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

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

Space: John Day River

Study objectives

1)Broad-scale measures of female anadromy

2)Predict patterns of female anadromy

3)Assess potential importance of local variability

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

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

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

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

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

Objective 2: Predictive model

Siteswith

anadromy

Broad-scaleenvironmental

variable(s)

Predictivemodel

Test for residual spatial

variation

Tests of model

performance

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

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

Mantel test for spatial autocorrelation

AABB

EuclideanEuclideandistancedistance

Stream networkStream networkdistancedistanceΔΔDistanceDistance

ΔΔR

esid

ual

Res

idua

l Autocorrelated Autocorrelated residualsresiduals

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

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

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

• 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