Thesis Proposal
Carbon Sequestration by Mesopelagic Fish
2:00 pm, 8-June, 2007
Vaughan Hall, room 328
Pete Davison
Scripps Institution of Oceanography
Committee:
Dr. Dave Checkley (advisor)
Dr. Mark Ohman
Dr. Phil Hastings
Dr. Rob Pinkel
Dr. Frank Powell
Dr. Tony Koslow
Dr. Jeff Graham
1
Abstract:
The active transport of carbon out of the surface ocean by migrating fish that form
the sonar Deep Scattering Layer (DSL) is poorly known, but potentially large.
Physiological data, mortality rates, and biomass estimates from the literature were
combined with the diel migration behavior of mesopelagic fish into a computer
model. The model results indicate an overall transport of 11.0 mgC m-2 d-1 or 1.3*1015
gC y-1 worldwide that is mediated by these fish. This is larger than comparable
estimates for the zooplankton and passive transport. I propose to enhance our
understanding of the influence of the DSL on carbon sequestration in the ocean by
measuring some of the governing physiological parameters in the context of the diel
movement of these fish, by improving biomass estimates, and then by combining
these data with global midwater temperature measurements to calculate regional and
global flux values of active transport.
Introduction:
The global carbon cycle is of interest due to the large and increasing amount of
anthropogenic CO2 that has been released into the atmosphere since the beginning of
the industrial age (Keeling et al., 1976). Much of this CO2 has been taken up by the
ocean. The large mismatch in reservoir sizes and turnover times between the ocean
and atmosphere means that oceanic carbon processes are important in determining
the atmospheric concentrations (Watson and Orr, 2003). The rate of net carbon
absorption by the ocean is thought to be ~2*1015 g = 2 Pg y-1 (Sarmiento and
Siegenthaler, 1992; Watson and Orr, 2003), although the gross atmospheric exchange
fluxes shown in Figure 1 are much larger. The air-sea flux of CO2 results from, and is
proportional to, the partial pressure differences between the atmosphere and the
ocean surface (Pilson, 1998; Takahashi et al., 2002). In general, outgassing occurs in
upwelling areas where older CO2-rich water is brought to the surface and warms up.
CO2 uptake occurs where surface water cools (the “solubility pump”) and when the
partial pressure of CO2 is reduced below the atmospheric level through incorporation
by photosynthetic autotrophs (Takahashi et al., 2002). Incorporation by
photosynthetic autotrophs (“new primary production”) is offset by respiration from
all organisms. The size of this imbalance is referred to as “net community
production”. It is maintained over an appropriate time scale by an equivalently sized
export term known as “export production” (Eppley and Peterson, 1979). The portion
of export production going to deeper water is known as the “biological pump”. The
production exported to the deep sea has been calculated between 10-11 Pg y-1
(Falkowski et al., 2003; Schlitzer, 2002). This is about two orders of magnitude larger
than world-wide export to fisheries (Jennings et al., 2001). About 70% of the CO2
concentration gradient in the top 1000m of the ocean is maintained by biological
2
Figure 1: Oceanic carbon cycle (Sarmiento and Gruber, 2002). Anthropogenic
portions are red.
processes (Volk and Hoffert, 1985). The magnitude of the biological export flux
influences more than just the total amount of CO2 imported from the atmosphere.
The rate of air-sea exchange is influenced because diffusion rates are proportional to
concentration differences. The biological pump is not a single mechanism, but rather
a group of several different processes mediated by a wide array of organisms. It is
broadly divided into passive and active transport terms. Passive transport refers to
the sinking of Particulate Organic Matter (POM) and Particulate Inorganic Matter
(PIM) through the water column. Both forms contain carbon. Active transport is the
flux of material physically carried by animals as they move daily, seasonally, or
ontogenetically across a depth range. This flux depends on the relative locations
within the water column of ingestion, respiration, excretion, defecation, and
mortality. Material may pass back and forth between active and passive portions of
the biological pump as it successively ingested and eliminated. A comparison of past
measurements of various portions of the biological pump is shown in Table 1. While
the magnitude of the biological pump is small compared to the total air-sea exchange,
it maintains the deep ocean as a CO2 sink rather than a CO2 source. If biological
carbon export were to reduce or cease, atmospheric CO2 concentration would
increase sharply over a time scale of decades due to oceanic outgassing (Sarmiento and
LeQuere, 1996; Watson and Orr, 2003). The depth over which air-sea exchange, or
3
Table 1: A comparison of past measurements of portions of the biological pump. Measurements were converted between local
and global rates using an open ocean area of 332x106 km2 (Pauly and Christensen, 1995) to put them in similar units.
