bivalves: the scourge of the estuary? - calfed science program
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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
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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
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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
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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
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Net
Out
flow
(m
3 /s)
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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.
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Net
Out
flow
(m
3 /s)
0.0
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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
physical processes dominate
physical processes critical
biological variables
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!)
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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
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100120140160180200
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Older year class shows wider spread which we expect because they are tolerant of lower salinities
# A
dult
s/0.
05m
2
physical processes dominate
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
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# R
ecru
its/
0.05
m2
Biomass Adults/0.05m2
physical processes critical
biological variables
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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)
physical processes critical
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# R
ecru
its
( ≤2.
5 m
m/0
.05m
2 )
Franks Tract Near Old River
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# 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
physical processes critical
physical processes critical
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
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Out
flow
(m
3 /s)
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lt C
orbu
la B
iomas
s (g
AFD
W/m
2 )
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500800+
Net
Out
flow
(m
3 /s)
# R
ecru
its
(/0.
05m
2 )
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