PALEOCEANOGRAPHY, VOL.
VOL. 13,
NO. 1,
FEBRUARY 1998
PALEOCEANOGRAPHY,
13, NO.
1, PAGES
PAGES 96-105,
96-105, FEBRUARY
1998
Testing
the effects
of tropical
tropical temperature,
temperature, productivity,
Testing the
effects of
productivity, and
and
mixed-layer
depth
on
foraminiferal
transfer
functions
mixed-layer depth on foraminiferal transfer functions
James
James M.
M. Watkins
Watkins and Alan
Alan C.
C. Mix
Mix
College
Sciences,
Oregon
University, Corvallis
Corvallis
Collegeof
of Oceanic
Oceanicand
andAtmospheric
Atmospheric
Sciences,
OregonState
StateUniversity,
Abstract.
Statistical transfer
functionsrelate
relateliving
living planktonic
planktonic foraminiferal
foraminiferalspecies
speciesofofthe
thecentral
central equatorial
equatorial Pacific
Pacific to
to
Abstract. Statistical
transferfunctions
measured
sea surfce
temperature,
integrated
The faunal
measuredsea
surface
temperature,
integratedprimary
primary productivity,
productivity,and
andmixed-layer
mixed-layerdepth.
depth. The
faunalestimates
estimates
successfully
reconstructlatitudinal
latitudinal patterns
patterns observed
observed in
in both
both warm
1992)
successfully
reconstruct
warm(El
(El Niflo,
Nifio, February-March
February-March
1992) and
andcool
cool(La
(LaNifia,
Nifia,
August-September
1992)
settings.
Predictions of
of mixed-layer
mixed-layerdepth
depthappear
appeartoto be
be unbiased
unbiased by
or
August-September
1992) seasonal
seasonal
settings. Predictions
by temperature
temperature
or
productivity
in our
our data
data set
set but
deep
productivityin
but tend
tendto
tounderestimate
underestimate
deepmixed
mixedlayers.
layers. Interactions
Interactionsbetween
betweenproductivity
productivityand
and
temperature,
perhaps
through their
on respiration
respiration and
transfer
functions
temperature,
perhapsthrough
theircommon
commoninfluence
influenceon
andgrowth
growthrates,
rates,bias
biasforaminiferal
fomminiferal
transfer
functions
for
both
properties.
Paleoceanographic
estimates
may
be
improved
by
accounting
for
such
biological
processes
that
for both properties. Paleoceanographic
estimatesmay be improvedby accotinting
for suchbiologicalprocesses
that
translate
the
into
preserved
in
translate
theenvironment
environment
intoaafaunal
fatrealresponse
response
preserved
inthe
thegeologic
geologicrecord.
record.
1.
Introduction
1. Introduction
and
tops, which
spatial
and undated
undatedcore
coretops,
which average
averageover
over different
differentspatial
scales
and timescales.
The comparison
comparison of
of two
two seasons
seasons is
is
scales and
timescales. The
important
First, the
importantfor
for two
tworeasons.
reasons.First,
the contrast
contrastof
ofupper
upperocean
ocean
properties between
properties
between our
our sampling
samplingin
in February-March
February-Marchand
and
August-September
of
1992
(roughly
peak
El Nifio
Niflo and
and La
August-Septemberof 1992 (roughly peak E1
La
do planktonic
planktonic foraminifera
track
How well
How
well do
foraminifera species
species track
changes in
in tropical
tropical oceanography
oceanography near
near the
the sea
sea surface?
surface? Can
changes
Can
temperature responses
responses [Climate:
[Climate: Long-Range
temperature
Long-Range Investigation,
Investigation,
Mapping, and
and Prediction
Mapping,
Prediction(CLIMAP)
(CLIMAP) 1981]
1981] be
be isolated
isolatedfium
fi'om
responses, such
such as
as those
other possible
other
possible responses,
those associated
associatedwith
with
productivity [Mix,
productivity
[Mix, 1989]
1989] or
or water
water column
column structure
structure
[Andreasen
and
Ravelo,
1997]?
Could
the
[Andreasenand Ravelo, 1997]? Could thefaunal
faunalestimates
estimates
be wrong
these variables
be
wrong because
becausetwo
two or
or more
moreof
of these
variablesinteract
interactin
in
of variables
variables in
in the
the
complex
complexways?
ways? Could
Could intercorrelation
intercorrelationof
it impossible
impossible to
calibration
calibration data
data sets
sets make
make it
to constrain
constrain
independent
equations?
independent equations?
On
temperature
On aa global
globalscale,
scale,sea
seasurface
surface
temperatureand
andprimary
primary
productivity are
productivity
areessentially
essentiallyuncorrelated.
uncorrelated. This
This offers
offershope
hope
Nifla events)
events) yields
yields aa large
large range
and
Nifia
rangeof
of environments
environments
andfaunas.
faunas.
Second,
the relationships
Second, the
relationships between
between the
the environmental
environmental
properties
radically within
within the
These
propertiesdiffer
differ radically
the two
two seasons.
seasons. These
seasonal
differences
provide
a
measure
of
independence
seasonal differencesprovide a measureof independence
between
properties,which
whichhelps
helps us
us assess
assess whether
all the
betweenproperties,
whether all
the
transfer functions
transfer
functions are reliable.
reliable.
2. Methods
Methods
2.
that statistical
that
statistical transfer-function
transfer-function estimates
estimatesof
of these
these variables,
variables,
calibrated using
using aa broad
broad geographic
geographic array
array of
of core
core tops,
tops, may
may be
be
calibrated
independent [Mix,
[Mix, 1989].
1989]. Within
independent
Within the
thetropics,
tropics,map
map patterns
patternsof
of
primary productivity
productivity are
primary
aregenerally
generallycorrelated
correlatedto
to mixed-layer
mixed-layer
or pycnocline
19821.
or
pycnocline depth
depth [Berger
[Berger et
et al.,
al., 1988;
1988; Levitus,
Levitus, 1982].
This
makes
it
difficult
to
calibrate
independent
equations to
to
This makes it difficult to calibrate independentequations
predict these
these two
Thus itit is
is
predict
two properties
propertiesusing
usingcore
coretops.
tops. Thus
unclear whether
transfer
can
unclear
whether paleoceanographic
paleoceanographic
transferfunctions
functions can
really isolate
isolate these
these different
properties of
of the
the upper
upper ocean
ocean or
or
really
differentproperties
whether
these
three
likely
influences
on
faunal
composition
whether these three likely influenceson faunal composition
could be
be confused
could
confusedin
in the
the geologic
geologicrecord.
record.
Here
by using
using living
living
Here we
we test
testsuch
suchfaunal
faunaltransfer
transferfunctions
functionsby
planktonic
foraminifera to
to estimate
estimate two
two'seasonal
seasonal regimes
in aa
planktonic
foraminifera
regimes
in
across
transect
the
central
equatorial
Pacific
upwelling
transect across the central equatorial Pacific upwelling
systemfrom
from12øS
12°Stoto9øN.
9°N. The
test we
with the
the living
system
The test
we make
makewith
living
fauna is
is important
important because
because the
the key
fauna
key environmental
environmentalparameters,
parameters,
water column
here
here primary
primary productivity
productivity and
and upper
upper water
column
temperature
and
structure,
were
measured
in
detail
temperatureand structure,were measuredin detailat
at the
thesame
same
time
the foraminifera
foraminiferawere
weresampled.
sampled. This
This removes
time the
removessome
someof
of the
the
ambiguity inherent
inherent in
in calibrating
calibrating equations
equationswith
with atlas
atlas data
data
ambiguity
Copyright 1998
Union.
Copyright
1998by
by the
theAmerican
AmericanGeophysical
Geophysical
Union.
Populations of
>64 gm
pm in
in
Populations
of living
living planktonic
planktonicforaminifera
foraminifera
>64
size were
were sampled
sampledinin plankton
plankton tows,
tows, using
using the
size
the Multiple
Multiple
Opening and
Sampling
System
Opening
andClosing
ClosingNet
NetEnvironmental
Environmental
Sampling
System
(MOCNESS).
Sampleswere
weretaken
takenalong
along the
the U.S.
(MOCNESS). Samples
U.S. Joint
Joint
Global
Flux
Study
(JGOFS)
equatorial
Pacific
transect
GlobalOcan
Oce•an
Flux
Study
(JGOFS)
equatorial
Pacific
transect
(9°N-l2°S,
near 140øW)
140°W) at
at II
(9øN-12øS,near
11stations
stations in
in February-March
February-March
and
both in
Live
and 12
12 stations
stationsin
in August-September,
August-September,both
in 1992.
1992. Live
(protoplasm
foraminiferalspecies
species were
were counted
counted at
at shell
(protoplasmfull)
full)foraminiferal
shell
sizes
sizes>150
> 150 .tm.
gm.
For
of this
this paper,
at
For the
the purposes
purposesof
paper,species
speciespercentages
percentages
at each
each
station
were calculated
standing stocks
hum
stationwere
calculatedfium
fromstanding
stocksintegrated
integrated
from
00 to
the
to 100
100m.
rr[ This
This integration
integrationapproximated
approximated
theaverage
average
composition
of the
the living
the euphotic
compositionof
living fauna
faunathroughout
throughout the
euphotic
zone
that
would
eventually
sink
to
build
the
zone that would eventually sink to build the geologic
geologic
record. At
ofof
foraminifera
record.
At depths
depths>100
>100m,
rn,standing
standingstocks
stocks
foraminifera
dropped
Most specimens
at these
these depths
droppedsharply.
sharply. Most
specimensat
depthslacked
lacked
protoplasm, and
and by
by inference,
were dead.
protoplasm,
inference,were
dead.
On
tow, temperatures
On the
the day
day of
ofeach
eachplankton
plankton tow,
temperatureswere
were
measured during
routine conductivity-temperature-depth
conductivity-temperature-depth
measured
during routine
(CTD) casts
casts [Murray
[Murrayet
efal.,
al., 1995].
1995]. Primary
Primary productivity
productivity was
(CTD)
was
measured
by
12-hour
in
situ
'4C
incubation
measured
by 12-hour in situ •4C incubationat each
eachsite
site
[Barber
et al.,
[Barberet
al., 19961.
1996]. These
Thesevalues
valueswere
wereintegrated
integratedhum
fi'omthe
the
surfaceto
tothe
theputative
putative0.1%
0.1%light
light level
level depth
depth (-120
surface
(-120 m)
m) to
to
yield
estimate of
of integrated
integrated primary
yieldan
an estimate
primaryproductivity
productivity (mmol
(mmol
Cm
m2
d'). Mixed-layer
Mixed-layer depth
depth (meters)
was estimated
firm
C
'2 d'l).
