Bivalves: The Scourge of the Estuary? - CALFED Science Program

http://www.dfg.ca.gov/marine/status/green_sturgeon.pdf

Selenium

Bivalves: The Scourge of the Estuary?

Using Conceptual and Numerical Models to Inform Our Water Management Options

0

10

20

30

40

50

6075 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00

Chl

orop

hyll

a ( μ

g/L

)

Jan ThompsonUS Geological SurveyMenlo Park, California

My chargeAny good models on the shelf? NoHow can we use conceptual models like those developed in DRERIP? Strengths? Weaknesses? How do conceptual models differ from numerical models being developed in CASCADE? Strengths? Weaknesses? What’s the future for bivalve models?

Invaded 1986 and limited laboratory studiesDRERIP conceptual modelSTELLA population model – not dynamically linked

Invaded 1940 and more laboratory studiesDRERIP conceptual modelSTELLA population model – not dynamically linked

Status:

Bivalve models are connected to many submodels but must be connected to hydrodynamic models

at a minimum to

Distribute gametes and youngMaintain/change water qualityDeliver food to the sedentary animals

Conceptual models such as DRERIP models are critical for:

Designing numerical models –

particularly valuable in making connections between bivalves and other factors (physical transport & mixing, phytoplankton etc)

Highlighting data needs and prioritizing research needs and dollars

Examining processes which may not be modeled in the near term

*From Nicolini, MH and DL Penry. 2000. Spawning, fertilization, and larval development of Potamocorbula amurensis (Mollusca: Bivalvia) from San Francisco Bay, California. Pacific Science: 54(4):377-388 NO PERMISSION FOR PUB.

Spawning &Fertilization

Pelagic VeligerLarvae*

Recruit(135 µm long) Pelagic

TrochophoreLarvae*

DRERIP: We have

developed

Corbula and Corbicula Life Cycle Models, the first step in building conceptual models

Reproductive Adult

(5mm long)

17-19 days24 hours

24 h

ours

Corbula

Gametogenesis

*From Nicolini, MH and DL Penry. 2000. Spawning, fertilization, and larval development of Potamocorbula amurensis (Mollusca: Bivalvia) from San Francisco Bay, California. Pacific Science: 54(4):377-388 NO PERMISSION FOR PUB.

physical processes dominatephysical processes critical

Spawning &Fertilization

Pelagic VeligerLarvae*

Recruit(135 µm long)

Gametogenesis

PelagicTrochophore

Larvae*

LarvalRecruit Model

Growth Model

MortalityModel

Adult Transport

Model

Mortality Model

LarvalTransport

Model

ReproductionModel

Reproductive Adult

(5mm long)

17-19 days24 hours

24 h

ours

DRERIP sub-models have been connected to each Life Cycle Model and coded for habitat and critical processes

benthic habitat

pelagic habitat

PelagicTrochophore

Larvae

Food Quantity

Food Quality

Water Quality

Toxics

Water Quality

Transport

Stress/ no food

Water Quality

Gametogenesis

Broadcast Spawning

ExternalFertilization

Gametogenesis

InternalFertilization

Release Brood

Each DRERIP sub-model includes triggers or forcing factors and stressors

Food Quantity

Food Quality

Water Quality

Toxics

Water Quality

Water Quality

Adult Health

Adult Health

Adult Health

el.erdc.usace.army

DRERIP models help us build numerical models AND tell us what we know, how well we know it, and what critical data are lacking

Gametogenesis

InternalFertilization

Release Brood

Food Quantity

Food Quality

Water Quality

Toxics

Water Quality

Water Quality

Adult Health

Adult Health

el.erdc.usace.army

ImportanceHighMediumLow

UnderstandingHighMediumLow

PredictabilityHighMediumLow

A similar exercise for Corbula shows different data gaps and needs

PelagicTrochophore

Larvae

Food Quantity

Food Quality

Water Quality

Toxics

Water Quality

Transport

Stress/ no food

Water Quality

Gametogenesis

Broadcast Spawning

ExternalFertilization

Adult Health

ImportanceHighMediumLow

UnderstandingHighMediumLow

PredictabilityHighMediumLow

0

5

10

15

20

25

30

35

Salin

ity

Year

Cla

ss 1

Gam

etog

enes

is

Fert

iliza

tion

Pela

gic

Larv

a

Sett

led

Juve

nile

Repr

oduc

tive

ly M

atur

e A

dult

Year

Cla

ss 2

Gam

etog

enes

is

Days0

Spaw

n

2

Pela

gic

Swim

min

g La

rva

1 17-19 80+ Fall

Spri

ng

Extended Life Cycle Models further our understanding of controls on a species distribution