Derived numbers are enclosed in parentheses.
term
method
location
passive transport
passive transport
passive transport
sediment traps
sediment traps
primary productivity
and the f-ratio
inverse modeling of
nutrients
zooplankton respiration
fish defecation
fish mortality
Fish respiration and
defecation
Fish respiration,
mortality, and
defecaton model
N. Pacific (open ocean)
N. Pacific
global
passive transport
active transport
active transport
active transport
active transport
active transport
local rate
(mgC m-2d-1)
33
(61)
(91)
global rate
(PgC y-1)
(4)
7.4
11
(83)
10
mean of 6 studies
Azores
Tasmania
W. Equatorial Pacific
2.9
2.5
2.1-7.4
13.7
(0.4)
(0.3)
(0.3-0.9)
(1.7)
global
(11.0)
1.3
global
reference
(Martin et al., 1987)
(Martin et al., 1987)
(Falkowski et al., 2003)
(Schlitzer, 2002)
§
(Angel, 1985)
(Williams and Koslow, 1997)
(Hidaka et al., 2001)
this study
§ (Al-Mutairi and Landry, 2001; Ikeda, 1985; Le Borgne and Rodier, 1997; Longhurst et al., 1990; Steinberg et al., 2000; Vidal and
Whitledge, 1982)
4
“ventilation”, occurs is largely determined by wind strength. The combination of
winds and heat exchange creates a surface mixed layer of relatively uniform
chemistry. Water within this layer mixes rapidly and is thus exposed to atmospheric
conditions. The bottom of this layer is the natural boundary to measure export flux
across because any change in CO2 concentration below the mixed layer will not be
communicated to the surface over short time scales.
The potential importance of active transport is illustrated by the Diel Vertical
Migration (DVM) behavior of the sonar Deep Scattering Layer (DSL). The DSL was
discovered in 1942 by Eyring, Christensen, and Raitt at the University of California
Division of War Research (Beamish et al., 1999). The DSL is a strong and ubiquitous
sound-reflecting layer of organisms. A portion of the DSL rises at night and descends
during the day. In 1951 two authors first attributed the DSL to mesopelagic fish
(Marshall, 1951; Tucker, 1951). Times of ascent and descent have been linked to
changes in light irradiance (Boden and Kampa, 1967). The DSL is found across all
oceans in water greater than 200m depth. It often has a complex spatial, temporal,
and frequency structure. At frequencies between 30 and 100 KHz the DSL is chiefly
composed of midwater fish with gas-filled swim bladders and some of the larger,
more reflective zooplankton (Beamish et al., 1999; Simmonds and MacLennan, 2005).
At higher frequencies, smaller animals such as copepods become “visible” to sonar.
The gas inclusions of fish and physonect siphonophores resonate at different
frequencies depending on the size and shape of the inclusion. Not all of the animals
populating the DSL exhibit DVM. Many of the animals inhabiting the DSL have an
annual life cycle, so there may be seasonal variability in its strength. Figure 2 shows
an example day-night migration cycle of the DSL. This migration cycle is thought to
be an adaptation to high visual predation risk in the epipelagic layer, which also
contains the highest food density (Angel, 1985). The dominant taxonomic group of
fish (by weight) in the mesopelagic ocean is the family Myctophidae, which
predominately consume pelagic crustaceans (Gjosaeter and Kawaguchi, 1980). The
global biomass of midwater fish that populate the DSL has been estimated as at least
one billion tons, or 1 Pg wet weight (Gjosaeter and Kawaguchi, 1980). For
comparison, annual world-wide fish landings have were about 0.09 billion tons y-1
between 1994 and 1997 (Jennings et al., 2001). During the time spent at deeper
depths, these fish will “lose” carbon in the form of respired CO2, feces, excreta, and
mortality. Midwater fish ingestion rates have been measured over a range of 1-4.5%
of their dry weight per night (Gartner et al., 1997). A crude calculation of the carbon
flux through these fish is possible with the assumption of conservation of mass and
some weight ratios. Assuming 69% water content for a myctophid fish (Childress and
Nygaard, 1973) and a C to dry weight ratio of 0.4 from a typical prey species
Euphausia pacifica (Childress and Nygaard, 1974) the annually processed carbon is
0.4*(0.31*fish mass*ration)*365 = 0.5 to 2.1 Pg y-1. These numbers are comparable to
5
estimates of the flux of sinking particulate organic carbon (POC) used in box models,
and they invite closer examination.
a
03:00
09:00
15:00
21:00
03:00
09:00
03:00
09:00
15:00
time (PDT)
21:00
03:00
09:00
area backscattering
10
5
0
b
100
depth (m)
200
300
400
500
600
700
Figure 2: R/V Revelle HDSS ADCP data collected from the Equatorial Pacific on 23-24
Sept. 2005 at latitude 1°N and longitude 124°W. Day, night, and nautical
twilight are marked in the center bar. a) Area backscattering coefficient sa (m2
m-2), as defined in Equation 10. b) Volume backscattering strength Sv (dB m-1,
red is more, blue is less). Sv is defined in Equation 9.
I propose to increase the accuracy and spatial resolution of our understanding of the active
transport term of the biological pump by quantifying the carbon transport mediated by fish
in the DSL. This involves addressing three aspects of the problem: the amount of carbon
transported per fish, the quantity of migrating fish, and the relationship between the global
distribution of these fish with respect to the temperatures that they experience and the food
that they consume. These three aspects would be combined using a computer model to
produce a global-scale map of active transport with regional resolution.
6
Background:
Part1. Physiological and Ecological Rates:
The foundation of any measurement of carbon transport by mesopelagic fish will be
the establishment of appropriate physiological and ecological rates. These interact
through the energy budget or mass balance of a fish (Equation 1).
Ingestion = Production + Respiration + Defecation + Excretion
(1)
Direct measurements of physiological rates on mesopelagic fish are few due to the
difficulty of keeping them in captivity (Robison, 1973; Torres et al., 1979). However,
there are some rates in the literature for small fish with a similar diet, and theory is
well developed. Of special interest is the potential interaction between physiology,
DVM behavior, and the temperature changes associated with DVM. I have selected
published rates and combined them with DVM into a model of the carbon transport
due to a single fish, hereafter referred to as the “single-fish model”. I propose
augmenting and supporting these rates with some of my own original work.