(meters)
was
estimated
from
CTD profiles
profiles using
using the
CTD
theLevitus
Levitus[1982]
[1982] definition,
definition, i.e., the
the
Paper number
number 97PA02904.
Paper
97PA02904.
0883-8305/98197PA-02904$ 12.00
0883-8305/98/97PA-02904512.00
96
96
WATKINS AND
TRANSFER
WATKINS
AND MIX:
MIX: FORAMINIFERAL
FORAMINIFERAL
TRANSFER FUNCTIONS
FUNCTIONS
shallowest
shallowestdepth
depth at
at which
which density
densitywas
was 0.125
0.125 density
density units
units
higher,
or at
at which
which temperature
temperature was
was 0.5øC
0.5°C lower,
lower, than
than at
at the
the
higher,or
acrossthe
the entire
entiretransect
transect(Figure
(Figurela).
la). Equatorial
upwelling
across
Equatorialupwelling
influenced temperature
temperaturelittle
little at
at the
influenced
the sea
seasurface
surfacebecause
becausethe
the
thermocline
was deep
deep and
and the
the upwelled
thermoclinewas
upwelled water
water was
waswami.
warm.
Productivity
>1
Cm
m3
Productivitywas
washigh
highon
onthe
theequator,
equator,
>1mmol
mmol C
'3 dd''l
__
sea surface.
Prior
the species
percentages were
Prior to
to data
dataanalysis
analysis the
species percentages
were
logarithmically
transformed to
to improve
improve normality.
normality. A
A Q-mode,
logarithmicallytransformed
Q-mode,
varimax-rotated
factormodel
modelthen
then simplified
simplified the
the combined
varimax-rotatedfactor
combined
data
set into
appropriate
dataset
into orthonormal
orthonormalfaunal
faunalassemblages
assemblages
appropriatefor
for
use
use in
in transfer
transfer functions.
functions.
(Figure
lc) or
Cm
m2
(Figurelc)
or >80
>80 mmol
mmolC
'2dd'
'l integrated
integratedthrough
throughthe
the
euphotic
upwelled water
water was
was enriched
euphotic zone,
zone, because
becauseupwelled
enriched in
in
nutrients
This provides
provides an
nutrients[Barber
[Barberet
etal.,
al.,1996].
1996]. This
an important
important
response
of
the
test
of
the
foraminiferal
species to
test of the response of the foraminiferalspecies
to
Faunal transfer
Faunal
transfer functions
functions were
were
calibrated
stepwise multiple
multiple regression
regression of
calibrated using
using stepwise
of the
the
productivity and
absence of
productivity
and food
foodsupply
supply in
in the
the absence
of large
large
temperature or
or mixed-layer
temperature
mixed-layerdepth
depthgradients.
gradients.
During the
During
the La
La Nifla
Nifia conditions
conditions of
of August-September,
August-September,
assemblage
factor loadings
loadings (including
(including squared
assemblagefactor
squared terms
terms and
and
interactions
onto each
[CLIMAP, 1981])
1981]) onto
each environmental
interactions [CLIMAP,
environmental
variable
using data
data from
flum both
both seasons.
seasons. Terms
were included
included
variable using
Termswere
strong upwelling
a shallow
cooled the
the
strong
upwelling from
from a
shallow thermocline
thermocline cooled
in
in an
an equation
equationonly
only if
if they
they were
were significant
significantat
at the
the 95%
95% level.
level.
Statistical
Statisticaltests
testsfor
forsignificance
significanceof
of regression
regressionrelationships
relationships
follow
Dixon and
follow Dixon
and Massey
Massey[1969].
[ 1969].
3.
3.
equatorial band
band to
to <25øC
<25°C and
and compressed
the mixed
layer to
to
equatorial
compressed
the
mixedlayer
<20
fueled
<20 m
m depth
depthnear
near2°N
2øN (Figure
(FigureIb).
lb). Upwelled
Upwellednutrients
nutrients
fueled
high
highproductivity
productivitynear
nearthe
theequator
equator(Figure
(Figureid),
l d),with
withmaxima
maxima
>2.5
mmolCC m
n13
(or >130
C n12
>2.5 mmol
'3 dd1
'l (or
>130 mmol
mmolC
m'2 dd''l integrated
integrated
Results
Results
through
the euphotic
zone) at
at convergences
near2øN
2°Nand
and
through the
euphotic zone)
convergencesnear
2°S.
2øS. Subtropical
Subtropicalwaters
waterswith
with relatively
relativelydeep
deepmixed
mixedlayers
layers
and
temperaturesoccurred
occurredsouth
south of
of 5øS
5°S and
and north
north of
and warm
warmtemperatures
of
3°N.
August-Septemberhad
had aa larger
larger range
range of
3øN. August-September
of sea
seasurface
surface
temperatures,
and mixed-layer
temperatures,productivity,
productivity, and
mixed-layerdepth
depth than
than
3.1.
3.1. Environmental
Environmental Properties
Properties
El Niflo
conditions in
to warm
El
Nifio conditions
in February-March
February-Marchled
led to
warmsurface
surface
water temperatures
temperatures (.-28°C)
and deep
water
(-•28øC)and
deepmixed
mixedlayers
layers(70-100
(70-100 m)
m)
February-March
February-March1992
1992
0-•
I
50-
August-September
1992
August-September
1992
b(°C) 2$
- -•.•..:••!.:ili:
.... :.?.:.¾::i•i.
..,•i.•.:.•i!:i'i'ii!i!ii!i::ii.:.:..i•;:'i:i.:':'i:i'::•
.:--::!:.i::?.:-.-.-::
#
.-.i;.!:?:;:•i•;i.!.i':-:.':':i:.
....":."~ •. !--?-i:::
,:.:i::':'-:i:..(?-•i!::•:•-!:::.•:i
--.-..:•"•:.:,,.•::•
....'"
......
-...........
-:-:.:-.:--.:•:i•:•.....-" •'.::.-'•
.... •.:::.::_:':':
...:.•.
:i'%ii::•i!..
'......
:i-,,." '
1
lOO
,&00-
24
a)
22
150150
•22
200 200
97
••J•//
I
•
•'*
I
•
I
•///12
'
J
I
S
7'
5S54sss
12
a.
0
,•,
5050
O.i
0.50
0.25
0.25
• 100
I100_
0.10
150150
200
200
-
C
Cm
rn3
c (mmol C
-3dd')
-1)
I
10°S
10øS
'
t
50
5ø
00
0ø
Latitude
Latitude
1i
5°N
5øN
d (mmol
d
(mmolC
C m3
m-3dd')
-1)
!
o0s
10os
50
5ø
00
0ø
5°N
5ON
Latitude
Latitude
Figure 1.
sectionsof
ofocean
ocean properties
propertiesin
inthe
thecentral
centraltropical
tropicalPacific,
Pacific, along
along the
the U.S.
Flux
Figure
1. Latitudinal
Latitudinal
sections
U.S.Joint
JointGlobal
GlobalOcean
Ocean
FluxStudy
Study
(JGOFS)
transect at
at 140°W
[Murray et
et al.,
al., 1995;
1995; Barber
Barber ef
(a) temperature
(°C)
February-March
1992
(JGOFS)
transect
140øW[Murray
etal.,
al.,1996]:
1996]:(a)
temperature
(øC)during
during
February-March
1992(El
(ElNiflo
Nifio
conditions),
(b)
(°C)
August-September
1992
Nina conditions)1
(c) in
in situ
situ primary
primary productivity
productivity (mmol
(mmol C
Cm
m3
d
conditions),
(b)temperature
temperature
(øC)during
during
August-September
1992(La
(LaNifia
conditions),
(c)
-3d'
I
ß
')during
February-March
1992,
and(d)
(d)In
In situ
situ primary
primaryproductivity
productivity(mmol
(mmolCCmm3
d) during
August-September
1992. The
) during
February-March
1992,and
-3d'l)
during
August-September
1992.
Thedashed
dashed
line
the depth
of
the mixed
mixed layer.
layer. Solid
triangles
along
the
of
field
the location
of a
a Multiple
Multiple Opening
Opening and
and
linerepresents
represents
the
depth
ofthe
Solid
triangles
along
thebottom
bottom
ofeach
each
fieldmark
markthe
location
of
Closing
Net Environmental
Sampling
System (MOCNESS)
(MOCNESS) plankton
planktontow
towstation.
station. Bands
Bands along
along the
the right
right side
side of
of each
each field
field outline
outline the
the net
Closing
Net
Environmental
Sampling
System
net
tow
tow intervals
intervals at each station.
WATKINS
TRANSFER FUNCTIONS
FUNCTIONS
WATKINS AND
AND MIX:
MIX: FORAMINTFERAL
FORAMINIFERAL
TRANSFER
98
Table 1.
(r)
Properties
Table
1. Correlations
Correlations
(r)Between
BetweenEnvironmental
Environmental
Properties
Item
Item
Tows,
Feb-March 1992
(El Nifio)
Nub)
Tows, Feb.-March
1992 (El
Tows,
Aug.-Sept 1992
Tows, Aug.-Sept.
1992(La
(La Nina)
Nifia)
Tows,
Tows, seasons
seasonscombined
combined
Global
Globalcore
coretops,
tops,40°N-40°S
40øN-40øS
Pacific
Pacificcore
coretops,
tops,24°N-24°S
24øN-24øS
Number of
of
Number
Observations
11
11
12
12
23
906
906
171
171
SST versus
versus
PROD
PROD
QQ
0.60
versus
SST versus
MLD
MLD
PROD
PROD
versus MLD
versus
MLD
0.23
0.23
0.17
0.17
QAQ
0.40
-0.10
QJQ
-0.21
:2I
0.50
-0.22
-0.22
-0.16
-0.55
-0,55
0.73a
-0.84a
-0.92
z22
-0,70
-0.46
Values
underlined are
are significantly
significantlydifferent
differentfrom
fromzero
zero (95%
(95% confidence).
confidence). SST
SST is
is the
the sea
sea surface
Valuesunderlined
surface
temperature;
PROD is
is the
the 0-100
0-100 m
m integrated
primary
MLD is
is the
the mixed-layer
depth.
temperature;
PROD
integrated
primaryproductivity;
productivity;
MLD
mixed-layer
depth.
aFor
this data
data set
set only,
only, MLD
MLD is
the depth
to
isotherm
(following
Andreasen
and
[1997]).
