Transcript Importance of Habitat in salmon declines and recovery

Importance of Habitat in salmon
declines and recovery
Ray Hilborn
School of Aquatic and Fishery Sciences
UW
What is wrong with salmon?
The 4-H’s
• Harvest
– We take too many
• Habitat
– We degrade their streams
• Hydroelectric
– We block passage, turn rivers into lakes
• Hatcheries
– We try to “mitigate” for habitat loss by artificial
production
Structure of talk
• Trends in abundance
– How bad is the problem
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Ocean conditions – the BIG driver
Hydroelectric
Harvest
Hatcheries
Habitat
Myth I
We are running out of wild
salmon
The “truth”: there are nearly as
many wild salmon in western North
America now as any time since
Europeans arrived
But: this due primarily to Alaska, and in the
Lower 48 many stocks are extinct and most
are well below historical levels
1995
1989
1983
1977
1971
1965
1959
1953
1947
1941
1935
1929
1923
1917
1911
1905
1899
1893
C atch in m il lio n s o f fish
Bristol Bay wild sockeye
50
40
30
20
10
0
O re g o n c o h o c a t c h ( m illio n s )
5
4
3
2
1
0
1960
1965
1970
1975
1980
1985
O c e a n c o h o c a tc h in 1 0 0 0 's
6000
5000
4000
3000
2000
1000
0
1970
1975
1980
1985
Y e ar
1990
1995
Puget Sound Coho Wild Returns
R un S ize
800
600
400
200
1987
1989
1991
1993
1988 1990 1992 1994 1996 1998
1970
1980
1990
2000
A
Chinook salmon past Bonneville
Dam
B
C
400
Run
Size 100
300
200
60
100
20
D
E
1988
1991
1994
F
1988 1990 1992 1994 1996 1998 2000
Year
1960
1970
1980
1990
2000
Myth II
The ocean is big, unlimited and
salmon abundance is driven by
freshwater and habitat
The “truth”: most large scale
variation in salmon abundance is
driven by ocean changes
But: this only means it is harder to detect
anthropogenic impacts
Historical sockeye population dynamics
Lake Nerka, SW Alaska
Sediment chemistry
7
(‰)
Mixing model
6
30
4
1750
1800
1850
1900
1950
2000
Sockeye population
20
10
reference
lakes
0
2
(1000s/km2)
Salmon density
12
(1000s/km2)
5
Salmon density
Sediment 15N
8
4
6
8
10
Sediment 15N (‰)
8
sockeye
4
0
1750
1800
1850
Year
1900
1950
2000
Schindler and Leavitt (2001)
Historical sockeye population dynamics
Lake Nerka, SW Alaska
Salmon density
(1000s/km2)
12
+ fishery catch
8
4
escapement
0
1750
1800
1850
1900
1950
2000
Year
Schindler and Leavitt (2001)
Sockeye
(1000s/km2)
Effects of sockeye population on phytoplankton production
12
8
4
Sockeye density
0
1750
1800
1850
1900
1950
2000
Lutein-zeaxanthin
(nmol/g)
Diatoxanthin
(nmol/g)
12
8
4
0
Algal pigments
in lake
sediments
6
4
2
0
1750
1800
1850
1900
Year
1950
2000
Schindler and Leavitt (2001)
Survival rate by Realm
Fall
chinook
Coho
Spring
chinook
Arctic
SE Alaska
Coastal BC
Georgia Strait
Puget Sound
Coastal Washington
Columbia Basin
Coastal Oregon
California
0%
2%
4%
6%
8%
0%
1%
2%
Avg survival rate
0%
1%
2%
Coho survival rate by Domain
15%
Alaska and Yukon
BC and Puget Sound
12%
Coastal WaOrCa
Columbia basin
Survival
rate
9%
6%
3%
0%
72
74
76
78
80
82
84
86
88
Release year
90
92
94
96
98
Fall chinook survival rate by Domain
5%
BC and Puget Sound
4%
Coastal WaOrCa
Columbia basin
Survival
rate
3%
2%
1%
0%
72
74
76
78
80
82
84
86
Release year
88
90
92
94
96
Spring chinook survival rate by Domain
7%
Alaska and Yukon
6%
BC and Puget Sound
Coastal WaOrCa
5%
Columbia basin
Survival
rate
4%
3%
2%
1%
0%
72
74
76
78
80
82
84
86
Release year
88
90
92
94
96
Coho survival~SST regression
Coho survival~SST regression
(incl. resid)
Gulf of Alaska – Small set
of structuring variables
operating at different
speeds - Whammo!
