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Steelhead and Snow Linkages to Climate Change ? Recruitment Curves Fact or Fiction? Salmonberry 4000 Upper John Day 5000 4000 3000 3000 2000 2000 1000 1000 0 0 0 2000 4000 6000 0 South Santiam 8000 3000 4000 2000 2000 1000 0 0 0 2000 4000 6000 8000 2000 3000 4000 5000 Umatilla 4000 6000 1000 0 1000 2000 3000 Clues from Residuals Salmonberry S Santiam Upper John Day Umatilla 4000 3000 2000 1000 0 -1000 -2000 -3000 1974 1980 1986 1992 1998 Possible Candidates PDO PNI Stream flow Others Mountain Snowfall A guess based on my experiences Good skiing years = good fishing years Data Sites for Snow Index Crater Lake Mount Rainier M a x i m u m S n o w D e p t h (c m) .......... Which Measurement? Seasonal Maximum Snow Depths 1000 Mt Rainier 800 600 400 200 0 1910 Crater Lake 1930 1950 1970 1990 2010 Snow Depth Index and Residuals Salmonberry S Santiam Upper John Day Umatilla 4000 3000 Snow Index 2000 1000 0 -1000 -2000 -3000 1974 1980 1986 1992 1998 Evaluation of Crater Lk & Mt Rainier Snow Index (CRSI) Spawner-Recruit time series for 26 populations of Oregon steelhead Evaluated 4 environmental indices as variables CRSI CRF nsPDO nPNI Attempted fit of B-H function w/ and w/o environmental variable Comparison Was model statistically significant ? Which model had lowest AICc score ? Four Environmental Indices The Last 80 Years CRSI nsPDO nPNI CRF 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 1910 1930 1950 1970 1990 2010 Fitting Recruitment Curves Overview 5000 Predictor Variable 1 Spawners 4000 Response Variable Recruits 3000 2000 5000 1000 4000 0 3000 1974 1980 1986 1992 1998 2000 1000 0.8 Predictor Variable 2 Environmental Index 0.6 0.4 0 1974 0.2 0.0 -0.2 -0.4 -0.6 -0.8 1974 1980 1986 1992 1998 1980 1986 1992 1998 Fitting Recruitment Curves Timing / Lags 5000 Predictor Variable 1 Spawners 4000 Response Variable Recruits 3000 2000 5000 1000 4000 0 3000 1974 1980 1986 1992 1998 2000 1000 0.8 Predictor Variable 2 Environmental Index 0.6 0.4 0 1974 0.2 0.0 -0.2 -0.4 -0.6 -0.8 1974 1980 1986 1992 1998 1980 1986 1992 1998 Population Count ..... Which Models Significant? 26 24 22 20 18 16 14 12 10 8 6 4 2 0 BH nsPDO nPNI CRF CRSI AICc “Best Model” Frequency nPNI nsPDO CRF 5 Populations CRSI 19 Populations The Not So Cool Part Relative Abundance ..... Decreased Snow = Fewer Steelhead 1.00 0.75 0.50 0.25 0.00 0% 5% 10% 15% 20% 25% Snow Index Decline 30% 35% Mountain Snow Levels are in Decline (from 1950 to present) Source: Mote et al. 2003 Air Temperature is the Story (Willamette Valley 7-yr Running Avg) CRSI AirTemp 1895 1915 1935 1955 1975 1995 Temperature Increase to Continue Source: IPCC (2007) Driven by Anthropogenic Factors Source: IPCC (2007) Climate Change is Here “The West’s snow resources are already declining as the climate warms ” - Mote et al. (2003) What Does this Mean for Steelhead ? Smaller Populations Higher Risk of Extinction How Much Higher ? Attempt to Quantify Extinction Risk Snow trends as proxy for climate change effect Forecast extinction risks with PVA Tested three CRSI scenarios Slight decline (8% per 100 yrs) Moderate decline (15% per 100 yrs) Large decline (34% per 100 yrs) PVA Model Recruits .... Add Spawners Spawners CRSI Recruits 0 0 20 40 60 80 Year Adjusted Recruits 100 10 20 30 40 50 60 Simulation Year 70 80 90 100 Slight Decline in CRSI Prob Extinct < 0.05 Prob Extinct < 0.05 to 0.25 Prob Extinct < 0.25 to 0.50 Prob Extinct < 0.50 to 0.80 Prob Extinct > 0.80 Moderate Decline in CRSI Prob Extinct < 0.05 Prob Extinct < 0.05 to 0.25 Prob Extinct < 0.25 to 0.50 Prob Extinct < 0.50 to 0.80 Prob Extinct > 0.80 Large Decline in CRSI Prob Extinct < 0.05 Prob Extinct < 0.05 to 0.25 Prob Extinct < 0.25 to 0.50 Prob Extinct < 0.50 to 0.80 Prob Extinct > 0.80 Grim Predictions At least 50% of populations vulnerable to extinction Implication for Fish Managers Crafting a Response Extreme Response #1 Extreme Response #2 A More Measured Response Accept that steelhead are in a evolutionary race against a rapidly changing environment Losing the race = extinction Management response should be: 1. Eliminate impediments to natural process of genetic adaptation 2. Support regional, national, and international actions to lessen and slow the impact of climate change Natural Evolutionary Processes Part 1 – Get all Pieces in Full Play Enable full expression of species diversity Functional populations across species range Function distribution across diverse habitats within a population’s range Resident life history strategy Repeat spawner life history strategy Older age smolts Maximize abundance of wild spawners to help retain genetic diversity Natural Evolutionary Processes Part 2 – Don’t put Adaptive Gains at Risk Limit use of hatchery fish Genetic (regardless of broodstock origin) Ecological Expect phenotypic changes that depart from the historical condition, for example More resident fish Smaller fish Different out-migration timing Different return timing Do not try to counteract these changes Natural Evolutionary Processes Part 3 – Change Definition of Success Steelhead management paradigm shift Old – Abundance, productivity, and fishery utilization goals New - Facilitation of rapid evolutionary change Evidence of population response will be much slower and more difficult to detect Determination if management strategy is a success will not occur in our lifetimes. Summary Mountain snowpack is linked to climatic factors that effect steelhead survival and recruitment Climate change will greatly increase the vulnerability of steelhead populations to extinction Facilitating the evolutionary process of population adaptation to climate change should be the primary focus of steelhead management in the future Questions ? 36 populations of steelhead, coho, and spring chinook 3.5 y = -2.9935x + 2.9167 R2 = 0.5639 3.0 .. 2.0 Ln(a) 2.5 1.5 1.0 0.5 0.0 -0.5 0.00 0.20 0.40 0.60 0.80 Hatchery Fish Proportion 1.00 Preview Demonstrate an association between variations in mountain snowpack and steelhead recruitment performance Quantify an increase in extinction risk due to climate change based on linkages with snowpack Suggest that facilitating the evolutionary process of population adaptation to climate change should be the primary focus of steelhead management in the future Summary of Evaluation Approach General Model Recruits = (Beverton-Holt Equation) * exp(c * Indx) Examined 29 variations of model per population Evaluation Was model statistically significant ? Which model had lowest AICc score ? Pretty Cool! .... CRSI Reflects this Decline Maximum Snow Depth 550 500 450 400 350 1875 1895 1915 1935 1955 1975 1995 Air Temperature the Last 1300 Years From 2007 IPCC Technical Summary Report Major Extinction Events