•Forthis
isthe
depth
to18°C
18øC
isotherm
(following
Andreasen
andRavelo
Ravelo
[1997]).
February-March and
and thus
thus provided
February-March
providedaa large
largedynamic
dynamicrange
rangeof
of
all variables
variables to
to test
all
testthe
the response
responseof
of the
theforaminiferal
foraminiferalfauna.
fauna.
Relationships
Relationships between
between these
these three
three environmental
environmental
variables differed
differed during
variables
during the
the two
two seasons
seasons (Table
(Table 1).
1).
Productivity was
was significantly
Productivity
significantly(>95%
(>95% level)
level) positively
positively
correlated to
to surface
temperature (r(r == 0.60)
0.60) during
correlated
surfacetemperature
during FebruaryFebruaryMarch
because
nutrient-rich
water
that
upwelled
March becausenutrient-rich water that upwelledwas
waswarm.
warm.
In contrast,
In
contrast,productivity
productivitywas
wasnegatively
negativelycorrelated
correlatedto
tosurface
surface
temperature(r(r =-0.92)
= -0.92) in
when cool
temperature
in August-September,
August-September,when
cool
water upwelled.
upwelled. Correlations
depth and
and
water
Correlationsbetween
betweenmixed-layer
mixed-layerdepth
were not
either sea
either
seasurface
surfacetemperature
temperatureor
or productivity
productivity were
not
significantly different
different from
from zero
significantly
zero in
in either
eitherseason.
season.
The
functions were
were calibrated
calibrated with
with data
The transfer
transfer functions
data combined
combined
from both
both seasons.
seasons. In
set, temperature
temperature and
and
from
In this
thiscombined
combineddata
dataset,
productivity
were
significantly
negatively
correlated
(r
productivity were significantly negatively correlated (r- =
-0.70), and
and mixed-layer
depth was
was significantly
significantly positively
positively
-0.70),
mixed-layer depth
correlated with
with temperature
(r == 0.40).
was not
not aa
correlated
temperature(r
0.40). There
There was
significant correlation
correlation between
significant
between mixed-layer
mixed-layer depth
depth and
and
productivity.
productivity.
3.2.
Fauna
3.2. Foraminiferal
Foraminiferal
Fauna
This
paper limits
limits the
the discussion
discussion to
to the
This paper
the depth
depthintegrated
integrated
species
factor
percentages,
assemblages,
transfer
species percentages, factor assemblages, and
and transfer
functions for
for temperature,
and mixed-layer
functions
temperature,productivity,
productivity, and
mixed-layer
depth.
properties and
and
depth. Detailed
Detailed evaluation
evaluation of
of other
other properties
documentation
of raw
raw species
species standing
standing stocks
documentation
of
stocksat
at each
eachdepth
depth
in the
et al.,
1996,
in
the water
water column
column occur
occur elsewhere
elsewhere[Watkins
[Watkins et
al., 1996,
1997].
1997]. Raw
Raw data
data are
are available
available from
fromthe
the U.S.
U.S. JGOFS
JGOFS database
database
via
1 .whoi.edu/jg/dir/jgofs/eqpac/). The
via internet
internet(http://www
(http://wwwl.whoi.edu/jg/dir/jgofs/eqpac/).
The
foraminiferal
speciesabundances,
abundances,integrated
integrated over
over the
the upper
foraminiferalspecies
upper
100
m of
of the
the water
100 m
watercolumn,
column,appear
appearhere
herein
in Table
Table2.
2.
A
factor model
model of
ofthe
the foraminiferal
foraminiferalabundance
abundance data
data
A Q-mode
Q-modefactor
(0-100
orthogonal faunal
(0-100 m)
m) resolved
resolved three
three significant
significant orthogonal
faunal
assemblages
that explained
explained 94%
94% of
of the
the data
data set
set (Table
(Table 3).
3). A
assemblages
that
A
fourth
factorwould
would explain
explain<2%
<2%of
ofthe
the data,
data, which
which is
is not
not
fourth factor
significant
small data
significant in
in this
this small
data set.
set. Seasonal
Seasonaland
and spatial
spatial
weightings of
of these
were expressed
expressed as
as the
weightings
these assemblages
assemblageswere
the
loadings of
of the
the three
three faunal
loadings
faunalfactors
factors(Table
(Table4).
4).
Factor
1,
dominated
by
Globorotalia
tumida and
Factor 1, dominated by Globorotalia turnida
and
Globoquadrina conglomerata,
at and
Globoquadrina
conglornerata,was
was most
mostimportant
importantat
and
north
but shifted
shifted south
south of
north of
of the
the equator
equatorin
in February-March
February-Marchbut
of
the
(Figure
the equator
equatorin
inAugust-September
August-September
(Figure 2).
2). A
A similar
similar
assemblage
has been
associated previously
previously with
assemblagehas
been associated
with warm
warm
equatorial
the central
and western
western Pacific
Pacific in
in both
equatorial waters
waters of
of the
central and
both
plankton
tows and
[Bradshaw,
plankton tows
and core
coretop
top sediments
sediments
[Bradshaw,1959;
1959;
Coulbourn
et al.,
Coulbournet
al., 1980].
1980].
Factor
species
such
Factor2,
2, which
whichincluded
includedseveral
severalherbivorous
herbivorous
species
such
as
Globorotalia
menardii,
Globigerinita
glutinata,
as Globorotalia rnenardii, Globigerinita glutinata, and
and
Globigerina
bulloides, had
had the
the highest
highest loadings
loadings at
at 2øS
2°S in
in
Globigerinabulloides,
February-March and
and 0-3°N
February-March
0-3øNininAugust-September.
August-September.These
These
species
are common
commoninin core
core top
top sediments
speciesare
sedimentsof
of the
the eastern
eastern
equatorial
Pacific and
and in
in the
the tropics
tropics are
are often
often associated
associated with
with
equatorialPacific
cool,
1959; Parker
Parker
cool, highly
highly productive
productivewaters
waters[Bradshaw,
[Bradshaw, 1959;
and Berger.
The widespread
widespread species
species Pulleniatina
Pulleniatina
and
Berger,1971].
1971]. The
obliquiloculata and
were
obliquiloculata
andNeogloboquadrina
Neogloboquadrina dutertrei
dutertrei were
present in
in both
both factors
factors I1and
present
and22 about
aboutequally.
equally.
Factor 3,
3, aa typical
typical subtropical
subtropical assemblage
containing the
the
Factor
assemblage
containing
species Globigerinoides
saccul(fer and
species
Globigerinoides sacculifer
andGlobigerinoides
Globigerinoides
ruber,
the equator
ruber,was
wasmost
mostprominent
prominentaway
awayfrom
fromthe
equatorin
in both
both
seasons. These
seasons.
Thesespecies
speciesobtain
obtain some
someof
of their
their nutrition
nutritionfrom
from
symbioticalgae
algae[Caron
[Caronetetal.,
al., 1981;
1981; Gastrich
Gastrich and
and Bartha,
symbiotic
Bartha,
1988] and
andthus
thus are
are well
well suited
suited for
1988]
for survival
survival in
in the
the ocean's
ocean's
oligotrophic
oligotrophicsubtropical
subtropicalregions.
regions.
3.3. Statistical
3.3.
Statistical Transfer
Transfer Functions
Functions
The multiple
multiple regressions
regressions of
of faunal
faunal factor
factor loadings
loadings on
The
on
environmentalvariables
variablesexplain
explain52%
52%of
of the
the variance
in
environmental
variance in
temperature
and
productivity
(r
=
0.72)
and
62%
in
mixedtemperatureand productivity (r = 0.72) and 62% in mixedlayer depth
depth (r(r == 0.79)
data sets
sets fim
layer
0.79) for
for the
the combined
combined data
from
February-Marchand
andAugust-September
August-September(Table
(Table5).
5). Given
Given the
the
February-March
small data
data sets,
sets, 95%
95% confidence
limits on
on these
these correlations
small
confidence limits
correlations
preclude
better than
preclude saying
saying one
one equation
equation is
is better
than another.
another.
Standard RMS
RMS errors
errors of
of the
the estimates
were +I.IøC
±1.1°C for
Standard
estimates were
for sea
sea
surface
temperature,+__23
±23mmol
mmolCCm
m2
primary
surface
temperature,
'2dd'
'i for
forintegrated
integrated
primary
productivity, and
and +15
±15 m
depth.
productivity,
m for
formixed-layer
mixed-layer
depth.
Within
factor 22 was
Within these
theseequations,
equations, factor
was associated
associatedwith
with
cooler
surface temperatures,
temperatures, higher
higher productivity,
productivity, and
and shallow
shallow
coolersurface
mixed
Factor 33 was
mixed layers.
layers.
Factor
was associated
associated with
with lower
productivity
and
mixed
productivity
anddeeper
deeper
mixedlayers.
layers.Factor
Factor11 was
wasweakly
weakly
related to
to cooler
cooler temperatures
temperaturesbut
but strongly
strongly associated
associated with
with
related
deeper
Factor 11 did
not appear
in the
deepermixed
mixedlayers.
layers. Factor
did not
appear in
the
productivity
function, which
which means
its inclusion
productivity transfer
transfer function,
means its
in'clusion
would
not have
significantly improved
estimates of
would not
have significantly
improved estimates
of
productivity.
all consistent
productivity. These
These associations
associationsare
are all
consistentwith
with the
the
geographic
ranges
of
the
species
known
for
core
geographicrangesof the speciesknown for coretop
top and
and
plankton
plankton tow
tow studies.
studies.
When the
the predictions
When
predictionsof
ofthe
thethree
threetransfer
transferfunctions
functionswere
were
considered
on
a
seasonal
basis
(Figure
3),
all
consideredon a seasonalbasis(Figure3), all the
theestimates
estimates
99
WATKINS
WATKINS AND
AND MIX:
MIX: FORAMINIFERAL
FORAMINIFERAL TRANSFER
TRANSFER FUNCTIONS
FUNCTIONS
Table 2.
Stock
(Living
Percentages
Integrated From
From 0-100
0-100 m
Table
2. Total
TotalStanding
Standing
Stockof
ofForaminfera
Foraminfera
(LivingShells
Shellsm3)
m'3)and
andSpecies
Species
Percentages
Integrated
m at
atEach
Each
Sample
Latitude in
Near
SampleLatitude
in the
theU.S.
