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Deep-Sea Research II
journal homepage: www.elsevier.com/locate/dsr2
The potential impact of ocean acidification upon eggs and larvae
of yellowfin tuna (Thunnus albacares)
Don Bromhead a,n, Vernon Scholey b, Simon Nicol a, Daniel Margulies b, Jeanne Wexler b,
Maria Stein b, Simon Hoyle a, Cleridy Lennert-Cody b, Jane Williamson c,
Jonathan Havenhand d, Tatiana Ilyina e, Patrick Lehodey f
a
Secretariat of the Pacific Community, BP D5 Noumea, New Caledonia
Inter-American Tropical Tuna Commission, 8901 La Jolla Shores Drive, La Jolla, CA 92037-1509, USA
c
Department of Biological Sciences, Macquarie University, NSW 2109, Australia
d
Department of Biological and Environmental Sciences, University of Gothenburg, SE-452 96 Stromstad, Sweden
e
Max Planck Institute for Meteorology, Bundesstr. 53, D-20146 Hamburg, Germany
f
Collecte Localisation Satellites, Space Oceanography Division, 8-10 rue Hermes, 31520 Ramonville Saint-Agne, France
b
art ic l e i nf o
Keywords:
Ocean acidification
Carbon dioxide
Tuna fisheries
Mortality
Growth
a b s t r a c t
Anthropogenic carbon dioxide (CO2) emissions are resulting in increasing absorption of CO2 by the
earth's oceans, which has led to a decline in ocean pH, a process known as ocean acidification (OA).
Evidence suggests that OA may have the potential to affect the distribution and population dynamics of
many marine organisms. Early life history processes (e.g. fertilization) and stages (eggs, larvae, juveniles)
may be relatively more vulnerable to potential OA impacts, with implications for recruitment in marine
populations. The potential impact of OA upon tuna populations has not been investigated, although tuna
are key components of pelagic ecosystems and, in the Pacific Ocean, form the basis of one of the largest
and most valuable fisheries in the world. This paper reviews current knowledge of potential OA impacts
on fish and presents results from a pilot study investigating how OA may affect eggs and larvae of
yellowfin tuna, Thunnus albacares. Two separate trials were conducted to test the impact of pCO2 on
yellowfin egg stage duration, larval growth and survival. The pCO2 levels tested ranged from present day
( 400 μatm) to levels predicted to occur in some areas of the spawning habitat within the next 100
years ( o2500 μatm) to 300 years ( o5000 μatm) to much more extreme levels ( 10,000 μatm).
In trial 1, there was evidence for significantly reduced larval survival (at mean pCO2 levelsZ4730 μatm) and
growth (at mean pCO2 levelsZ2108 μatm), while egg hatch time was increased at extreme pCO2
levelsZ10,000 μatm (nintermediate levels were not tested). In trial 2, egg hatch times were increased at
mean pCO2 levelsZ1573 μatm, but growth was only impacted at higher pCO2 (Z8800 μatm) and there was
no relationship with survival. Unstable ambient conditions during trial 2 are likely to have contributed to the
difference in results between trials. Despite the technical challenges with these experiments, there is a need
for future empirical work which can in turn support modeling-based approaches to assess how OA will affect
the ecologically and economically important tropical tuna resources.
& 2014 Elsevier Ltd. All rights reserved.
1. Introduction
1.1. Background
Anthropogenic (man-made) carbon dioxide (CO2) emissions are
resulting in increasing concentrations of CO2 in the earth’s atmosphere (IPCC, 2007). This buildup in atmospheric CO2 is, in turn,
causing a gradual warming and acidification of the earth’s oceans
n
Corresponding author. Mob.: þ61479119142.
E-mail address: [email protected] (D. Bromhead).
(e.g. Caldeira and Wickett, 2003; Feely et al., 2004; Barnett et al.,
2005). Both warming and acidification have the potential to affect
the distribution and population dynamics of many marine organisms (Raven et al., 2005; Fabry et al., 2008). Tuna populations are
key components of pelagic ecosystems and, in the Pacific Ocean,
form the basis of one of the largest and most valuable fisheries in
the world (Williams and Terawasi, 2009). The sustainable management of this fishery requires an understanding of not only fishery
impacts, but impacts of other factors upon population biomass and
structure over time. While fishery scientists are now attempting to
predict how ocean warming will affect Pacific tuna populations
(Lehodey et al., 2010, 2013), no one has previously investigated
http://dx.doi.org/10.1016/j.dsr2.2014.03.019
0967-0645/& 2014 Elsevier Ltd. All rights reserved.
Please cite this article as: Bromhead, D., et al., The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna
(Thunnus albacares). Deep-Sea Res. II (2014), http://dx.doi.org/10.1016/j.dsr2.2014.03.019i
D. Bromhead et al. / Deep-Sea Research II ∎ (∎∎∎∎) ∎∎∎–∎∎∎
2
how ocean acidification (OA) may affect these species and associated fisheries.