Food

Pelagic Infaunal

Som

e ti

mes

&

plac

es >1

/sea

son

0

5

10

15

20

25

30

35

Tem

pera

ture

Year

Cla

ss 1

Oog

enes

is

Self

-Fe

rtili

zati

on

Pedi

velig

er

Juve

nile

s Re

prod

ucti

vely

Mat

ure

Days0

Sper

mat

ogen

esis

75 17 90+Fall

Spri

ng

And allow us to contrast controls on different species

Alw

ays

Sper

mat

ogen

esis

Pedi

velig

er

Sper

mat

ogen

esis

12 19

Stop

whe

n ≈<

13>2

6°C

or in

suff

icie

nt f

ood

Food

Epibenthic/Pelagic Infaunal

Self

-Fe

rtili

zati

on

Sper

mat

ogen

esis

Year

Cla

ss 2

Oog

enes

is

0

100000

200000

300000

400000

500000

600000

88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06

Net

Out

flow

(m

3 /s)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Corb

ula

Biom

ass

(g A

FDW

/m2 )

DRERIP can help us discover critical processes DRERIP can help us discover critical processes before we are ready to model them. before we are ready to model them. EgEg. Why do . Why do the clams disappear in winter? Does it matter?the clams disappear in winter? Does it matter?

Grizzly Bay Corbula

Samples courtesy of DWR (D7)

Mortality

Benthic Predation

Invertebrates

Migratory Birds

Resident Birds

Fish

Dungeness Crab

Natural Death - Age

Water Quality Stress

Burial

Starvation

White Sturgeon

Sacramento Splittail

Opisthobranchs

Pelagic LarvaePredation

Ducks

CorbulaMortality

Model

physical processes dominate

physical processes critical

biological variables

Migratory ducks heavily prey upon Migratory ducks heavily prey upon shallow water shallow water CorbulaCorbula and they are and they are biobio--accumulating Se. accumulating Se.

0

100000

200000

300000

400000

500000

600000

88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06

Net

Out

flow

(m

3 /s)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Corb

ula

Biom

ass

(g A

FDW

/m2 )

However DRERIP models canHowever DRERIP models can’’t explain the intert explain the inter-- annual variability in biomass (and thus grazing) annual variability in biomass (and thus grazing)

peakspeaks

Samples courtesy of DWR (D7)

Grizzly Bay Corbula

DRERIP models also canDRERIP models also can’’t tell us why t tell us why CorbiculaCorbicula juveniles occur throughout the Delta but only juveniles occur throughout the Delta but only

growgrow--up in the central Delta.up in the central Delta.

Shell Growth

Growth (increase in

Carbon)

Consumption Rate

Tissue Growth

Food Quality

Carbon Available for Assimilation

Filtration Rate

Filtration Efficiency

Phytoplankton

Bacteria

Zooplankton

DOM

Food Quantity

Suspended Sediment

Phytoplankton

Bacteria

Zooplankton

DOM

Population Size of Filter Feeders

Current Speed

Water Quality Pumping Rate

Behavior

Species

Size

Species

Size

Attached/Free?

Size

Energy for Growth Energy for

Reproduction

Energy for Respiration

Energy forExcretion

PhysiologicalStressors

Water Quality

CorbulaGrowth Model




To do that we need to look at growth To do that we need to look at growth models and it becomes obvious that we models and it becomes obvious that we need more than a conceptual model.need more than a conceptual model.

In Summary, conceptual models such as DRERIP models do not

Tell us the magnitude of the bivalves ecological functions (eg. consuming phytoplankton)

Tell us the temporal and spatial variability of those ecological functions, the causes of which might be exploited by water management

Tell us if the model is wrong –

surprises are when we learn

Allow us to dynamically link and manipulate variables

Numerical models within CASCADEWe are using STELLA models until we know

more –

adapted to be spatially variable and responsive to stressors

Will tell us about processes and limits on populations –

I will discuss some today

Allow us to dynamically link and manipulate some variables

We expect and look forward to surprises

Standard StellaEnergetic GrowthModel

Statistical StellaGrowth Model

Adapted from Grant and Bacher

1998

We are simplifying STELLA models to operate with statistical relationships instead of using energetic principles.

Output: Temporally and spatially varying growth rate, biomass, grazing rate for phytoplankton and contaminant models

phytoplankton biomass

1.......20

1 . . . . . 5 1 . . . . .

4

temperature

habi

tat

Spatial resolution and physical variables known to dynamically interact with clam functions will be run sequentially to produce a “look up”

table for other

models.

Step 1: Initial Conditions: Where are the two species in space? Corbula recruits settle downstream of X2 (Corbicula juveniles settle upstream of X2!)