U.S. Joint
JointGlobal
GlobalOcean
OceanFlux
FluxStudy
Study(JGOFS)
(JGOFS)Transect
Transect
Near140°W
140øW
Total
Total
conglb, ruber, sacc,
aequi, calida,
bull,
Standing conglb,ruber, sacc, aequi, calida, bull,
%
%
%
%
%
Latitude
Standing
%
% %
%
%
%
%
Stock
Stock
Latitude
dut, cnglm,
dut,
cnglm, obliq,
obliq, rnenard,
menard, tumida,
tumida, glut,
glut,
%
%
%
%
%
%
% %
%
%
%
%
Other
Other
%
%
February-March 1992
February-March
1992
9°N
9øN
7°N
7øN
5°N
5øN
3°N
3øN
2°N
2øN
1°N
IøN
0°
0ø
1°S
IøS
2°S
2øS
5°S
5øS
12°S
12øS
12.8
18.7
41.4
41.4
134.7
134.7
62.4
62.4
53.0
53.0
28.8
28.8
12.4
12.4
16.0
16.0
30.6
30.6
17.8
17.8
2.1
8.3
2.2
2.2
1.8
1.8
2.9
2.9
2.3
0.5
0.5
1.3
1.3
2.1
2.1
3.2
3.2
1.6
1.6
1.0
1.0
17.1
17.1
2.5
4.8
4.8
5.8
3.2
2.4
2.4
1.0
1.0
10.4
13.5
13.5
11.4
53.0
15.0
15.0
6.0
8.0
9.4
9.4
9.0
7.0
6.1
6.1
12.3
40.2
58.0
1.0
1.0
2.7
7.7
7.7
2.7
2.6
6.8
7.2
12.0
12.0
13.5
13.5
9.9
3.2
0.9
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
1.2
1.0
2.6
2.6
1.5
1.5
1.3
1.3
5.0
2.0
2.0
0.7
3.0
2.0
3.2
8.5
5.6
8.7
6.2
12.3
12.3
8.3
4.1
4.1
4.8
3.0
0.3
0.3
6.5
6.5
10.6
10.6
29.9
21.0
17.3
17.3
16.7
16.7
29.9
38.3
2.0
0.1
0.1
1.2
1.2
1.6
1.6
7.3
9.1
9.1
15.5
15.5
21.9
29.6
27.8
12.7
13.3
13.3
3.5
0.0
0.0
0.4
0.0
0.0
17.5
17.5
21.0
29.6
20.4
15.3
15.3
7.9
1.5
1.5
3.9
0.0
0.0
0.0
0.0
3.7
3.3
7.8
11.2
11.2
6.8
4.7
6.2
15.4
15.4
11.8
11.8
0.5
0.7
2.8
2.8
14.1
14.1
1.5
1.5
0.0
0.0
3.9
0.0
0.0
18.1
18.1
12.2
5.2
13.8
13.8
2.4
4.0
1.5
1.5
0.6
0.6
0.0
0.0
0.0
0.0
2.7
22.1
21.6
14.2
14.2
18.8
16.0
0.2
0.6
0.5
3.1
3.1
35.2
9.9
0.6
4.1
4.1
2.2
2.7
1.4
1.4
1.2
1.2
0.3
0.3
0.0
0.3
0.3
0.9
3.9
6.7
8.2
8.2
2.5
0.0
1.7
1.7
0.8
1.2
1.2
9.0
3.4
1.7
1.7
August-September
1992
August-September
1992
9°N
9øN
7°N
7øN
5°N
5øN
3°N
3øN
2°N
2øN
1°N
løN
0°
0ø
l°S
løS
2°S
2øS
3°S
3øS
5°S
5øS
l2°S
12øS
4.0
4.0
10.8
10.8
20.5
57.8
57.8
400.0
400.0
108.3
108.3
25.6
25.6
109.5
72.5
72.5
107.8
52.0
19.2
4.3
7.5
6.0
2.9
1.9
1.9
0.7
0.3
0.3
2.3
2.0
1.7
1.7
3.9
10.6
10.6
14.8
14.8
25.0
15.3
15.3
9.5
10.4
10.4
12.4
12.4
12.7
12.7
14.0
14.0
12.1
12.1
14.6
14.6
8.3
18.0
18.0
66.0
47.5
8.5
10.9
10.9
6.1
6.1
8.3
8.3
12.9
12.9
13.4
13.4
15.4
15.4
12.2
12.2
26.5
23.2
2.2
5.3
7.2
4.3
4.3
3.2
3.2
6.4
3.2
4.2
7.9
7.4
5.8
1.7
1.7
2.2
0.8
1.6
1.6
2.5
2.5
2.2
2.8
2.0
0.8
1.8
1.8
0.4
1.7
1.7
0.0
0.0
0.0
2.1
1.0
1.0
10.5
12.3
12.3
8.4
10.6
9.6
7.0
9.0
7.4
7.6
0.0
0.7
0.7
20.1
20.1
16.1
16.1
4.8
76
7.6
5.1
5.1
13.6
0.5
0.0
4.9
2.1
2.1
0.4
8.3
8.3
0.8
0.8
0.2
0.1
0.1
5.2
5.2
10.5
10.5
11.3
11.3
10.0
10.0
18.5
18.5
33.5
12.2
11.1
11.1
12.2
9.5
17.0
11.1
11.1
10.8
10.8
8.5
8.5
7.0
0.0
1.1
1.1
0.1
0.1
0.0
0.0
8.9
8.5
7.8
13.8
13.8
0.3
14.1
14.1
9.1
9.1
12.4
12.4
3.1
3.1
6.2
Here, conglb
conglobatus, ruber
ruber is
is Globigerinoides
ruber, sacc
sacculifer, aequi
aequi is
Here,
conglbis
isGlob:gerinoides
Globigerinoides
conglobatus,
Globigerinoides
ruber,
saccis
is Globigerinoides
Globigerinoides
sacculifer,
isGlobigerinella
Globigerinella
aequilateralis,
calida is
calida, bull
bullo:des, dut
dut is
duterire:, cnglm
aequilateralis,
calida
isGlobigerinella
Globigerinella
calida,
bull is
is Globigerina
Globigerina
bulloides,
is Neogloboquadrina
Neogloboquadrina
dutertrei,
cnglmisisGloboquadrina
Globoquadrina
conglomerala, obliq
is Pulleniatina
Pulleniatina obliquiloculata,
menard is
is Globorotalia
Globorotalia menardii,
menardii,tumida
tumidais
is Globorotalia
Globorotaliatumida,
lumida,and
andglut
glut is
is Globigerinita
conglomerata,
obliqis
obliquiloculata,
menard
Globigerinita
glutinata.
glutinata.
except
except that
that of
ofmixed-layer
mixed-layerdepth
depth in
in February-March
February-Marchwere
were
statistically significant.
significant. Considering
limits of
statistically
Consideringthe
the confidence
confidencelimits
of
the
the regression
regressionanalyses,
analyses,we
we can
cansay
saythat
thatin
inAugust-September
August-September
the
mixed-layer depth
depth were
the estimates
estimatesof
of mixed-layer
were significantly
significantly better
better
(r == 0.84
surface
temperature
(r
0.84 ±+0.03)
0.03)than
thanthose
thoseofofsea
sea
surface
temperature
(r
quality as
(r =
= 0.68
0.68 ±
+ 0.06),
0.06), but
but essentially
essentiallyof
of the
the same
samequality
as the
the
estimates
of productivity
productivity (r
(r == 0.77
estimatesof
0.77 ±+ 0.05).
0.05). In
In FebruaryFebruaryMarch
the estimates
temperature(r(r == 0.72
March the
estimatesof
of sea
seasurface
surfacetemperature
0.72 ±
+_
0.05)
(r
0.05) were
wereas
asgood
goodas
asthose
thoseof
ofproductivity
productivity
(r == 0.71
0.71±+0.06).
0.06).
Table
Table 3.
3. Factor
FactorScores
ScoresDescribe
DescribeOrthonormal
OrthonormalAssemblages
Assemblages
Species
Species
Factor
Factor11
Globoquadrina
Globoquadrinaconglomerata
conglomerata 0.611
0.611
Globorotalia
0.607
Globorotalia lumida
tumida
0.607
0.390
Pulleniatina obliquiloculata
Pulleniatina
obliqu#oculata
0.390
Neogloboquadrina
dutertrei
0.232
Neogloboquadrina
dutertrei
0.232
Globorotaiza
-0.031
Globorotalia menardi:
menardii
-0.031
0.064
Globigerinita
glutinata
Globigerinitaglutinata
0.064
-0.062
Globigerina
Globigerinabulloides
bulloides
-0.062
0.141
Glob:gerinella
aequilateralis
Globigerinellaaequilateralis
0.141
Globigerinella
calida
0.060
Globigerinellacalida
0.060
0.107
Globigerinoides
sacculifer
Globigerinoides
sacculifer
0.107
GlobEgerinoides
ruber
-0.027
Globigerinoides
ruber
-0.027
0.087
Globigerinoides
conglobatus
Globigerinoides
conglobatus
0.087
Percentage
of
data
explained
Percentageof data explained 38%
38%
Factor 22
Factor
Factor
Factor33
-0.145
-0.145
-0.247
-0.247
0.366
0.366
0.274
0.274
0.467
0.467
0.466
0.466
0.354
0.354
0.236
0.236
0.134
0.134
0.058
0.058
0.268
0.268
-0.016
-0.016
33%
33%
0.036
0.036
-0.063
-0.063
-0.286
-0.286
-0.012
-0.012
-0.177
-0.177
0.054
0.054
-0.125
-0.125
0.119
0.119
0.073
0.073
0.714
0.714
0.469
0.469
0.337
0.337
24%
24%
As aa sensitivity
the
As
sensitivitytest
test(not
(notshown),
shown),we
werepeated
repeated
theabove
above
experimentsusing
usingthe
the fauna
faunaintegrated
integrated•om
fim 0-40
0-40 m,
n rather
experiments
rather
than 0-100
0-100 n•
m. Faunal
and their
this
than
Faunalfactors
factorsand
their loadings
loadingsfmm
fromthis
smaller data
data set
set were
were very
very similar
similar to
to those
smaller
thoseabove.
above. Transfer
Transfer
functions were
functions
werecalibrated
calibratedusing
using these
these factor
factor loadings,
loadings,
including
both
February-March
and
August-September
including both February-Marchand August-September
samples. The
predictions
ofofprimary
samples.