Myth III
The decline of NW salmon is due
to dams
The “truth”: systems without dams
have had similar trends
But: clearly dams are not good for salmon
and are part of the problem
Chinook survival by river segment
Survival rate
Fall chinook
Spring chinook
Fall chinook
100%
100%
10%
10%
1%
1%
0.1%
B: Columbia below dams
W: Willamette River
A: Columbia above dams
S: Snake River
0.1%
L: Lower Fraser
T: Thompson River
U: Upper Fraser
0.01%
0.01%
0.001%
0.001%
B W A S
B W A S
Columbia
Columbia
L
T
U
Fraser
Chinook survival in Columbia Basin
Spring chinook
Fall chinook
4.0%
Survival rate
3.0%
3.0%
2.0%
2.0%
1.0%
1.0%
0.0%
0.0%
0
200
400
600
800
0
1000
200
600
800
1000
U p stre am (mile s)
U p stre am (mile s)
Survival rate
400
1.0%
1.0%
0.5%
0.5%
0.0%
0.0%
0
1
2
3
4
5
D ams
6
7
8
9
0
1
2
3
4
5
D ams
6
7
8
9
Chinook survival in Fraser Basin
Fall chinook
Survival rate
2.0%
1.0%
0.0%
0
100
200
300
400
U p stre am (mile s)
500
600
Myth IV
Hatcheries are necessary to
mitigate for lost of habitat and
over-harvest
The “truth”: hatcheries have
strong negative impacts on wild
salmon
But: if we eliminate hatcheries we might
have no salmon left in some places
Hatcheries
• The basic assumptions
– Freshwater habitat is limiting
– Egg to smolt survival in the wild is about 5%
– Hatcheries can usually obtain 80% egg to smolt
survival
– Release smolts ready to go to sea – they don’t
need any freshwater habitat
Why hatcheries were built
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To compensate for over-harvesting
To compensate for habitat destruction
To mitigate for dam impacts
To buffer natural variation
To provide extra fish for harvest
To conserve threatened stocks
Did Hatcheries Work
• We have over 300 hatcheries in the Pacific
Northwest
• “If hatcheries were the solution, we
wouldn’t have a problem!”
• Much disagreement, what would have
happened without hatcheries
O P I C o h o S a lm o n
70
5
60
4
50
40
3
30
2
20
S m o lts
1
10
0
0
1960
1970
1980
1990
A d u lts p ro d u c e d
S m o lts re le a s e d
A d u lts
Concerns about hatcheries
• Generate over-harvesting on wild fish in
mixed stock fisheries
• Compete with wild fish in freshwater and
ocean
• Introduce and exacerbate diseases
• Genetically degrade wild fish by
domestication and hybridization
• Provide an excuse to allow habitat loss
Pink salmon hatcheries in Prince
William Sound
• Largest hatchery program in North America
• 600 million fish stocked each year
• Competing hypotheses re marine fish
stocking
– stocking augments wild production
– stocking replaces wild production
• We have BACI !!!!!!
