Transcript Slide 1

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
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Mountain Snowfall
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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)
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Spawner-Recruit time series for 26 populations of Oregon
steelhead
Evaluated 4 environmental indices as variables
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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
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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)
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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
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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
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Enable full expression of species diversity
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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
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Limit use of hatchery fish
Genetic (regardless of broodstock origin)
 Ecological
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Expect phenotypic changes that depart from the
historical condition, for example
More resident fish
 Smaller fish
 Different out-migration timing
 Different return timing
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Do not try to counteract these changes
Natural Evolutionary Processes
Part 3 – Change Definition of Success
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Steelhead management paradigm shift
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Old – Abundance, productivity, and fishery utilization goals
New - Facilitation of rapid evolutionary change
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Evidence of population response will be much
slower and more difficult to detect
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Determination if management strategy is a success
will not occur in our lifetimes.
Summary
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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
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Demonstrate an association between variations in
mountain snowpack and steelhead recruitment
performance
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Quantify an increase in extinction risk due to climate
change based on linkages with snowpack
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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
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General Model
Recruits = (Beverton-Holt Equation) * exp(c * Indx)
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Examined 29 variations of model per population
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Evaluation
Was model statistically significant ?
 Which model had lowest AICc score ?
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Pretty Cool!
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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