Excretion has not been included in the single-fish model because its carbon content is
small in comparison to the other fluxes as calculated from the proximal composition
and urine flow rate of marine fish (Hickman and Trump, 1969). A stable population
size and age structure are assumed in the single-fish model, thus equating production
with mortality.
Respiration:
Respiratory release of carbon in the form of CO2 results from the catabolism of
organic matter for energy. The basal level of respiration required for tissue
maintenance is referred to as the Standard Metabolic Rate (SMR). Additional energy
is required for activity such as swimming, feeding, and processing of food. The
process of absorption, molecular transformation, and storage of food is referred to as
the Specific Dynamic Action (SDA). The SDA can be substantial. It consumes about
14% of the ingested energy, averaged from several studies of carnivorous fish (Brett
and Groves, 1979). The SDA may approximately double the metabolic rate for ~24
hours as shown in Figure 3 (Jobling and Davies, 1980). The sum of these energy
expenditures is the Routine Metabolic Rate (RMR), as shown in Equation 2.
RMR = SMR + Ractivity + SDA
(2)
For the purpose of the single-fish model, I have used a multiple linear regression for
the yellow perch SMR (Equation 3) and applied a factor of 4.1 (Equation 4) to get the
RMR (Enders et al., 2006). This is a crude approximation used for the initial version
7
of the single-fish model. These rate equations are plotted in Figure 4. Single-point
measurements for myctophids from other sources are also plotted for comparison.
Figure 3: SDA of plaice (Jobling and Davies, 1980). Triangles, squares, and circles
represent increasing meal sizes (1x, 2x, 4x).
2
respiration (mgO h-1 g-1 wet weight)
10
10
10
10
10
2
perch SMR regression
perch RMR approximation
myctophids
1
0
-1
SMR = aWbecT
a=0.01, b=0.72, c=0.19
-2
0
5
10
15
20
o
temperature ( C)
25
30
Figure 4: Yellow Perch respiration equations (Enders et al., 2006) shown as lines.
Individual points are measurements made on myctophids (Donnelly and
Torres, 1988; Torres et al., 1979; Torres and Somero, 1988).
8
SMR = 0.01*(wet weight)0.72*e0.19*temperature mgO2 hr-1 mg-1 wet weight
(3)
RMR = 4.1*SMR
(4)
The molar ratio of CO2 respired to O2 consumed is called the respiratory quotient
(RQ). The RQ is used to calculate respiratory carbon flux from oxygen consumption.
The RQ differs for the proximate dietary components, as shown in Table 2. In
combination with the proximate composition of marine crustacean ash-free dry
weight (Table 3), the overall RQ of was calculated to be 0.85 for use in the single-fish
model.
Table 2: Metabolic properties of dietary components
dietary
RQ
component
(Brett and Groves, 1979)
protein
0.9
lipid
0.7
carbohydrate
1.0
% Carbon
digestible fraction
(Harris et al., 2000) (Phillips and Brockway, 1959)
53.1
0.9
77.6
0.85
44.4
0.4
Table 3: Proximate composition of zooplankton
dietary
% crustacean dry weight
component
(Harris et al., 2000)
protein
50
lipid
22
carbohydrate
5
ash
23
% crustacean ash-free dry weight
(Harris et al., 2000)
65
29
6
0
Defecation:
The carbon flux resulting from the defecation by migrating fish is a function of
behavioral pattern (DVM, feeding periodicity), ingestion rate, assimilation efficiency,
and defecation rate. For a poikilotherm, daily food intake (Jobling, 1993) and
stomach evacuation rate vary with ambient temperature. Stomach evacuation rate
(used as a proxy for defecation rate) in relation to temperature has been measured on
sockeye salmon (Brett and Higgs, 1970). This rate varied between ~24 hours at 5°C to
~8 hours at 15°C for 75% evacuation of the stomach contents. Assimilation efficiency,
or the ratio of the food assimilated to the food ingested, differs for the three major
dietary components (Table 2). It has been measured in fish to be between 70-95%
overall (Jobling, 1993). Carbon assimilation efficiency in the single-fish model is
estimated to be 86%, based on the proximate organic matter composition (Table 3)
9
and the digestibility fractions (Table 2) of zooplankton. The assimilation efficiency
has been shown to decrease with decreasing temperature (Elliott, 1976), but this has
not been incorporated into the single-fish model. Daily rations are 1-6% of dry
weight in several studies on myctophids (Gartner et al., 1997). For the purpose of the
preliminary single-fish model, I have assumed a daily ration of 3% dry weight
(Pakhomov et al., 1996; Tseitlin and Gorelova, 1978), a constant gut-passage time of
10 hours, continuous feeding at night, and continuous elimination. In the single-fish
model, feces released above the flux boundary do not contribute to the export. This is
a conservative assumption because the depth of remineralization is much greater than
the point of release due to the fast sinking rate for fecal matter (Angel, 1985).
Mortality:
Carbon transport due to the mortality of fish depends on the chemical composition of
the fish, the rate of mortality, and location of mortality. For the single-fish model, it
is assumed to be evenly distributed over the diel cycle. This is likely to lead to
conservative results, since DVM is thought to be a visual predation avoidance strategy
(Angel, 1985; Robison, 2003). Annual myctophid mortality from deeper-living
stomiid fishes has been calculated to be 89% (Sutton and Hopkins, 1996).
