ppss tt,pp εε tt RR tt,pp = ααss tt,pp ee ββ ppss tt,pp εε tt,pp Each pop separate, k=12, WAIC=224.8 Shared error variance, k=9, WAIC=248.9 Shared productivity, k=9, WAIC=217.6 Bayesian analysis Yields probability-based inference for parameters. MCMC provides random draws of parameters that include covariance. Bayesian analysis for PVA Feel the power of the dark side

11 Population Dynamics Density-dependence Sea lion predation identical to fishing Multi-model inference 1:1 (replacement) math model model uncertainty Bayesian analysis Yields probability-based inference for parameters. MCMC provides random draws of parameters that include covariance.

12 Goal: Quantify threat of extirpation posed by sea lions Method: Population Viability Analysis (PVA) Data Model Model Result Adult abundance Age at maturity Proportion hatchery Harvest Sea lion mortality math Population dynamics programming Population simulator cpu time Probability of extirpation Scenarios with sea lions without sea lions

13 Population Simulator Reality Model Data nn ii=1 yy ii yy 2 inferential statistics simulation for i=1:length(x) y(i,:)=normrnd(x(i,1),x(i,2),[1,50]); end Parameters

14 Population Simulator 4000 N.Santiam 8000 S.Santiam Replication random draws of parameters per population each draw used to simulate 100 years process above replicated 100 times = 100,000 simulations of 100 years per population Allee effects (negative density dependence) If N t < 100, then no reproduction If N t:t+3 < 100 (4 consecutive years), then functionally extirpated Pr[extirpation] = #extirpations/100,000 Spawner Abundance Spawner Abundance Year Calapooia Simulated Empirical Year Simulated Empirical Spawner Abundance Spawner Abundance Simulated Empirical Year Molalla 6000 Simulated Empirical Year

15 Goal: Quantify threat of extirpation posed by sea lions Method: Population Viability Analysis (PVA) Data Model Model Result Adult abundance Age at maturity Proportion hatchery Harvest Sea lion mortality math Population dynamics programming Population simulator cpu time Probability of extirpation Scenarios with sea lions without sea lions

16 Probability of Extirpation Scenario North Santiam Population South Santiam Molalla Calapooia Without Sea Lions: 2% 5% 0% 99% With Sea Lions: lowest observed predation (2015) average predation (2016) highest observed predation (2017) 8% 16% 0% 99% 27% 34% 2% 99% 64% 60% 21% 99%

17 Management Sea lions functionally extirpated steelhead at Ballard Locks, Seattle Non-lethal deterrence is ineffective (20+ years) Take under Marine Mammal Protection Act is a long process Source: NOAA Fisheries

18 Effects of sea lion predation on Willamette River winter steelhead viability Reproducible results: (data and code) Matt Falcy Bryan Wright

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