partial migration in oncorhynchus mykiss: a spatially and sexually explicit approach justin mills,...
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![Page 1: Partial migration in Oncorhynchus mykiss: A spatially and sexually explicit approach Justin Mills, USGS/OSU (MS, 2008) Jason Dunham, USGS-FRESC Chris Jordan,](https://reader035.vdocuments.mx/reader035/viewer/2022062517/56649f1b5503460f94c3058f/html5/thumbnails/1.jpg)
Partial migration in Oncorhynchus mykiss: A spatially and sexually explicit approach
Justin Mills, USGS/OSU (MS, 2008)Jason Dunham, USGS-FRESCChris Jordan, NOAA-FisheriesGordie Reeves, USFS-PNWJohn McMillan, USGS/OSU (MS 2009)Chris Zimmerman, USGS
J. McMillan photos
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Sex and migration
Costs / benefits of migration Males Females
Decreased age-specific survival X X
Avoid poor freshwater conditions X X
Increased body size X X
Fitness strongly size dependent o X
J. McMillan photos
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Space: John Day River
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Study objectives
1)Broad-scale measures of female anadromy
2)Predict patterns of female anadromy
3)Assess potential importance of local variability
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Study designCollect juvenile
O. mykiss Determine maternal origin
Test for non-randomdistribution
Siteswith
anadromy
Broad-scaleenvironmental
variable(s)
Predictivemodel
Test for residual spatial
variation
Tests of model
performance
Collect watersamples
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Collection and maternal origin
P. Stratis photos
1
1.2
1.4
1.6
1.8
2
2.2
2.4
0 200 400 600Distance from centrum (microns)
Sr/
Ca
rat
io
Two fish+ water sample
Four otoliths
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Two rainbow trout offspringTwo rainbow trout offspring
Anadromy was common, widespread
Offspring of #
Steelhead 91Rainbow trout 58
Anadromy at 52 Anadromy at 52 of 72 sitesof 72 sites
One of eachOne of each
Two steelhead offspringTwo steelhead offspring
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How is maternal origin distributed?
RR
RR
RR
RR
SS
RRRR RR
SS
SS SS
SS
SS
SS
SS
SS
SS
SSSS
SSSS SS
SS
SS
RR
RR
RRSS
SS
SS
SS
RR
RR
RRRR
RR
RR
RR
SS
SS
SS
SS
SS
SS
RR = Rainbow trout offspring= Rainbow trout offspring SS = Steelhead offspring= Steelhead offspring
Random distributionRandom distribution Numerical dominance Numerical dominance or spatial segregationor spatial segregation
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Maternal origin was clustered
Combination at site Observed Expected
Different maternal origin 11 (23%) 22.4 (48%)
Same maternal origin 36 (77%) 24.6 (52%)Both steelhead 23 (49%) 17.3 (37%)
Both rainbow trout 13 (28%) 7.3 (15%)
nn = 47 sites; only those with 2 juveniles < 2 years old = 47 sites; only those with 2 juveniles < 2 years old
² = 11.15, ² = 11.15, dfdf = 1, = 1, PP < 0.001 < 0.001
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Objective 2: Predictive model
Siteswith
anadromy
Broad-scaleenvironmental
variable(s)
Predictivemodel
Test for residual spatial
variation
Tests of model
performance
![Page 11: Partial migration in Oncorhynchus mykiss: A spatially and sexually explicit approach Justin Mills, USGS/OSU (MS, 2008) Jason Dunham, USGS-FRESC Chris Jordan,](https://reader035.vdocuments.mx/reader035/viewer/2022062517/56649f1b5503460f94c3058f/html5/thumbnails/11.jpg)
Stream size and anadromy
• Associated with many ecological and physical processes– Sediment transport– Water temperature– Biological organization
• Readily used in spatial statistics
• Simple to estimate for large area
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Anadromy varied with stream size
Log(mean annual runoff; mLog(mean annual runoff; m33/s)/s)
-5-5 -4-4 -3-3 -2-2 -1-1 00 11 22
Ele
vatio
n (m
)E
leva
tion
(m)
00
500500
10001000
15001500
20002000
Rainbow trout offspringRainbow trout offspring Steelhead offspringSteelhead offspring
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Mantel test for spatial autocorrelation
AABB
EuclideanEuclideandistancedistance
Stream networkStream networkdistancedistanceΔΔDistanceDistance
ΔΔR
esid
ual
Res
idua
l Autocorrelated Autocorrelated residualsresiduals
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No spatial autocorrelation
ΔΔ Stream network distance (km)Stream network distance (km)00 100100 200200 300300 400400 500500 600600
ΔΔ R
esid
uals
Res
idua
ls
0.00.0
0.20.2
0.40.4
0.60.6
0.80.8
1.01.0• Mantel tests non-significant
• Spatial gradients accounted for by model
Subset of 1/5 of pairwise distancesSubset of 1/5 of pairwise distances
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Bottom lines• Sampling approach proved useful
• Female anadromy was predictable
• Stream size accounted for most of the broad-scale variability in female anadromy
• Local factors potentially source of remaining variability
J. McMillan photos
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Improvements
• Model improvements– Redd counts– Combined probabilistic predictions
• Local factors– Bioenergetics– Species interactions– Community effects– Ecosystem processes
• Doesn’t address males• Doesn’t address resident females
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• The process: critical periods, sexual tension, and everything in-between
• The evidence: observation, model, experiment – correlation vs causation?
• The relevance: ESA listing, modeling, monitoring, recovery du les sauvages?
Discussion