Yakima O. mykiss Modeling Workshop

Ian Courter Casey Justice Steve Cramer

Introductions

• What interests you most about the topic of anadromy and residency in O. mykiss?

• What would it take to make this a successful meeting for you?

Project Objective

Quantify the influence of life-history diversity and environment on steelhead sustainability in the Yakima Basin.

Deliverables

• Excel-based O.mykiss life-cycle model

• Peer reviewed publication

• Updated BA Steelhead Effects Analysis

Roles and Responsibilities

“Clarifying roles up front is like writing a job description – without it, you have no idea who can do what to whom.”

Steve CramerProject Advisor

Yakima Joint Board / Bureau of ReclamationProject Sponsor

Ian CourterProject Lead

Casey JusticeLead Analyst

Advisors

Choosing the Right Approach

Advisor Comments and Contributions

• Comments will be addressed on an individual basis.

• Participants who to make substantive contributions will be given coauthorship on publications.

Proposed Modeling Approach to Evaluating Drivers of Anadromous and Resident O. mykiss Abundance in the

Yakima Basin

Project Background and Purpose

ICTRT Extinction Risk Analysis

Atlas of Pacific Salmon (2005)

“…abandon the typological thinking (‘steelhead’ and ‘rainbow trout’ as biologically independent units) that has pervaded the biology and management of this species...”

McPhee et al. 2007

Ecotype Abundance Drivers

• Carrying Capacity– Size-dependent, flow-dependent

• Growth– Temperature dependent

• Survival– Smolt to adult

• Fecundity– Size and life-history dependent

Habitat Characteristics Favoring Residency or Anadromy

• High summer rearing temperatures

• Low summer flows• Variable growth

conditions• Reduced capacity for

adult fish• High migration survival

• Low summer rearing temperatures

• High summer flows• Consistent growth

conditions• Year-round capacity for

adult fish• Low migration survival

Anadromy Residency

Resident Recruits

ResidentSpawners

Genetics

Environment

Mature Adults

NRAnadromous

Spawners

AnadromousRecruits

NA

Mature Adults

Genetics

Environment

Juvenile Life-history Response

genotype + environment =

Anadromy Non-anadromy

phenotype

Life-History Response

“The capability to balance life-history options fits understandings of anadromy as ‘…a suite of life history traits… expressed as points along continua for each species and population.’ (Quinn & Myers 2005) as ‘…a function of variation in costs and benefits…’ (Hendry et al. 2004)…”

McPhee et al. 2007

Key Concepts

• Resident trout produce anadromous offspring

• Anadromous O. mykiss produce resident offspring

• Resident trout and anadromous steelhead in the upper Yakima, though phenotypically different, are genetically indistinguishable

• Phenotypic state is determined by a combination of environment and genotype

• Phenotypic state determines juvenile life-history response (anadromy or non-anadromy)

• “State-dependent” or “conditional” strategies allow individuals within a population to maximize their fitness

Key Concepts

To appropriately model Yakima steelhead abundance drivers, exchange between life-history forms in the population needs to be accounted for.

Quantitative O. mykiss Population Assessment

Abundance

(1) Stochastic Population ModelProductivity

DiversitySpatial Structure

(2) Mechanistic Model

Genetic & Env ControlsSurvival

FecundityJuvenile Capacity

Modeling Phase

-Viability analysis tool

-Restoration planning tool

Resident Contribution

Conceptual Modeling Approach

Carrying Capacity

Growth(bioenergetics)

Juvenile Life-History Response

Survival and Fecundity

Genetics

Key Model Components

Flow Temperature

Territory Size(competition)

Growth(bioenergetics)

Food supply

Capacity

Abundance Body Size

Conditions: Habitat

Fish Metrics:

(survival)

WUA(PHabSim)

* Influenced by body size

Reproductive Success

Fecundity*

Marine Survival*

ResidentAnadromous

Life-history decision*

Juvenile Abundance

Freshwater Survival*

Reproductive Success

Fecundity

Population

Metolius Upper Yakima Naches Satus Toppenish

Flo

w v

aria

bili

ty i

nd

ex

(CV

flo

w M

ar-S

ep (

%))

0

20

40

60

80

100

120

140

Mar Apr May Jun Jul Aug Sep Oct

Flo

w (

cfs)

at

Up

per

Yak

ima

0

1000

2000

3000

4000

Flo

w (

cfs)

at

To

pp

enis

h C

r.