Prince William Sound salmon
production
to tal ru n
Ar e a A
ye a r
Total return
Ar e a A
Ar e a B
to tal ru n
to tal ru n
C orrec t
G ues s
ye a r
ye a r
Ar e a D
to tal ru n
to tal ru n
Ar e a C
ye a r
ye a r
Wild fish production
A re a B
W ild R etu r n
W ild R etu r n
A re a A
Ye a r
Ye a r
A re a D
W ild R etu r n
W ild R etu r n
A re a C
Ye a r
Ye a r
re tu rn
P in k s a lm o n
P rin c e W illia m S o u n d
Ha tc h e r y
W ild
1962
1965
1968
1971
1974
1977
1980
Ye a r
1983
1986
1989
1992
1995
Myth V
The collapse of salmon in the late
80s and 90s is due to habitat
changes
The “truth”: habitat has not
changed that much
But: habitat is definitely declining
Few (if any) attempts to integrate
all factors in combined analysis
• We have detailed harvest models
• We have no hatchery impact models in use
• Changes in ocean conditions are being
better understood but not used in evaluating
recovery plans
• A number of habitat models, EDT the most
used
Framework for impact of habitat
• Multi-stage life history model from
Moussalli and Hilborn 1986
– each life history stage as a Beverton-Holt curve
with a productivity (initial slope or survival)
and a capacity
• Key question is how to relate habitat to
productivity and capacity
Sharma coho carrying capacity
S m o lt d e n s ity
4800
3600
2400
1200
0
0
2000
4000
6000
8000
10000
2
P o o l d e n s ity (m /k m )
12000
14000
Key Model Components
SHIRAZ
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Spatially explicit – reaches or estuarine areas
Life stages as many as you want
Stocks may be life histories, wild/hatchery etc
Capacity and productivity – any life history
Habitat characteristics by reach
Stochastic factors (flows, ocean survival etc)
Functional relationships between habitat
characteristics and stochastic factors and
productivity and capacity
Reach Characteristics
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Passage
Square meters spawning gravel
Distance
Square meters rearing habitat
Percent fines in gravel
Watershed area by reach
Percent impervious by reach
Temperature, DO etc.
Functional relationships
• Spawning gravel and egg capacity
• % fines in gravel and egg to fry survival
• Up the the user to define what you want to
use
• Will ultimately build a “library” of
functional relationships much like EDT …
– But the user will decide which ones to use from
the library
General model framework
• Read in the data
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reach-specific habitat
hatchery input
Functional relationships
Hatchery practice
Harvest and ocean conditions specification
habitat interventions
• Loop over time
– Calculate the change in habitat
– Calculate the change in population size
• End the loop
Habitat Changes
• Annual habitat change: habitat
degradation
• Habitat change due to a 1-time event:
habitat restoration
Hatchery Influence
• Affect wild fish through competition
• Interbreeding can cause domestication
of wild fish, and reduced survival
Functional Relationships
Mark I version
• Spawner capacity depends on gravel area
• Egg survival as a function of fines
• Fry survival as a function of percent
impervious and rearing area
Spawners to Egg
• capacity depends on gravel area
• productivity depends on age specific fecundity and age
distribution of spawners
8,000,000
7,000,000
6,000,000
Egg
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
0
2000
4000
Spaw ners
6000
8000
Eggs to Fry
• capacity is unlimited
• productivity depends upon % fines
0.250
Egg survival
0.200
0.150
0.100
0.050
0
10
20
% fines
30
40
Fry to Smolt
• capacity determined by rearing area
• productivity determined by % impervious
fry to sm olt productivity
0.250
0.200
0.150
0.100
0.050
0
5
10
15
20
% im pervious
25
30
35
Other outstanding issues
Beyond current efforts
• Allow for parameter uncertainty
• Formalize reality checks
• Potentially imbed the above in formal
Bayesian framework
Current status
• Muckelshoot tribe using to meet TRT
requirements for a rebuilding plan – Green
River chinook well developed, White and
Lake Washington just beginning
• Joint work with NMFS and Mark Sheuerell
to interface SHIRAZ with PRISM dynamic
hydrology models
Essential Fish Habitat:
SHIRAZ provides a format
• To calculate the sensitivity of population
size to each habitat indicator in each area
• This allows a quantitative ranking of the
importance of different habitat
characteristics and sites
• This ranking can be used to define
“essential”, much like NMFS defines
“overfishing”
Summary I
• Current work in evaluating natural and
anthropogenic impacts on salmon suffer
from lack of unified modelling framework
• SHIRAZ can serve as an initial general
model structure for cost benefit analysis,
policy evaluation, and parameter estimation
Summary II
• The Ocean, and the four H’s are all
important
• We need to identify where time, effort and
money will be best spent in salmon
restoration
• This will require a new generation of
models, data collection and analysis