Mesopelagic fish production has not been found to be a large source of energy for
major epipelagic predators such as tuna (Mann, 1982), although some cetaceans feed
predominantly on myctophids (Beamish et al., 1999). The daily instantaneous
mortality rate (M d-1) can be estimated from an expression for the weight-specific
mortality of fish shown in Equation 5 and Figure 5 (Peterson and Wroblewski, 1984).
M = 5.3*10-3*(dry weight)-0.25 d-1
(5)
This relationship was derived from biomass size spectra. Mortality estimates for
several species of midwater fish have been published. Some of these are also
displayed in Figure 5b. Chemical composition has been measured for myctophids,
with the relevant results shown in Table 4 (Childress and Nygaard, 1973). For these
assumptions and a 1-g fish, M is 0.007 d-1 and carbon flux is 1.2 mgC d-1 fish-1.
Single-fish Model:
The physiological and ecological rates discussed above are all combined into a singlefish model of daily carbon transport. The model is constructed as arrays with depth
rows and time columns (Figure 6). Location of the fish is used as a mask. Note that
when the model is expanded to world-wide coverage, temperature is interpolated
from the World Ocean Atlas (Locarini et al., 2006) and the single fish is multiplied by
fish biomass (Gjosaeter and Kawaguchi, 1980). For a 1-g fish and nominal parameter
values as described above, the modeled flux components (mgC d-1) are as follows:
10
respiration 0.7, defecation 0.6, and mortality 0.7. Parameters and equations for the
single-fish model are summarized in Table 4.
Table 4: Parameters and equations in the single-fish model.
Parameter or variable
Ww: fish wet weight
Wd: fish dry weight
Waf: fish ash-free dry
weight
fish carbon content
DR: daily ration
Cz: zooplankton carbon to
dry weight ratio
RQ: Respiratory Quotient
SMR
a: regression constant
b: regression constant
c: regression constant
RMR
carbon respiration
night to day length ratio
Db: DSL day depth
Dt: night depth
Tt: top temperature
Tb: bottom temperature
temperature (linear
between Tt and Tb)
flux depth boundary
DVM speed
AE: carbon assimilation
efficiency
gut-passage time
M: instantaneous mortality
daily mortality fraction
defecation rate
Cexp defecation
Cexp mortality
Cexp respiration
value or expression
1g
0.313*Ww g
0.9045*Wd g
source
0.58175*Waf g
0.03*Wd g dry weight
0.4583
(Childress and Nygaard, 1973)
(Mann, 1982)
0.85
a*(Ww)b*ecT mgO2 h-1
0.01 (SE 0.01)
0.72 (SE 0.13)
0.19 (SE 0.02)
4.1*SMR
RMR*RQ*(12/32) mgC h-1
1
400m
50m
15 °C
5 °C
((Tb - Tt)*depth + Tt*Db Tb*Dt)/(Db - Dt)
75m
5 cm s-1
0.857
10 h
6.1*10-8*(Wd)-0.25 s-1
1-e-M*60*60*24
DR*Cz*(1-AE)*(feeding
duration)-1 gC h-1
 (def. below boundary)
 (mort. below boundary)
 (resp. below boundary)
11
(Childress and Nygaard, 1973)
(Childress and Nygaard, 1973)
(Enders et al., 2006)
(Enders et al., 2006)
(Enders et al., 2006)
(Enders et al., 2006)
(Enders et al., 2006)
(Ashjian et al., 2002)
(Peterson and Wroblewski, 1984)
-1
b
instantaneous mortality rate (y )
a
1
10
0
10
-1
10 -1
10
0
1
2
10
10
dry weight (g)
10
Figure 5: a) Weight-specific fish mortality relationship (Peterson and Wroblewski,
1984). b) measurements of myctophid mortality rate from other sources
(Beamish et al., 1999; Gjosaeter, 1973, 1984; Gjosaeter and Kawaguchi, 1980;
Young et al., 1988). The line is the same expression shown in Figure 5a.
Where length-weight distributions and water content were not specified,
values from other sources (congeners, if necessary) were used (Childress and
Nygaard, 1973; Childress et al., 1980; Filin, 1995; Pakhomov et al., 1996).
feeding
start/stop
position
C_flux d-1
C_defecation
temperature
C_respiration
mortality
C_mortality
24 hours


depth
defecation
start/stop
Figure 6: Single-fish carbon transport model
In order to assess the sensitivity of the single-fish model to the parameter values,
these values were modified one at a time within reasonable limits. The impact to the
total flux is shown in Table 5. The results indicate strong temperature dependence as
well as a great sensitivity to biomass sampling efficiency.