1.2. The process of ocean acidification
Concentrations of CO2 in the ocean tend towards equilibrium with
CO2 in the atmosphere. To date, the world’s oceans have absorbed
about 30–50% of global man-made CO2 emissions (Feely et al., 2004;
Sabine et al., 2004), substantially changing ocean water chemistry by
increasing concentrations of dissolved CO2 (aq), H2CO3 (carbonic
acid), HCO3 (bicarbonate ions), and H þ (hydrogen ions), and
decreasing concentrations of CO3 2 carbonate ions (Fabry et al.,
2008). Increased concentrations of oceanic CO2 (i.e. increased pCO2)
have lowered the average sea-surface pH by 0.1 units (i.e., making the
ocean more acidic and less alkaline) since the start of the industrial
revolution. It is projected that uptake of atmospheric CO2 by global
oceans will further reduce average sea-surface pH by
0.3–0.4 units in 2100 and up to 0.7 units by the year 2300
(Caldeira and Wickett, 2003, 2005). These represent larger and faster
shifts in oceanic pH than is believed to have occurred in millions of
years (Feely et al., 2004). These model based projections of increasing
acidification are supported by recent time series of ocean pH and
pCO2 off Hawaii in the Pacific Ocean and Bermuda in the Atlantic
Ocean (Doney et al., 2009; Bates et al., 2012).
These predictions describe global mean changes in surface waters,
but pH and pCO2 concentrations show spatial heterogeneity both
between oceanic regions (surface waters) (Fig. 1) and throughout the
water column (Fig. 2). Currently, surface layer pH values are lowest in
higher latitudes and areas where regular upwelling brings subsurface
waters with lower pH to the surface, including in the eastern and
central tropical Pacific (Fig. 2). Although seawater pH is expected to
decrease globally (and pCO2 to increase), the rate of this change is
predicted to be greater in high latitudes, and lower in tropical and
subtropical waters (Fig. 1). The degree of change is also dependent on
future anthropogenic CO2 emissions and whether these are higher or
lower than “business as normal” IPCC projections, and may be
impacted by other predicted consequences of climate change, such
as changed ocean circulation patterns (IPCC, 2011).
1.3. Impacts on fish
OA is recognized as having the potential to impact marine
organisms via direct physical or physiological impacts upon individuals which then propagate to the population level, or indirect
ecological impacts through impacts upon habitat and interacting
species (e.g. predators, prey, competitors, symbionts, parasites).
Evidence for direct impacts of OA upon key population processes
(e.g. calcification, growth, development, mortality, reproduction,
activity levels) has been presented for many marine organisms
including phytoplankton and zooplankton, marine calcifiers such as
corals, molluscs, crustaceans, echinoderms, and cephalopods as well
as fish (see reviews by Raven et al., 2005; Fabry et al., 2008; Wittman
and Portner, 2013).
The direct influence of pH upon fish has been studied for
decades (e.g. see Brauner and Baker, 2009), although early studies
typically focused on adults and extreme pH levels. It is only more
recently that research has focused on early life stages of fish and
their physiological responses to pH and pCO2 levels that are
ecologically relevant to those forecast to occur within the next
100–300 years. While many of the original OA studies focused on
reef species, a range of non-reef species have now been considered
(e.g. Frommel et al., 2010, 2011; Bignami et al., 2013a, 2013b;
Sundin et al., 2013; Tseng et al., 2013), but not tuna species.
Adult fish within the species studied to date generally show a
strong capacity to compensate for prolonged exposure to elevated
pCO2 due to their ability to control acid–base balance by bicarbonate
buffering across the gills and to a more limited degree, via the
kidneys (Brauner and Baker, 2009; Esbaugh et al., 2012). The early life
stages of fish may be more vulnerable to OA impacts than are adult
fish, due to different modes of respiration and ion exchange (Jonz
and Nurse, 2006; Pelster, 2008); a situation possibly exacerbated by
their higher surface to volume ratio (Kikkawa et al., 2003; Ishimatsu
et al., 2004). Exposure to elevated pCO2 has been found to adversely
affect embryonic development (Tseng et al., 2013), larval and juvenile
growth (Baumann et al., 2011), tissue/organ health (Frommel et al.,
2011), and survival (Baumann et al., 2011) in some species of fish.
However a number of studies have not detected any effect upon
embryogenesis (Franke and Clemmesen, 2011), hatching (Frommel et
al., 2012), growth and development (Munday et al., 2011a; Frommel
et al., 2011, 2012; Hurst et al., 2012, 2013; Bignami et al., 2013a) or
swimming ability (Bignami et al., 2013a; Munday et al., 2009b).