Theresults
resultswere
weresignificant
significant
predictions
primary
productivity
depth (which
productivity and
andmixed-layer
mixed-layerdepth
(which contained
containedthe
the
same dominant
dominant terms
terms as
as the
the 0-100
same
0-100 m
mequations).
equations).Unlike the
the
0-100
0-100 m
m equations,
equations,however,
however,the
the predictions
predictionsof
ofsea
seasurface
surface
temperature
temperaturewere
were not
not significant.
significant.
A
test, which
A second
secondsensitivity
sensitivity test,
whichcalibrated
calibratedequations
equations
with
the AugustAugustwith the
the fauna
faunafrom
•om 0-100
0-100 m
musing
using only
only the
September samples
samples (the
(the more
variable La
September
morespatially
spatially variable
La Nifia
Nifia
conditions),
yielded statistically
significant equations
equations for
for all
all
conditions),yielded
statisticallysignificant
three
with results
results for
for sea
temperature
three variables,
variables,with
seasurface
surface
temperature
significantly
better (r
(r =
= 0.86
than
significantly
better
0.86 ±
+ 0.03
0.03 at
at95%
95%confidence)
confidence)
than
for
mixed-layer
depth
(r
=
0.66
±
0.06
at
95%
confidence).
for mixed-layer
depth(r = 0.66+ 0.06at 95% confidence).
Thus
significant
Thus all
all three
threetransfer
transferfunctions
functionsproduced
produced
significant
results,
results, with
with the
theexceptions
exceptionsthat
thatestimates
estimatesof
of sea
seasurface
surface
temperatures were
werenot
not successful
successfulin
in one
one sensitivity
sensitivity test
test and
temperatures
and
that predictions
of mixed-layer
mixed-layer depths
depthswere
werenot
not significant
significant in
in
that
predictions
of
aa test
test from
from one
oneseason.
season. The
The predictions
predictions of
primary
of integrated
integrated
primary
productivity were
productivity
were the
the only
only ones
ones to
to pass
pass all
all tests
tests of
of
significance.
We
infer
from
these experiments
that all
all of
significance.We infert•omthese
experiments
that
of the
the
variables considered,
sea surface
variables
considered,primary
primary productivity,
productivity, sea
surface
WATKINS
WATKINS AND
AND MIX:
MIX: FORAMINIFERAL
FORAMINIFERALTRANSFER
TRANSFERFUNCTIONS
FUNCTIONS
100
100
Table 4.
Loadings
and
Property
Data
Transfer
Table
4. Factor
Factor
Loadings
andOcean
Ocean
Property
DataUsed
UsedtotoCalibrate
Calibrate
TransferFunctions
Functions
Latitude
Latitude
COMM
COMM
Factor
Factor 11
9°N
9øN
7°N
7øN
5°N
5øN
3°N
3øN
2°N
2øN
0.88
0.95
0.95
0.99
0.99
0.97
0.97
0.97
0.97
0.98
0.98
0.91
0.91
0.91
0.93
0.95
0.95
0.92
0.92
0.75
0.75
0.76
0.76
Factor
Factor 2
2
Factor
Factor 3
3
SST
SST
PROD
PROD
MLD
MLD
26.5
26.5
27.9
27.9
28.4
28.4
28.4
28.4
28.3
28.3
28.5
28.5
28.3
28.3
28.5
28.5
28.6
28.6
28.7
28.7
28.5
27
27
38
38
54
54
63
63
70
70
70
90
100
100
100
110
50
70
70
90
80
54
28.3
28.3
28.3
28.3
24
24
22
42
42
57
57
February-March
February-March1992
1992
1°N
1øN
00ø
0
los
løS
2°S
2øS
5°S
5øS
12°S
12øS
9°N
9øN
7°N
7ON
5°N
5øN
3°N
3øN
2°N
2øN
1°N
1øN
0°ø
0
los
løS
2°S
2øS
3°S
3øS
Sos
5øS
12°S
12øS
0.94
0.94
0.96
0.97
0.97
0.95
0.95
0.95
0.96
0.96
0.97
0.97
0.95
0.95
0.98
0.98
0.92
0.99
0.99
0.82
0.91
0.91
0.86
0.86
0.83
0.82
0.82
0.75
0.75
0.79
0.79
0.44
0.30
0.30
0.24
0.30
0.30
0.24
0.24
0.58
0.58
0.37
0.37
0.30
0.30
0.30
0.45
0.45
0.67
0.68
0.68
0.64
0.64
0.78
0.78
0.47
0.47
0.07
0.07
0.35
0.35
0.27
0.27
0.36
0.36
0.42
0.42
0.48
0.48
0.56
0.56
0.50
0.50
0.76
0.76
0.67
0.67
0.44
0.44
0.56
0.56
0.50
0.50
0.28
0.28
0.31
0.31
0.33
0.28
0.28
0.21
0.21
0.15
0.15
0.40
0.65
0.65
0.81
0.81
August-September 1992
August-September
1992
0.27
0.88
0.27
0.88
0.43
0.84
0.43
0.84
0.41
0.69
0.69
0.41
0.83
0.35
0.35
0.90
0.24
0.90
0.24
0.89
0.30
0.89
0.82
0.29
0.82
0.29
0.61
0.36
0.61
0.61
0.37
0.61
0.37
0.36
0.61
0.36
0.37
0.49
0.37
0.49
0.23
0.74
0.23
0.74
28.1
28.1
27.0
27.0
24.2
24.2
24.7
24.7
24.9
24.9
25.3
25.3
25.3
25.3
25.3
25.3
25.9
25.9
26.4
26.4
64
64
65
65
46
46
83
83
49
49
60
60
34
135
135
no
no data
data
94
94
93
112
115
115
67
67
34
34
55
77
50
50
55
55
10
10
45
50
75
100
60
85
85
65
COMM is
is Communality,
Communality,the
thefraction
fractionofof pooled
pooled population
populationvariance
varianceexplained
explainedby
by aa linear
COMM
linear
SST is
is the
the sea
combination
of the
the three
three factors.
combinationof
factors. SST
sea surface
surfacetemperature
temperaturein
in °C.
øC. PROD
PROD is
is primary
primary
productivity
integratedover
overthe
theeuphotic
euphoticzone
zoneininmmol
mmolCCmm2
depth
in
productivity
integrated
a dd4.
4. MLD
MLDisisthe
themixed-layer
mixed-layer
depth
in
meters using
meters
usingthe
thedefinition
definitionof
ofLevitus
Levitus[1982].
[1982].
could influence
temperature,
influence the
temperature,and
and mixed-layer
mixed-layerdepth
depth could
the
foraminiferal
foraminiferal fauna.
fauna.
4.
Discussion
Discussion
4.1.
4.1. Spatial
SpatialPatterns
Patternsand
andCorrelations
CorrelationsBetween
BetweenVariables
Variables
To
To further
further explore
explorethe
the significance
significanceof
of the
thefaunal
faunaltransfer
transfer
functions,
we examined
examined the
the spatial
spatial patterns
patterns of
of the
the predictions
predictions
functions,we
of
productivity, and
of temperature,
temperature,productivity,
andmixed-layer
mixed-layerdepth
depth in
in the
the
transfer
function
was
The
temperature
two
seasons.
two seasons. The temperature transfer function was
reasonably
successful in
reconstructing the
reasonably successful
in reconstructing
the measured
measured
latitudinal
patterns of
of sea
temperature in
in both
both seasons
seasons
latitudinalpatterns
seasurface
surfacetemperature
(Figures
4a and
(Figures 4a
and 4b).
4b).
It reproduced
It
reproducedboth
both the
the observed
observed
decrease
in sea
temperature to
to the
the north
decreasein
seasurface
surfacetemperature
north in
inFebruaryFebruaryMarch
minimum
at 2°N
observed in
March and
andthe
the sharp
sharptemperature
temperature
minimumat
2øN observed
in
August-September. The
equation simulated
the
August-September.
The productivity
productivityequation
simulatedthe
broad
ofprimary
primaryproductivity
productivity apparent
broad equatorial
equatorialmaximum
maximtunof
apparent
during
both
seasons
(Figures
4c
and
4d).
during both seasons(Figures4c and 4d). The
The mixed-layer
mixed-layer
depth
depth equation
equationsuccessfully
successfullypredicted
predictedthe
theshallow
shallowmixed
mixed
layer
(Figures
layerat
at 2°N
2øNin
inAugust-September
August-September
(Figures4e
4eand
and4f).
4f).
The
transfer functions
functions derived
derived fiitn
The transfer
from the
the combined
combined data
data set
set
relationships
between
also
preserved
the
different
also preserved the different relationships between
two seasons.
temperature
in the
the two
temperature and
and productivity
productivity in
seasons.
estimates were
were essentially
Temperature
and productivity
Temperatureand
productivity estimates
essentially
uncorrelated
(r =
uncorrelated(r
= 0.19)
0.19) in
in the
theFebruary-March
February-Marchsamples
samplesand
and
were
correlated (r(r == -0.80)
-0.80) in
in the
were strongly
stronglyinversely
inverselycorrelated
the AugustAugustSeptember
samples, roughly
roughly similar
Septembersamples,
similarto
to the
thedifferent
differentseasonal
seasonal
patterns
of observed
patternsof
observedvalues
values(r
(r =
= 0.60
0.60 for
forFebruary-March
February-Marchand
and
rr =
-0.92 for
August-September). Transfer
functions correctly
=-0.92
for August-September).
Transferfunctions
correctly
reproduced
different sign
of correlations
reproduced the
the different
sign of
correlations between
between
temperature
temperatureand
andproductivity
productivityin
in February-March
February-Marchand
andAugustAugust-
September
eventhough
thoughthe
the equations
equations were
were calibrated
with
Septembereven
calibrated with
the
the combined
combined data
data sets
sets in
in which
which the
the two
two variables
variables were
were
significantly
significantlynegatively
negativelycorrelated
correlated(Table
(Table 1).
1). This
This success
successof
of
the equations
the
equationsin
in reproducing
reproducingthe
thedifferent
differentlatitudinal
latitudinalpatterns
patterns
and
and seasonal
seasonalrelationships
relationshipsof
of the
theenvironmental
environmentalproperties,
properties,
rather than
than just
just mimicking
the correlation
structure in
in the
rather
mimicking the
correlation structure
the
calibration
data
set,
is
encouraging
for
the
use
of
preserved
calibration data set, is encouragingfor the use of preserved
fauna to
to reconstruct
fauna
reconstructpast
pastvalues
valuesof
of multiple
multiplevariables.
variables.
4.2. Analysis
4.2.