12
Table 5: Fish transport model sensitivity analysis (nominal settings are yellow)
Modeled Transport in mgCd-1g-1 wet weight
parameter
surface temperature
units
low
C
0
high
low
5
medium
15
20
30
1.682
1.727
medium
1.971
2.308
4.603
high
deep temperature
C
0
5
10
15
20
1.668
1.971
2.670
4.452
8.487
assimilation efficiency
%
70
75
80
85
90
2.634
2.423
2.212
1.971
1.789
% CO2 O2-1
cm s-1
70
75
80
85
90
1.846
1.888
1.929
1.971
2.013
2.5
5
7.5
10
15
2.362
1.971
1.811
1.734
1.655
respiratory quotient
swimming velocity
daytime depth
m
200
400
600
800
1000
0.517
1.971
2.219
2.463
2.707
fish wet weight
g
0.25
0.5
1
2
4
0.645
1.124
1.971
3.483
6.201
length of night
h
6
9
12
15
18
2.398
2.190
1.971
1.643
1.307
daily ration
% dry weight
1
2
3
4
5
1.568
1.770
1.971
2.172
2.374
gut passage time
mortality constant a (M=aWb)
h
4
8
10
16
20
1.604
1.849
1.971
1.848
1.785
-
5.30E-08
5.70E-08
6.10E-08
6.50E-08
6.90E-08
1.885
1.928
1.971
2.014
2.058
mortality constant b (M=aWb)
% fish vol-1
-0.15
-0.2
-0.25
-0.3
-0.35
1.899
1.934
1.971
2.011
2.052
5
15
50
80
100
39.420
13.140
3.942
2.464
1.971
SMR regression constant a
-
0.006
0.008
0.010
0.012
0.014
1.688
1.830
1.971
2.112
2.254
SMR regression constant b
-
0.5
0.6
0.72
0.8
0.9
1.971
1.971
1.971
1.971
1.971
SMR regression constant c
-
0.15
0.17
0.19
0.21
0.23
1.775
1.863
1.971
2.103
2.265
RMR to SMR ratio
-
3
3.5
4.1
4.5
5
1.781
1.868
1.971
2.040
2.126
capture efficiency
13
Part 2. Measurement of Biomass:
Biomass is a major source of uncertainty in the calculation of active transport. The
number of animals, their size distribution, and the division between migrating and
non-migrating portions of the biomass are all needed for the carbon flux calculation.
Non-migrating fish contribute to the flux indirectly by inflicting mortality on
migrating zooplankton and fish. There are many measurements of mesopelagic fish
biomass in the literature from all over the world, but they are very difficult to
compare. The difficulty lies in the diverse equipment used to make the measurements
and the differences between sampling efficiencies. Sampling efficiency is the ratio of
the measured biomass to the true biomass. Sampling efficiency is the most sensitive
parameter in the single-fish carbon transport model.
Mesopelagic fish biomass is usually measured using net collections, sonar, or a
combination of the two. Both of these methods have potentially serious biases when
used in isolation. A large source of bias in estimates of biomass made using data from
trawling is avoidance of the net mouth and escapement or extrusion of the fish
through the mesh. The ratio of observed to true biomass has been estimated to be 420% for large commercial nets, using simultaneously collected sonar data (Gjosaeter,
1984; Koslow et al., 1997; May and Blaber, 1989). Capture efficiency of nets is
thought to be different for different net designs, different sizes of fish, and fish of
different avoidance capabilities. Many of the existing regional biomass estimates have
been made using data collected with the Isaacs-Kidd Midwater Trawl (IKMT) (Isaacs
and Kidd, 1953) for which the capture efficiency is unknown.
The principle of using sonar to measure fish biomass is that each fish will reflect
acoustic energy in proportion to its backscattering cross-section bs. The decibel form
of bs is referred to as the Target Strength (TS). The intensity of reflected signals is
measured over a series of depth ranges corresponding to the time delay between
transmission and receipt of the ping. The measured backscattered intensity Ib is
corrected for energy losses due to beam spreading and absorption, and then divided
by the ensonified volume to become the “volume backscattering coefficient” sv. The
decibel form of this measure is the “volume backscattering strength” Sv. This is what
is shown in Figure 2b. Integration of sv through the water column gives the “area
backscattering coefficient” sa, a measure of the reflected acoustic energy per m2 of
surface area (Simmonds and MacLennan, 2005). This is what is plotted in Figure 2a.
The relationships between these variables are shown in Equations 6-10. In the
equations below, R is the distance traveled by the acoustic energy, R2 is the beam
spreading correction factor,  is the absorption coefficient, eR is the absorption
correction factor, and V is the ensonified volume.
bs = [(Ib at 1 m)/Ii]*R2*eR
(6)
14
TS = 10*log10(bs)
(7)
sv = bs/V
(8)
Sv = 10*log10(sv)
(9)
sa = sv
(10)
Sonar biomass surveys of small, densely packed fish (defined by multiple fish present
in the beam at once) can be made using a technique called “echo integration”. For
uniform targets, sa is divided by the individual bs to derive the number of fish m-2.
More commonly, ensonified animals will have a variety of target strengths. The
distribution of target strengths can be sampled using another technique, “echo
counting”, which detects and records the target strength from individual reflectors.
The concentration of different targets can now be deconvolved from the intensity
data. The major drawback to sonar measurements is that it is difficult to assign targets
to known taxonomic groups with much confidence. The problem of assigning sonar
measurements to particular animal taxa is usually solved by either making
measurements in high dominance ecosystems or by collecting concurrent trawl
samples. A critical component of sonar intensity interpretation is the target strength
of the ensonified fish. The intensity of the reflection is proportional to the size of the
object and its reflectance properties. For fish with a swim bladder, the bladder
dominates the sonic reflections due to the large density difference between the gas
inclusion and the body of the fish. The main function of a swim bladder is to provide
buoyancy to the fish. Bioenergetic calculations suggest that fish performing a DVM
of more that 200m would save energy allowing the swim bladder to deflate during
descent (constant mass strategy) rather than maintaining a constant volume of gas in
the swim bladder over the change in depth (Alexander, 1972). In other words,
generation of lift by swimming takes less energy than daily inflation of the swim
bladder at mesopelagic pressures. If constant mass strategy is used by mesopelagic
fish, it would be observed as a decrease in fish target strength with depth.