0100200300400500600700800900

1000

40 45 50 55 60 65 70 75 80 85 90 95 100

station is at X2 of 72; as long as X2 meets or exceeds that level we get recruits

X2 at 4.1 # R

ecru

its/

0.05

m2

020406080

100120140160180200

40 45 50 55 60 65 70 75 80 85 90 95 100

Older year class shows wider spread which we expect because they are tolerant of lower salinities

# A

dult

s/0.

05m

2


In order to establish some statistical bounds to our results, we will use an ensemble of salinity and temperature distributions in time and space to establish a range of distributions for each scenario

-122.3 -122.2 -122.1 -122 -121.9 -121.8 -121.7 -121.6 -121.5 -121.4 -121.30.4

0.45

0.5

0.55

0.6

0.65

Longitude

ΔW

ater

Tem

p / Δ

Air T

emp

Grantline TracySJ AntiochMiddle TracySJ PrisonerWickland PierSac MartinezStockton BurnsOld TracySJ Navy BridgeSac MallardSJ MossdaleSac Rio VistaSac Hood

Preliminary data from Wayne Wagner & Mark Stacey

Initial Conditions: Number of recruits is partially dependent on the biomass of adults present; there is a physical and biological basis for this relationship

0

200

400

600

800

1000

1200

1400

0 0.2 0.4 0.6 0.8

# R

ecru

its/

0.05

m2

Biomass Adults/0.05m2



0

100000

200000

300000

400000

500000

600000

87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06

0

100

200

300

400

500800+

Net

Out

flow

(m

3 /s)

# R

ecru

its

(/0.

05m

2 )

We assign Corbula recruit abundance based on biomass of adults and antecedent outflow events –

note the grouping of abundances

around 100, 200 and <20.

Recruit numbers are assigned as ≈100, ≈200, ≈10 (prolonged high outflow), and 400 (sufficient outflow to remove adults plus phytoplankton ‘bloomlet’

in region)


0

10

20

30

40

50

60

70

80

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

# R

ecru

its

( ≤2.

5 m

m/0

.05m

2 )

Franks Tract Near Old River

0

50

100

150

200

250

300

350

Jan-98

Apr-98

Jul-98

Oct-98

Jan-99

Apr-99

Jul-99

Oct-99

Jan-00

Apr-00

Jul-00

Oct-00

# R

ecru

its

( ≤2.

5 m

m/0

.05m

2 ) Sacramento River Near Rio Vista

We observe Corbicula recruit abundance at ≈20 recruits/0.05 m2

throughout the year at monitoring stations

We are determining if transport of larvae from non-local sites, where temperature regimes may be staggered, accounts for the near continuous pattern of recruitment observed in areas like the central delta where many sources of water are mixed.

If so changing temperatures throughout the tributaries and Delta could alter this population distribution

No data



Corbicula’s present and potential biomass distribution is likely based on food limitation so a growth relationship is critical to our model

Feb-Dec 1981

Foe & Knight 1985

Corbicula

growth is food limited at 20 mg/L chlorophyll a at 15°C and 47 mg/L at 20-24°C. Growth was limited in the San Joaquin River except for two months in 1981 so this relationship is probably as good as is available for this system

San Joaquin River at Antioch Ship Channel

05

1015202530

Jan Feb Mar Apr May

Jun Jul Aug Sep Oct Nov Dec

Chlo

roph

yll a

(m

g/L)

We believe that Corbula is also food limited in this system but we do not have laboratory data similar to that for Corbicula to verify our beliefs.

y = 0.0121e0.6072x

R2 = 0.9791

00.010.020.030.040.050.060.070.08

0 0.5 1 1.5 2 2.5 3

Shel

l Len

gth

Grow

th R

ate

(mm

/d)

Average Chlorophyll a (mg/L)

Recruits in Grizzly Bay

Our preliminary analyses of field data confirm food limitation in bivalves in the shallow water.

SummaryOur skill with both bivalve models is limited by

dataData gaps and priorities are well summarized by

conceptual models (DRERIP)Developing relationships for use in the numerical

models is ongoing and is helping us define important processes and thresholds

Given the best data and models, ecologists are still struggling with multi-generation, spatially explicit population models of benthic animals. We also expect to have problems so we are taking it slow, linking sub-models one step at a time

0

100000

200000

300000

400000

500000

600000

87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06

Net

Out

flow

(m

3 /s)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Adu

lt C

orbu

la B

iomas

s (g

AFD

W/m

2 )

0

100000

200000

300000

400000

500000

600000

87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06

0

100

200

300

400

500800+

Net

Out

flow

(m

3 /s)

# R

ecru

its

(/0.

05m

2 )

Bivalves: The Scourge of the Estuary? - CALFED Science Program

Documents

Transcript of Bivalves: The Scourge of the Estuary? - CALFED Science Program