Analysisof
ofResiduals
Residualsand
andBiological
BiologicalProcesses
Processes
The
functions
were
of
It is
Thetransfer
transfer
functions
werenot
notperfect,
'perfect,
ofcourse.
course.It
is
assess whether
important to
to assess
these imperfections
imperfections were
were
important
whether these
randomly
distributed, such
such as
as might
might happen
happen with
with noise
noise in
randomlydistributed,
in
counting the
species, or
counting
the species,
or whether
whether they
they were
weresystematic,
systematic,
implying an
an inappropriate
inappropriate statistical
statisticalmodel.
model. To
To explore
explore these
these
implying
possibilities, we
we plotted
possibilities,
plottedresidual
residualerrors
errorsof
ofeach
eachestimate
estimate(i.e.,
(i.e.,
estimated minus
minus observed
observed values)
values) versus
versus the
the observed
estimated
observedvalues
values
of the
(Figure 5).
5).
Some
of
the three
threeenvironmental
environmental variables
variables (Figure
Some
important relationships
important
relationshipsemerged.
emerged.
Positive
residuals (indicating
Positive temperature
temperature residuals
(indicating transfer
transfer
function
estimates that
that were
were too
too warm)
functiontemperature
temperatureestimates
warm)occured
occured
was high,
when
and negative
negative temperature
when productivity
productivity was
high, and
temperature
residuals
when productivity
productivity was
residualsoccured
occuredwhen
was low
low (Figure
(FigureSb).
5b).
101
101
WATKINS
WATKINS AND
AND MIX:
MIX' FORAMINIFERAL
FORAMINIFERAL TRANSFER
TRANSFER FUNCTIONS
FUNCTIONS
1
30
30'
0.8
29
29-
Seasurface
temperature/
p2828-
0.6
F2 •
a
2727-
Feb.-Mar.
25-
1992
oFebruary
ß August
0
10 ø
15os
5ø
0ø
5ON
24
10 ø
II
24
II
I
I
I
28 29
26 27
27
29
Observed
Observed(°C)
(øC)
25
30
30
140
o.8-
•'?
140
t bIntegrated
primary
120
120
0 t productivity
"!3.6-
-
80-
604
b Aug.-Sep.1992
'
'
'
15°S
15øS
'
I
'
100ø
10
'
'
'
I
'
'
'
50
5ø
'
I
'
0°
0ø
'
'
'
I
'
'
5°N
5ON
'
40-
40•
'
100ø
10
Latitude
Latitude
Figure
Latitudinal patterns
patterns of
of foraminiferal
Figure 2.
2. Latitudinal
foraminiferalfactor
factorassemblage
assemblage
loadings
record the
the relative
relative abundance
abundanceof
of each
each assemblage
loadingsrecord
assemblageat
at each
each
j20-]
00
0
0
-- - * August
I'
20
20 40
40 60
60 8080100
100120
120 140
140
' I • I • I • I
'
I
I
sampling
1992 and
and (b)
(b) August-September
samplingstation:
station: (a)
(a) February-March
February-March1992
August-September
1992.
compositions.
1992. See
SeeTable
Table33 for
forassemblage
assemblage
compositions.
Observed
(mmol C m
rn-2
Observed(mmol
-2dd)
-•)
Similarly,
productivity estimates
estimateswere
weretoo
too high
high (residuals
Similarly, productivity
(residuals
positive) when
when temperatures
temperatures were
were warmer
warmerand
andtoo
too low
low when
when
positive)
c Mixed layer depth
temperatures were
were cooler
cooler (Figure
(Figure5d).
5d). This
This implies
implies aa possible
possible
temperatures
interaction
in the
interaction between
between these
these environmental
environmental variables
variables in
the
faunal
faunal response.
response.
the link
We
in the
We suggest
suggest that
that the
link between
betweenvariables
variables in
the
response
of foraminiferal
foraminiferalspecies
speciesisis respiration
respiration rate,
which is
responseof
rate, which
is
driven
and food
food supply.
supply. This
driven by
by both
both temperature
temperatureand
Thiscombines
combinesaa
so-called
Effect,"inin which
which respiration
so-called "Qio
"Q10 Effect,"
respiration rate
rate increases
increases
exponentially
with temperature
temperature [Gauld
[Gauld and
and Raymont,
exponentiallywith
Raymont, 1953],
1953],
Table
Table 5.
5. Transfer
TransferFunction
FunctionEquations
EquationsCalibrated
CalibratedWith
With
Central
Central Tropical
Tropical Pacific
PacificPlankton
PlanktonTows
Tows
r
Sea
Sea surface
surfacetemperature
temperature
Integrated primary
productivity
Integrated
primary
productivity
Mixed layer
layerdepth
depth
2
0.52
0.52
0.52
0.52
0.62
0.62
RMS Error
RMS
Error
±1.1°C
_+I.IøC
±23
mmol
m2
d'4
+_23
mmolC m
'2d
±15
_+15meters
meters
Sea
temperature
isis31.6
- -10.0
(F2)2
(F1
Seasurface
surface
temperature
31.6
10.0
(F2)2--11.9
11.9
(FlF3)
F3)++11.3
11.3
(F2
F3) -3.7
-3.7 (F3)2;
(F3)2;integrated
integrated primary
primary productivity
productivity is
(F2F3)
is62.5
62.5-- 66.2
66.2(F3)
(F3)
+ 57.9
layer
depth
isis0.9
(F1)2
+
57.9(F2);
(F2);and
andmixed
mixed
layer
depth
0.9++109.0
109.0
(Fl)2++ 216.1
216.1(F2F3)
(F2F3)
- 35.0
35.0 (F2).
(F2).
•
,00
t
g
8Ol
• 60-
,.•
•
O
-
•-
//-
o
40
20 ,•'
oFebruary
---
0
11111
ß August
' I ' I ' I ' I ' I '
0 20
40 60 80 100
100 120
20
120
Observed
(rn)
Observed(m)
I
Figure 3.
3. Model
values for
Figure
Modelestimate
estimateplotted
plottedversus
versusobserved
observedvalues
for three
three
environmental
properties:
(°C),
(b)
environmental
properties:(a)
(a) sea
seasurface
surfacetemperature
temperature
(øC), (b)
integratedprimary
primaryproductivity
productivity(mmol
(mmol
m2d'l),
d'), and
and (c)
(c) mixed-layer
integrated
CCm'2
mixed-layer
depth
(meters). Models
were derived
fauna
depth(meters).
Modelswere
derivedfrom
from0-100
0-100 m
m integrated
integrated
fauna
data
Open
datacombined
combinedfrom
from both
bothseasons.
seasons.
Opencircles
circlesand
andsolid
solidregression
regression
lines
1992. Solid
linesare
are samples
samplesfrom
from February-March
February-March1992.
Solidcircles
circlesand
anddashed
dashed
regression
lines
from
1992.
regression
linesare
aresamples
samples
fromAugust-September
August-September
1992.
WATKiNS
WATKINS AND
AND MIX:
MIX: FORAMINIFERAL
FORAMINIFERAL TRANSFER
TRANSFER FUNCTIONS
FUNCTIONS
102
February-March 1992
February-March
1992
30
30
b
a
2929-
•% i 0
28
28
27
27-
r,J
2626
252524
24
August-September 1992
August-September
1992
observed•J
If'-
0
observed
ß observed
ß estimated
....
i
....
I
....
i
....
!
o estimated
ß
estimated
I ....
I ....
ß"- ''
....
I ....
I ....
140
140
d
120-
120
.c100
80-
a.0 60-
n
20-d
20
0
120
,
ß ooservea
, o ,estimated
120
•
I.
100100 e
8060-
ß estimated
....
i
....
i
....
i
....
i
f
.
ß
ø'
p
].6o
....
?..
c('
'
40
20
ß observed
ß ß i ....
15°
15 ø
10°ø
10
i ....
SOS
5øS
ß estimated
i ,, ß ß i ß ß ß ß
0°
0 ø 5°N
5ON
Latitude
Latitude
ß
10°
10 ø 15 ø
10 ø
5øS
0ø
5ON
10 ø
Latitude
Latitude
Figure
Latitudinalpatterns
patternsofofobserved
observed(solid
(solidcircles
circlesand
and solid
solidlines)
lines)and
and estimated
estimated(open
(opencircles
circlesand
and dashed
dashed lines)
lines) ocean
ocean
Figure 4.
4. Latitudinal
properties:
1992, (b)
(b) sea
(°C)
1992,
properties:(a)
(a) sea
seasurface
surfacetemperature
temperature(°C)
(øC) in
in February-March
February-March1992,
seasurface
surfacetemperature
temperature
(øC)ininAugust-September
August-September
1992,
(c)
primary productivity
productivity(mmol
(mmolCCm
m2
d') in
in February-March
February-March1992,
1992, (d)
(d) integrated
integratedprimary
primaryproductivity
productivity(mmol
(rnmol
d') in
in
(c)integrated
integrated
primary
'2d")
CCmm2
4 d4)
August-September
1992,(e)
(e) mixed
mixed layer
layer depth
depth (meters)
(meters) in
in February-March
February-March 1992,
1992, and
and (f)
(f) mixed
mixed layer
layer depth
depth (meters)
August-September
1992,
(meters)in
inAugustAugustSeptember
September1992.
1992.
with an
with
an "Ivlev
"Ivlev Effeci;"
Effect," in
in which
which growth
growth rate
rate (and
(and the
the
respiration
associated
with
this
growth)
respiration associated with this growth) increases
increases
logarithmically
with food
logarithmicallywith
food stocks
stocks[Ivlev,
[Ivlev, 1961;
1961; Vidal,
Vidal, 1980].
1980].
Thus aa high
high food
food supply
supply associated
associated with
with productive
Thus
productiveregions
regions
may yield
may
yield overestimates
overestimatesin
in transfer-function
transfer-functiontemperatures
temperatures
because the
species abundances
abundances actually
because
the foraminiferal
foraminiferalspecies
actually reflect
reflect
the characteristic
the
characteristicgrowth
growth and
and respiration
respirationrates
ratesdriven
drivenby
by both
both
variables.
variables.
The
depth appear
The residuals
residualsof
of mixed-layer
mixed-layerdepth
appearto
to be
berandomly
randomly
distributed
relative
to
temperature
and
productivity
(Figures
distributedrelative to temperatureand productivity (Figures
5g and
and 5h).