Preliminary data to test this prediction comes from ADCP data collected during a
2005 R/V Revelle Pacific transect. Integration of volume scattering over the water
column indicates a higher biomass at night, when the DSL is near the surface, than
during the day, when the DSL is at depth. This is clearly displayed in Figure 2.
Direct evidence of this phenomenon would greatly benefit interpretation of sonar
data.
15
Part 3. Extension to Global Carbon Cycle:
In order to estimate global carbon transport performed by mesopelagic fishes,
knowledge is required of how the parameters in the single-fish model vary globally.
These parameters include temperature, biomass, community composition, and
metabolic rate. Although mesopelagic fish biomass has been measured in many
places, large areas of the ocean have not been well sampled. A comprehensive
summary of world mesopelagic fish biomass distribution (Gjosaeter and Kawaguchi,
1980) is shown in Figure 7. A few of the regional estimates were based on
measurements made with modern sonar techniques, and they show biomass values up
to ten times larger than the values based solely upon trawling catches. Global
temperature data are available by depth from the World Ocean Atlas (Locarini et al.,
2006). Figure 8 shows the temperature at 400m, a typical depth of the DSL.
g m-2
80
6
60
5
latitude
40
4
20
0
3
-20
2
-40
-60
1
-80
0
60
120
180
longitude
-120
-60
0
Figure 7: World mesopelagic fish biomass distribution (Gjosaeter and Kawaguchi,
1980). Regions measured with acoustics were truncated at 6 g m-2 for clarity
of display.
16
20
70
50
15
latitude
30
10
10
-10
-30
5
-50
-70
0
30
60
90
120
150
180 -150 -120
longitude
-90
-60
-30
Figure 8: Ocean temperature at 400m (°C) (Locarini et al., 2006)
The global biomass distribution, global ocean temperature map, and single-fish model
have been combined into an array with 1° latitude and longitude resolution as
pictured in Figure 9. Capture efficiency is conservatively assumed to be 100%.
Biomass density is assumed to be a multiple of 1-g fish (2.5 g m-2 is treated as 2.5 1-g
fish m-2). The proportion of migrating biomass is also assumed to be 100%, since nonmigration has not been incorporated into the single-fish model yet. Results are
plotted in Figure 10. The overall flux is 1.3 PgC y-1 with components as follows (PgC
y-1): respiration 0.9, defecation 0.2, and mortality 0.2. This is the value reported in Table
1 for comparison with other forms of carbon transport. The largest sources of uncertainty
in this calculation are the biomass values (capture efficiency, proportion of biomass
participating in DVM) followed by the physiological and ecological rate estimates.
Accepting the caveats of uncertainty, the overall flux is clearly large in comparison with
zooplankton active transport, and more than 10% of the passive flux.
C_defecation
temperature
C_respiration
one-fish
longitude
C_mortality
longitude
Figure 9: Globally expanded single-fish model
17
latitude
biomass
latitude

C_flux y-1
mgC m-2 d-1
50
80
60
40
latitude
40
20
30
0
-20
20
-40
10
-60
-80
0
60
120
180
longitude
-120
-60
0
Figure 10: Carbon flux mediated by mesopelagic fish. Regions measured with
acoustics were truncated at 6 g m-2 and the Red Sea temperature was
reduced by 4.4 °C for clarity of display.
Hypotheses:
1) Vertical Carbon transport mediated by mesopelagic fishes is comparable in
magnitude to the flux from diel migrating zooplankton and passive transport.
2) Zoogeographic regions with high fish biomass and high mesopelagic temperatures
will contribute the most to active transport of carbon.
Proposed Research:
I plan to make the best possible estimate of carbon transport mediated by mesopelagic
fish by performing research in the two major areas of uncertainty in the calculation;
physiological and ecological rates, and biomass. The results of this research would be
used to create a global distribution of the carbon flux by mesopelagic fish. I currently
do not have scheduled ship time or funding, so future cruises remain speculative. I
will be submitting (through Dr. Checkley) a UC Ship Funds application and an NSF
proposal in June and August, respectively, to address these needs. I also plan to
participate in the 2008 and 2009 CCE LTER process cruises, if possible.
18
Part 1. Physiological and Ecological Rates:
Respiration:
I propose to investigate the hypothesis that respiration by fish in changing water
temperature can be predicted from measurements made at a constant temperature.
The delay between feeding and SDA is important, as it may maintain elevated
metabolic rates during the day when many mesopelagic fish are inactive and at depth.
Oxygen consumption would be measured on starved fish at constant water
temperature. Some of the fish would be fed at the start of the experiment. This
experiment would be repeated for at least one other constant temperature and for a
temperature that changed over a 24 hour period. The constant temperature
experiments would establish the respiration rates, and the cycled temperature
experiment would test the hypothesis. These experiments would have to be
performed on a proxy fish species that is able to be kept in captivity and is tolerant of
temperature variation. Migrating mesopelagic fish have been shown to be more
similar in physiology and composition to epipelagic fish than to bathypelagic fish
(Childress et al., 1980), which would add support to the use of a proxy. Additional
shipboard respiration measurements would be collected on fresh mesopelagic
specimens in good condition.