0

100

200

300

400

500

600

Upper Yakima Toppenish

Toppenish Creek above Olney

Flow (cfs)

0 20 40 60 80 100 120 140 160

Juve

nile

ste

elh

ead

WU

A (

ft²/

1000

ft)

500

1000

1500

2000

2500

3000

Recalibrated data from Hardin and Davis (1990)

How do we model effects of flow on capacity?

Flow (cfs)

0 500 1000 1500 2000

WU

A (

ft²/

1000

ft)

0

1000

2000

3000

4000

5000

6000

FryJuvenileAdult

From Grant and Kramer (1990)

Fish length (mm)

0 50 100 150 200 250 300 350 400

Ter

rito

ry s

ize

(m²)

0

2

4

6

8

10

12

14

16Fry Juvenile Adult

Rearing capacity = WUA (m2) / Territory size (m2)

Flow (cfs)

0 500 1000 1500 2000

Fry

cap

acit

y

0

10000

20000

30000

40000

50000

60000

Juve

nil

e an

d a

du

lt c

apac

ity

0

1000

2000

3000

4000

FryJuvenileAdult

Flow (cfs)

0 200 400 600 800 1000

Ad

ult

cap

acit

y /

juve

nile

cap

acit

y

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

Stream temperature (°C)

0 5 10 15 20 25

Gro

wth

(g

/day

)

-0.01

0.00

0.01

0.02

0.03

0.04

0.05

From Rand et al. (1993) and Mangel and Sattherthwaite (2008).

Modeling Growth in FreshwaterGrowth = anabolic gains – catabolic losses

Factors influencing growth:

1) Temperature

2) Food availability

3) Fish density (competition)

4) Fish size

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Dai

ly a

vg.

tem

per

atu

re (

°C)

0

5

10

15

20

Upper YakimaNaches

Data from Hydromet 2000-2007:

Satus and Toppenish temperature data?

Jun Dec Jun Dec Jun Dec Jun

Fo

rk l

eng

th (

mm

)

0

100

200

300

400

Age-0 Age-1 Age-2

Predicted growth in the Upper Yakima

May Jun Jul Aug Sep Oct

Fo

rk le

ng

th (

mm

)

120

140

160

180

200

220

240

Expected(Optimal)

Observed(Constrained

Jun-Sep)

DecisionPoint

Δ Length (Observed – Expected)

= -28.6 mm

Age-1 Juvenile Growth

How does fish growth influence life-history variability?

May Jun Jul Aug Sep Oct

Fo

rk le

ng

th (

mm

)

120

140

160

180

200

220

240

Expected(Optimal)

Observed(Constrained September)

DecisionPoint

Δ Length (Observed – Expected)

= -7.8 mm

Age-1 Juvenile Growth

Survival Tradeoffs

Marine(smolt to adult survival)

Fre

shw

ater

(juve

nile

to

adul

t su

rviv

al)

Resident

Anadromous

Both?

Both?Neither?

Reproductive Success(population status)

Length at emergence (mm)

100 125 150 175 200 225 250 275 300

Mar

ine

surv

ival

sca

lar

(% o

f m

ax)

0

20

40

60

80

100

120

Data from Ward and Slaney (1989)

Fecundity vs Body Size

Fecundity of Steelhead and Rainbow Trout Stocks

Length (inches)

0 5 10 15 20 25 30

Eg

gs

0

1000

2000

3000

4000

5000

6000

7000

8000

Rainbow Trout

Steelhead

Genetics Modeling

• Thrower et al. 2004

– Heritabilities: probability of smolting and maturing

• Falconer 1989

– Response to selection

Communication Platforms

• Project website: http://www.fishsciences.net/projects/yakima

• Webinar meetings and conference calls

• Personal email and phone correspondence

End of Show

Sashin Creek Rearing Studies 1996 Brood

Weight (g)

Age-2 life-history Jun-97 Oct-97 Jun-98

Resident 4 30 67

Mature 5 43 71

Smolt 5 41 89

Frank Thrower, pers. comm.