5h). Thus
of mixed-layer
mixed-layer depth
depth were
were not
not
5g
Thusthe
theestimates
estimatesof
our inference
biased by
by the
biased
the other
othervariables
variablesexamined
examinedhere.
here. If
If our
inference
interactions between
about
the biological
about the
biological cause
cause of
of interactions
between
correct,
temperature
and
productivity
is
not
temperature and productivity is correct, this
this is
is not
Respiration rate
rate is
surprising. Respiration
surprising.
is probably
probablynot
notrelated
relatedto
to mixedmixedlayer depth.
depth. There
for
of
layer
Therewas
wasaatendency
tendency
forestimates
estimates
of mixed-layer
mixed-layer
depth
to
be
too
large
when
the
mixed
layer
was
shallow and
and
depthto be too large when the mixed layer was shallow
too
This
too small
smallwhen
whenthe
themixed
mixedlayer
layerwas
wasdeep
deep(Figure
(Figure5i).
50. This
means that
that the
the transfer
transfer function
function did
did not
means
not adequately
adequatelycapture
capture
the full
the
full sensitivity
sensitivityof
of the
thevariations
variationsin
in mixed-layer
mixed-layerdepth.
depth.
4.3.
4.3. Implications
Implicationsfor
forthe
theGeologic
GeologicRecord
Record
Our
offers
hope
Our study
studyof
of the
theliving
livingfauna
fauna
offers
hopethat
thatforaminifera
foraminifera
in
more
in the
thegeologic
geologicrecord
recordmay
maybe
be used
usedto
toreconstruct
reconstruct
morethan
than
one
property
of
the
upper
ocean.
It
also
suggests
caution
one propertyof the upperocean. It also suggestscautionand
and
yields some
insights into
that may
bias geologic
yields
someinsights
into processes
processesthat
may bias
geologic
transfer functions.
transfer
functions.
There are
in this
this relatively
There
are some
somelimitations
limitationsin
relatively small
small data
dataset.
set.
We
three variables
variables (temperature,
productivity, and
We examined
examinedthree
(temperature,productivity,
and
mixed-layerdepth)
depth) that
that are
mixed-layer
are likely
likely candidates
candidates for
for primary
primary
control
of species
but certainly
controlof
speciesassemblage,
assemblage,
but
certainly other
otherinfluences
influences
may exist
exist as
may
aswell.
well. Although
Although we
weexamined
examinedaa relatively
relatively large
large
range of
range
of faunas
faunas in
in this
this dynamic
dynamic equatorial
equatorial system
system by
by
studying the
events of
of E1
El Nifio
Niflo and
and La
La Nina,
studying
the extreme
extremeevents
Nifia, we
we
sampled aa relatively
This, no
no doubt,
us
sampled
relativelysmall
smallregion.
region. This,
doubt, caused
causedus
to
important in
in other
other regions
regions and
and in
in the
to miss
misstaxa
taxa important
the geologic
geologic
record.
Different life
life spans
spans and
and preservation
record. Different
preservationchange
change the
the
relative
weighting
of
species
in
sedimentary
assemblages
relative weighting of species in sedimentary assemblages
relative
relative to
to plankton
plankton tows.
tows. Thus
Thusthe
theequations
equationscalibrated
calibratedhere
here
with
tow data
with plankton
plankton tow
data should
should not
not be
beapplied
applieddirectly
directly to
to
geologic
geologicdata.
data.
Estimates
Estimates of
of all
all three
three variables
variables we
we considered
considered were
were viable.
viable.
The
estimatesofofproductivity
productivity were
werethe
the only
only ones
ones to
to pass
The estimates
passall
all
of significance
statistical
for seasonal
and annual
statistical tests
tests of
significance for
seasonal and
annual
estimates
and for
for several
severalsensitivity
sensitivity tests
tests involving
involving subsets
estimatesand
subsets
of
the
data.
This
suggests
that
estimates
of
paleoproductivity
of the data. This suggests
that estimatesof paleoproductivity
in
in the
thegeologic
geologicrecord
recordwill
will be
beuseful.
useful.
The
influences of
of productivity
productivity and
The combined
combined influences
and temperature
temperature
on
may bias
both estimates.
estimates.
on foraminiferal
foraminiferal populations
populations may
bias both
Productivity
estimates
appear
to
be
too
high
Productivity estimates appear to be too high at
at warm
warm
temperatures,
and
temperatures,
andtemperature
temperatureestimates
estimatesappear
appearto
tobe
betoo
toowarm
warm
103
103
WATKINS
WATKINS AND
AND MIX:
MIX: FORAMINIFERAL
FORAMINIFERAL TRANSFER
TRANSFER FUNCTIONS
FUNCTIONS
ß
I..
2
a
I
.
I
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27
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28
Observed sea surface
surface
Observed
temperature (°C)
temperature
(øC)
'
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00
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24
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o
S
ß
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o ß
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S
ß
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ß
-30
-30
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o
fib
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o0o
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i
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-e.1
ß
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r
S
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5
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3O
ß
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I.
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oo
I
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--
ß
S
-
8 0
o
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S
S
00
-
-
(Ooø
•Po
0
.
I
I
S
II
.ß
II
ß
20
20 40
40 60
60 80
80 100
100 120
120 140
140
Observed
primary productivity
Observedprimary
productivity
(mmol C
Cm
rn2
d1)
(mmol
'2d
'1)
-
_
0
.,
i
0
0
I
.
1
0
0
ß
ß
S
I
,
II
.
II
,
0.
101' o
I
Q
I
20
40 60
80
20
40
60
80 100
100
Observed
mixed
layer
Observedmixed layer
depth
depth(m)
(m)
.
ß
120
120
Figure 5.
errors
ofofocean
properties
(estimated
minus
observed)
plotted
bias
Figure
5. Model
Modelresidual
residual
errors
ocean
properties
(estimated
minus
observed)
plottedversus
versusobserved
observedproperties
propertiesassess
assess
biasor
or
interaction of
in
estimates:
residuals
versus
temperatures,
(b)
interaction
of properties
properties
inthe
thetransfer-function
transfer-function
estimates:(a)
(a) temperature
temperature
residuals
versusobserved
observedsea-surface
sea-surface
temperatures,
(b)
temperature
(c) temperature
residuals versus
versus observed
observed mixed
mixed layer
layer depth,
temperatureresiduals
residualsversus
versusobserved
observedprimary
primaryproductivity,
productivity,(c)
temperatureresiduals
depth,(d)
(d)
productivity residuals
residuals versus
versus observed
observedsea
seasurface
surfacetemperatures,
temperatures,(e)
(e) productivity
productivityresiduals
residualsversus
versusobserved
observed primary
primary productivity,
productivity,(f)
(f)
productivity
productivity residuals
residualsversus
versusobserved
observedmixed
mixedlayer
layerdepth,
depth,(g)
(g)mixed
mixed
layerdepth
depthresiduals
residualsversus
versusobserved
observedsea
seasurface
surfacetemperatures,
temperatures,
productivity
layer
(h) mixed-layer
mixed-layer depth
versus
primary
and (i)
(i) mixed
mixed layer
layer depth
depth residuals
residuals versus
versus observed
observed mixed
(h)
depthresiduals
residuals
versusobserved
observed
primaryproductivity,
productivity,
and
mixedlayer
layer
depth. Dashed
mark the
errors of
of regression.
regression. Open
from
depth.
Dashedhorizontal
horizontallines
linesmark
theaverage
average±1
+1(• RMS
RMS errors
Opencircles
circlesare
aresamples
samples
fromFebruary-March
February-March
1992; solid
solid circles
circles are
are samples
1992.
1992;
samplesfrom
from August-September
August-September
1992.
at
This relationship
at high
high productivity
productivityvalues.
values. This
relationshipsuggests
suggestssome
some
biological process
process that
that combines
combines these
these effects.
effects. We
biological
We speculate
speculate
that the
the link
link is
which is
is known
to
that
is through
through respiration
respirationrate,
rate, which
known to
be
by both
and feeding
feeding rate
rate in
be influenced
influenced by
both temperature
temperatureand
in most
most
organisms.
this type
organisms. If
If this
type of
of bias
bias exists
existsin
in geologic
geologictransfer
transfer
account for
functions,
it could
could perhaps
perhaps account
of the
functions, it
for some
some of
the
transfer
function
and
geochemical
mismatches
between
mismatches between transfer function and geochemical
temperature estimates
[CLIMAP,
temperature
estimatesof
of the
thelast
lastglacial
glacialmaximum
maximum
[CLIMAP,
1981;
Guilderson et
et al.,
1981; Guilderson
al., 1994].
1994].
To
detect and
and remove
remove this
this bias
bias fi'm
To detect
fromgeologic
geologic estimates
estimatesof
of
temperature
and
productivity
may
be
difficult,
but
temperatureand productivity may be difficult, but aa key
key fact
fact
leads
us to
to think
think it
be possible.
possible. In
leadsus
it may
may be
In the
theset
setof
of all
all core
coretops
tops
in which
have been
been counted
counted between
which foraminifera
foraminifera have
between 40°N
40øN and
and
40°S
40øS [Prell,
[Prell, 1985]
1985] the
thecorrelation
correlationbetween
betweenmean
meanannual
annual sea
sea
surface
temperature [Levitus,
[Levitus,1982]
1982] and
and primary
primary productivity
productivity
surfacetemperature
(data
Berger et
(datafrom
from Berger
et al.
al. [1988]
[1988])) is
isnearly
nearlyzero
zero(Table
(Table1).
1). This
This
statistical independence
statistical
independenceof
of properties
properties in
in the
thewater
watercolumn
column
means that
that we
we may
means
may be
be able
ableto
to detect
detectinteractions
interactionsand
andbiases
biasesin
in
the
the core
coretop
topcalibrations
calibrations and,
and, by
by including
including the
therelevant
relevant
processes in
in predictive
to correct
correct for
for them
them in
in the
processes
predictive models,
models,to
the
geologic record.
geologic
record. Of
Of course,
course, analysts
analysts must
must be
be careful
careful in
in
assembling their
their core
core top
top calibration
assembling
calibration data
data sets
setsto
to maintain
maintain
this
this statistical
statisticalindependence.
independence.
Some
have used
species
Somehave
usedthe
thegeologic
geologicrecord
recordof
of foraminifera
foraminifera
species
to assess
to
assesschanges
changes in
in mixed-layer
mixed-layer(or
(or pycnocline)
pycnocline) depth
depth
[Andreasen
1997].
Our finding
[Andreasen and
and Ravelo,
Ravelo, 1997].