Defecation:
I plan to incorporate temperature-dependent ingestion and defecation rates into the
single-fish transport model. The temperature dependence of these rates is difficult to
measure using wild fish. Temperature dependence of stomach evacuation rate has
been measured on captive salmon (Brett and Higgs, 1970), and this rate can be applied
to mesopelagic fish as an approximation of the defecation rate. Several estimates of
mesopelagic fish stomach evacuation rate and ingestion rate exist in the literature
(Pakhomov et al., 1996; Williams et al., 2001). I will supplement these data with my
own measurements to test the hypothesized temperature relationship of the rates.
Normalized stomach fullness S (mg g-1 dry weight) will be measured on the dominant
fish species over the course of a 24-hour cycle. A regression of the log-transformed
stomach fullness will allow the instantaneous rate of stomach evacuation R (h-1) to be
calculated using Equation 11 (Eggers, 1977). Daily food ingestion I (mg g-1 dry
weight) can be calculated from the mean stomach fullness using Equation 12 (Eggers,
1977). Future cruises will provide more data.
R = -ln(S/S0)/t
(11)
I = 24*Smean*R
(12)
19
Mortality:
I propose to measure mortality rates of a series of mesopelagic fish of different sizes in
order to test the size-dependent mortality hypothesis (Equation 5). Existing mortality
rates of mesopelagic fish from the literature would be incorporated into the
experiment. Mortality can be estimated by analyzing otolith rings. Annual opaque
and translucent rings are formed in the sagittal otoliths, and these can be used to age
the fish (Childress et al., 1980). The instantaneous mortality rate M (y-1) can be
calculated from a regression of the log-transformed age structure of the population
sample per Equation 13. Analysis of specimens from future cruises preserved with
ethanol or by freezing on future cruises will augment the mortality rate data
available. Formalin damages the otoliths through acidity.
M = -ln(N/N0)/t
(13)
Part 2. Measurement of Biomass
It is desirable to have an independent estimate of mesopelagic biomass due to the
sensitivity of carbon transport models to this term and its uncertainty. I plan to
propose a model including mesopelagic fish biomass, annual net primary productivity,
and bathymetry. Figure 11 is a plot of annual primary productivity from 2003.
Figure 11: 2003 annual net primary productivity from MODIS and SeaWiFS satellite
data (Behrenfeld and Falkowski, 1997)
Sampling transects at a variety of places (eastern and western boundary currents, midocean islands, high and low latitudes) would be required, necessitating the
incorporation of applicable data from the literature (Gjosaeter and Kawaguchi, 1980;
Pearcy, 1976). I plan to collect as many of my own samples as possible, and also to
make use of existing IKMT and Multiple Opening/Closing Net and Environmental
Sensing System (MOCNESS) (Wiebe et al., 1976) trawl data from the literature by
calculating the capture efficiency of the two nets. Simultaneous trawling and sonar
measurements of fish populations will provide the data required to compute the
conversion factor C to correct existing biomass B (g m-2) estimates collected using that
20
type of net to the standard set by the combined acoustic and trawl measurement. A
size-dependent relationship will be estimated (Equation 14, for size classes i).
B =  Ci*(BIKMT)i
(14)
Sonar target strength measurements will be made in situ using a low frequency splitbeam transducer. Volume scattering measurements will be made using a hullmounted transducer at the same frequency. If possible, multiple frequencies will be
used in order to distinguish reflectors of different sizes. The sonar volume scattering
data will be interpreted based upon the species composition of the captured animals
and on target strength distributions from the split beam sonar. The comparison
would be easiest in ecosystems with low diversity and high biomass, such as the
Arabian Sea (Gjosaeter, 1984). This will simplify the target strength distributions,
ensure a large enough sample size, and allow a more robust statistical comparison.
Additional efficiency measurements will be made on future cruises with a calibrated
sonar and midwater trawls. Other existing data are available from the 2007 CCE
LTER process cruise, where concurrent samples were taken using a 1m2 MOCNESS
and a high-speed 5m2 Oozeki midwater trawl (Oozeki et al., 2004). I plan to calculate
the capture efficiency of the MOCNESS relative to the Oozeki trawl. The lack of
appropriate sonar data from this cruise prevents calculation of a correction factor to
the best possible estimate of biomass.
The hypothesis that target strength varies with depth can be tested concurrently with
the capture efficiency and biomass experiments by mounting a small sonar onto a
mesopelagic trawl. The trawl would be towed near the surface at night and in the
DSL during the day. Comparison of the day-night target strength distributions would
allow one to estimate the change in target strength. The net contents would confirm
that the sonar was measuring the target strength of the same species at each depth.
Part 3. Extension to Global Carbon Cycle:
I will combine the physiological rates, ecological rates, biomass compensated for
capture efficiency, and temperature distribution discussed above with an array
version of the single-fish model (Figure 9) in order to calculate carbon transport
mediated by mesopelagic fish spatially and globally. The fraction of fish biomass
performing DVM will be incorporated.
Preliminary Results:
Preliminary results primarily consist of the single-fish modeling discussed in the
Background section, and its application to the global biomass and temperature
21
distributions. In addition, a total of thirteen samples were collected from three
stations of the 2007 CCE LTER process cruise (Figure 12).
35oN
bathymetry lines at 200, 500, 1000, 2000, 3000, and 4000m
30'
34oN
Cycle 4
30'
Cycle 2
Cycle 0
33o
N
30'
32oN
124oW
123oW
122oW
121oW
120oW
119oW
o
118 W
o
117 W
Figure 12: April 2007 CCE LTER process cruise stations
As yet, only three of the samples have been processed beyond the preservation stage.
The relative biomass comparison is shown below in Figure 13. Cycle 0 consisted of a
single oblique trawl. This sample was preserved in ethanol, which will allow aging of
the dominant fish Triphoturus mexicanus by otolith analysis. Aging of the sample
will allow calculation of the instantaneous mortality rate using Equation 13. Figures
14 and 15 show the length-weight relationship and the length histogram respectively.
Cycles 2 and 4 included six trawls, each over various periods within the diel cycle.
These samples were preserved in buffered formalin and then transferred to 50%
isopropanol. Samples from Cycles 2 and 4 will be used to measure stomach fullness
over the diel cycle, from which ingestion and evacuation rates can be determined
using Equations 11 and 12. Dr. Ohman allowed me to remove the fish from the
fifteen MOCNESS samples taken during the cruise. Comparison between biomass
measurements from the Oozeki trawl and MOCNESS will allow calculation of the
capture efficiency of the MOCNESS relative to the Oozeki trawl.
22
6
fish biomass (g m-2 )
5
Cycle 4
8 :1 8
4
0 -5 0 0 m
3
Cycle 2
2
1
Cycle 0
1 3 :1 0
2 0 :3 0
0 -5 0 0 m
0 -5 5 0 m
0
0
500
1000
1500
2000
2500
3000
3500
4000
4500
bottom depth (m)
Figure 13: Relation between mesopelagic fish biomass and bathymetry from three
2007 CCE LTER process cruise Oozeki trawls. Biomass was calculated by
dividing the catch by the volume filtered and multiplying by the trawl
depth. Cycle number, starting time, and depth of the trawl are marked.
2.50
2.00
y = 3E-07x3.7914
mass (g)
2
R = 0.984
1.50
1.00
0.50
0.00
20
30
40
50
60
70
standard length (mm)
Figure 14: Length-weight relationship for 169 Triphoturus mexicanus collected at
Cycle 0 during the 2007 CCE LTER Process Cruise
23
10
fish
8
6
4
2
0
20
30
40
50
standard length (mm)
60
70
Figure 15: Length histogram for 169 Triphoturus mexicanus collected at Cycle 0
during the 2007 CCE LTER Process Cruise. There is an apparent cohort
structure. Older cohorts probably overlap in the 37-65mm portion of the
range. This species is known to live five years in the CA Current
(Childress et al., 1980).
Conclusion:
The biological pump is one of the main controls of the atmospheric-oceanic flux of
CO2, which is a major sequestration path for anthropogenic emissions (Sarmiento and
Siegenthaler, 1992; Treguer et al., 2003). Nearly all calculations of the export flux of
carbon today are based upon passive transport (Falkowski et al., 2003). This neglects
the contribution of active transport. Measurements of the passive transport flux at
the base of the euphotic zone using sediment traps are known to underestimate
export in relation to other methods (Buesseler, 1998; Usbeck et al., 2003). Active
transport across this depth horizon provides a mechanism for the bias. It is known
that the atmosphere-to-ocean CO2 flux varies strongly by region (Takahashi et al.,
2002; Watson and Orr, 2003). Flux to the deeper waters is also known to be
regionally dependent on ecological factors and to be decoupled from surface primary
productivity (Treguer et al., 2003). A better understanding of active transport and its
regional variation will help reduce uncertainty in our knowledge of local differences
in the air-sea flux (Sarmiento and Siegenthaler, 1992). The contribution of migrating
animals to the active transport is poorly known, but preliminary calculations and data
24
suggest that it is both large and variable. Quantification of active transport requires a
better understanding of the distribution of biomass and the temperature dependence
of ecological and physiological rates. Improved knowledge of this flux and its spatial
variation will provide basic data required to build future generations of carbon cycle
models. These models are essential to our ability to predict the response of the Earth
system to changing levels of anthropogenic CO2 (Doney et al., 2003).
Proposed Publications:
First priority:
1) Single-fish carbon transport model and carbon transport in the CA current
(including evacuation and ingestion rates work, plus MOCNESS and Oozeki capture
efficiencies).
2) Relationship between NPP, bathymetry, and mesopelagic fish biomass. Revised
global estimate of mesopelagic fish biomass, worldfish model and global carbon
transport.
If possible:
3) Influence of diel temperature cycling on SDA and overall respiration.
4) Depth-dependent target strength of myctophids in DSL.
5) Antarctic mesopelagic fish biomass estimate from sonar and IKMT data, capture
efficiency of the IKMT.
25
26
2009
defend dissertation
2009 LTER Cruise?
2008
Biomass cruise 2?
2008 LTER Cruise?
2007
Biomass cruise 1?
2007 LTER Cruise
Qualifying Exam
UC ship funds app.
NSF prop.
Schedule:
mortality, evacuation, ingestion rates
capture efficiency, biomass
respiration, SDA
global model
write dissertation
2010
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