Our
finding that
that an
an
apparently
unbiased relationship
relationship may
may exist
exist between
apparently unbiased
between the
the
living fauna
that such
living
fauna and
andmixed-layer
mixed-layerdepth
depth indicates
indicates that
such
attempts
too, however,
attemptsare
arereasonable.
reasonable. Here
Here too,
however, we
we suggest
suggest
caution.
mixed-layer depth
caution. In
In the
themodern
modemocean,
ocean, mixed-layer
depth (or
(or
pycnocline
depth)
and
primary
productivity
are
pycnocline depth) and primary productivity are highly
highly
correlated
correlatedwith
with each
eachother,
other,especially
especiallyin
in the
the tropics
tropics(Table
(Table 1).
1).
104
WATKINS AND MIX:
WATKINS
MIX: FORAMINIFERAL
FORAMINIFERAL TRANSFER
TRANSFER FUNCTIONS
FUNCTIONS
Productivity and
and food
supplies tend
Productivity
food supplies
tend to
to be
behigher
higherwhere
wherethe
the
pycnocline
is shallow
of the
the higher
higher availability
pycnoclineis
shallow because
becauseof
availability of
of
biologists have
nutrients. Indeed,
nutrients.
Indeed, some
somebiologists
haveused
usedwater
watercolumn
column
structure
to predict
productivity [Herbiand
structureto
predictmodem
modemprimary
primaryproductivity
[Herbland
and
Voituriez,
1979].
Thus,
in
a
core
and Voituriez, 1979]. Thus, in a core top
top calibration
calibration of
of
it would
would be
difficult to
faunal
faunal transfer
transferfunctions
functions it
be very
very difficult
to
distinguish
distinguishbetween
betweenthese
thesetwo
twoproperties.
properties.
Our
observationofofaaconsistent
consistent relationship
relationship of
of the
the living
living
Our observation
fauna
to
primary
productivity
present
in
two
very
different
faunato primaryproductivity present in two very different
seasonal
to
seasonalsettings
settingssuggests
suggeststhat
that the
thefauna
faunaresponds
responds
to primary
primary
in the
productivity
productivity as
aswell
well as
asmixed-layer
mixed-layerdepth,
depth,at
at least
least in
the
the relationship
relationship of
central
centralPacific
Pacificsetting
settingwe
we studied.
studied. If
If the
of food
food
supply
through
supplyto
to pycnocline
pycnoclinestructure
structurestayed
stayedconstant
constant
throughtime,
time,
then
estimates
of
thenit
it is
isvery
verylikely
likelythat
thattransfer-function
transfer-function
estimates
of mixedmixedlayer
layer depth
depthwould
wouldbe
besuccessful.
successful. But
But if
if the
therelationship
relationship
between
betweenthese
theseproperties
propertieschanges,
changes,for
forexample,
example,because
becauseof
of
regional
changes
in
subsurface
nutrients
or
in
eolian
regionalchangesin subsurface
nutrientsor in eolianfluxes
fluxesof
of
nutrients
nutrients without
without corresponding
correspondingchanges
changesin
in water
watercolumn
column
structure,
then changing
changing productivity
productivity could
structure,then
coulddrive
drive erroneous
erroneous
transfer-function
estimates
transfer-function
estimatesof
of mixed-layer
mixed-layerdepth.
depth.
5. Conclusions
Conclusions
situ measures
the living
By using
By
using the
living fauna
fauna and
and in
in situ
measuresof
of
environmental properties
properties (sea
environmental
(seasurface
surfacetemperature,
temperature,integrated
integrated
primary productivity,
productivity, and
depth) we
primary
and mixed-layer
mixed-layerdepth)
we evaluated
evaluated
potential interactions
potential
interactions between
between these
these properties
properties as
as they
they
influence
influenceforaminiferal
foraminiferalpopulations.
populations. Key
Key elements
elementsof
of our
our
transfer function
function test
test were
were aa large
largedynamic
dynamicrange
range provided
provided by
by
transfer
the seasonal
between E1
El Nifio
Niflo and
and La
La Nitla
Nina events
events in
in
the
seasonal contrast
contrast between
1992 and
and different
relationships between
the environmental
1992
differentrelationships
betweenthe
environmental
properties in
that provided
provided statistical
properties
in the
the two
two seasons
seasons that
statistical
independence.
independence.
Three orthogonal
faunal assemblages
accounted for
for 94%
94% of
Three
orthogonalfaunal
assemblages
accounted
of
the living
the
living fauna
faunaof
ofthe
thecentral
centralequatorial
equatorialPacific.
Pacific. Transfer
Transfer
function equations
equations reconstructed
about half
function
reconstructedabout
half the
the observed
observed
variance of
temperature, productivity,
productivity, and
depth
variance
of temperature,
andmixed-layer
mixed-layerdepth
when both
both seasons
The
when
seasons were
were included
included in
in the
the calibration.
calibration.
The
statistically
estimates of
estimates
of primary
primary productivity
productivity were
were statistically
significant
in both
significantin
both seasons
seasonsand
androbust
robustin
inall
allsensitivity
sensitivitytests.
tests.
This suggests
ofofpaleo-productivity
This
suggeststhat
thatestimates
estimates
paleo-productivity in
in the
the
geologic record
record will
will be
be useful.
and
geologic
useful. Estimates
Estimatesof
oftemperature
temperatureand
mixed-layerdepth
depthpassed
passed most
most of
of the
mixed-layer
the statistical
statistical tests
tests we
we
applied and
applied
and successfully
successfully reconstructed
reconstructed the
the spatial
spatial and
and
seasonal
patterns across
seasonalpatterns
acrossthe
theequatorial
equatorial Pacific
Pacific upwelling
upwelling
system.
This
offers
hope
that
more
than
one
system. This offershope that more than oneenvironmental
environmental
property
frompopulation
population data
data in
in the
the fossil
property can
can be
be extracted
extractedfrom
fossil
record.
record.
Transfer
function estimates
Transfer function
estimatesof
of temperature
temperaturewere
werewarmer
warmer
than
observations
when
productivity
was
high
than observationswhen productivity was high and
and cooler
cooler
than
than observations
observationswhen
when productivity
productivity was
waslow.
low. Similarly,
Similarly,
productivity
were
productivityestimates
estimateswere
were too
too high
highwhen
whentemperatures
temperatures
were
warm
and too
too low
Thus the
warm and
low when
whentemperatures
temperatureswere
were cool.
cool. Thus
the
combined
combined influences
influencesof
of productivity
productivity and
and temperature
temperatureon
on
foraminiferal
populations may
may bias
bias both
foraminiferalpopulations
both estimates.
estimates. We
We
suggest
that the
the biases
biological respiration
suggestthat
biasescome
come from
frombiological
respirationrate,
rate,
which
which is
is known
known to
to be
beinfluenced
influencedby
by both
bothtemperature
temperatureand
and
feeding
rate. This
This type
type of
bias in
feedingrate.
of bias
in geologic
geologictransfer
transferfunctions
functions
could
between
could perhaps
perhapsaccount
accountfor
for some
someof
of the
themismatches
mismatches
between
faunal
temperatureestimates
estimatesof
ofthe
the last
last glacial
faunal and
and chemical
chemicaltemperature
glacial
madmunt
maximum.
Caution
Cautionis
is warranted
warrantedin
in using
usingtransfer
transferfunctions
functionsto
to predict
predict
multiple
properties
of
ancient
oceans.
Many
of
the
properties
multiplepropertiesof ancientoceans.Many of the properties
(for
productivity and
and mixed-layer
mixed-layer depth)
(for example
example productivity
depth) are
are
correlated
This means
that geologic
correlatedin
in the
themodern
modemocean.
ocean. This
meansthat
geologic
transfer
functions
calibrated
with
core
tops
may
transferfunctionscalibrated with core tops mayconfuse
confuseone
one
ocean
and if
the relationships
oceanproperty
property for
for another,
another,and
if the
relationshipsbetween
between
properties
change through
time, the
the transfer
transfer function
function
properties change
through time,
estimates
The problem
problem is
is worse
estimatescould
could be
beerroneous.
erroneous. The
worse in
in
regional
than in
regional calibrations
calibrationsthan
in global
global arrays.
arrays.
Progress
Progresswill
will come
comefium
fromtwo
two approaches.
approaches. Studies
Studiesof
of the
the
living
fauna, both
both in
in the
the field
field and
and in
in cultures,
cultures, will
will lead
lead to
to aa
living fauna,
better
of
chemical,
and biologic
biologic
betterunderstanding
understanding
ofthe
thephysical',
physical:,
chemical,
and
processes
that
influence
populations
of
shell-forming
processes that influence populations of shell-forming
plankton.
More attention
attention to
to optimizing
optimizing the
plankton. More
the design
designof
ofcore
core
top
top data
data sets
setswill
will improve
improvestatistical
statisticalindependence
independenceof
of key
key
properties
By combining
insights into
propertiesof
ofthe
thecalibrations.
calibrations. By
combining insights
into
biological
response mechanisms
mechanisms that
may bias
transfer
biological response
that may
bias transfer
functions
fiim studies
studies of
fauna, with
with aa new
functions from
of the
the living
living fauna,
new
generation
of high-quality
core top
generation of
high-quality core
top calibrations
calibrations an
an
opportunity
now exists
exists to
to improve
transfer functions
functions and
opportunitynow
improve transfer
and our
our
understanding
of changes
understandingof
changesin
in multiple
multipleproperties
properties of
of past
past
oceans.
oceans.
Acknowledgments.
Acknowledgments. We
We thank
thankU.S.
U.S.JGOFS
JGOFScollaborators
collaborators L.
Welling,
J. White,
and M.
M. Roman
Roman (who
(who collected
collected the
the plankton
planktontows),
tows), R.
R
Welling, J.
White, and
Barber (responsible
Barber
(responsiblefor
for primary
primary productivity),
productivity), and
and J.J. Murray
Murray
(hydrography) as
as well
well as
as others
others who
who assistant
assistantat
at sea
sea and
J.
(hydrography)
andin
inthe
thelab.
lab. J.
Wilson and
and A.
N. Pisias
Wilson
A. Morey
Moreyprovided
providedtaxonomic
taxonomicassistance.
assistance. N.
Pisias
improved
Norris, D.
improvedthe
thestatistics.
statistics.Reviewers
ReviewersR.
R. Norris,
D. Andreason,
Andreason,and
andW.
W.
Howard helped
the manuscript.
manuscript. This
Howard
helpedto
to improve
improvethe
Thisproject
projectwas
wasfunded
funded
under
under NSF
NSF grant
grantOCE-90-22299.
OCE-90-22299. This
This paper
paper is
is U.S.
U.S. JGOFS
JGOFS
contribution 438.
contribution
438.
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