Otolith growth appears to increase in size and/or density in some
species (Checkley et al., 2009; Munday et al., 2011b; Hurst et al.,
2012; Bignami et al., 2013a, 2013b) possibly due to increased internal
HCO2 concentrations that result from pH regulation (Esbaugh et al.,
2012) with possible implications for auditory sensitivity (Bignami et
al., 2013b). However, some species of fish have not shown increased
otolith growth (Franke and Clemmesen, 2011; Munday et al., 2011a;
Fig. 1. Projections of OA in CMIP5 simulations, calculated with the model MPI-ESM (Source: Ilyina et al., 2013; see also Bopp et al., 2013) for changes in pH in surface waters
from (A) 1850 to 2010 and (B) 1850 to 2100 (following the “business as usual” scenario RCP8.5 as described in Van Vuuren et al. (2011) in which radiative forcing increases up
to 8.5 W/m2 in the year 2100).
Please cite this article as: Bromhead, D., et al., The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna
(Thunnus albacares). Deep-Sea Res. II (2014), http://dx.doi.org/10.1016/j.dsr2.2014.03.019i
D. Bromhead et al. / Deep-Sea Research II ∎ (∎∎∎∎) ∎∎∎–∎∎∎
3
Fig. 2. Average present day pH in the global ocean surface waters and at depths of 50 m, 100 m and 500 m .
(Source: Ilyina et al., 2013)
Frommel et al., 2012). Further investigation is required given the
important role of otoliths in detecting sound, acceleration and body
position (Bignami et al., 2013b).
Many studies show no direct relationship between “near future”
levels of elevated pCO2 and survival (Munday et al., 2011a; Frommel et
al., 2012) and it is believed that for many species, sub-lethal effects of
OA may present the largest risk to individuals and populations (Briffa et
al., 2012). Somatic impacts such as reduced growth and size at age have
been linked to increased mortality in natural fish populations due to
increased risks of predation and reduced ability to find food
(e.g. Houde, 1989; Leggett and Deblois, 1994). Neurological and behavioral effects associated with elevated CO2 levels have also been
observed in coral reef fish (and other organisms; Briffa et al., 2012).
These include impaired mating propensity (Sundin et al., 2013),
impaired ability to make settlement choices (Munday et al., 2009c),
altered timing of settlement (Devine et al., 2012), impaired response to
predator and prey olfactory cues (Allen et al., 2013; Cripps et al., 2011;
Dixson et al., 2010; Nilsson et al., 2012), reduced escape distances (Allen
et al., 2013), altered responses to visual threats (Ferrari et al., 2012a) and
auditory signals (Simpson et al., 2011), behavioral lateralization
(Domenici et al., 2012; Nilsson et al., 2012) and a reduced capacity to
learn (Ferrari et al., 2012b). Many of these studies used larval or juvenile
fish. Such behavioral changes were recently linked in reef species to the
impact of elevated pCO2 upon a key brain neurotransmitter GABA-A
(Nilsson et al., 2012), with potential implications for other species given
the highly conserved nature of GABA-A across species.
Evidence suggests that the effects of OA will be species specific
and many key uncertainties remain regarding its implications for
fish populations, including:
How impacts on predators will affect prey mortality rates (e.g.
Allen et al., 2013),
Potential parental environment effects (e.g. Miller et al., 2012);
The effect of natural variability in pH (pCO2) in a species
environment upon its past evolution and resilience to future
acidification (e.g. Bignami et al., 2013a, 2013b; Franke and
Clemmesen, 2011; Frommel et al., 2012, 2011);
Individual variability in responses and species capacity to
rapidly adapt through natural selection (Munday et al., 2012);
Indirect ecological impacts on fish populations through impacts
upon habitat and interacting species populations (e.g. predators and prey). While logistically challenging, such studies have
been shown to be important in understanding pCO2 impacts
upon marine populations (e.g. Alsterberg et al., 2013) and
should be a focus of future fish research.
The potential impacts of OA upon commercially valuable tropical tunas are currently unknown. The only experiment known to
have tested impacts upon a tuna species (Eastern little tuna) tested
pCO2 levels that far exceeded those predicted using IPCC scenarios
(Kikkawa et al., 2003). In the remainder of this paper, we describe
the likely vulnerability of yellowfin tuna, and report the first set of
experiments exploring the impact of OA on the early life stages of
yellowfin tuna.
1.4. Potential vulnerability of tunas: Yellowfin tuna (Thunnus
albacares)
Yellowfin tuna (T. albacares) are epipelagic, serial batch spawners
which inhabit all tropical and subtropical oceans of the world
(Schaefer, 2001, 2009). The distribution and occurrence of yellowfin
tuna eggs and larvae may be largely determined by encounters with
favorable biological and physical conditions for growth and survival
(Boehlert and Mundy, 1994; Lang et al., 1994; Wexler et al., 2007,
Please cite this article as: Bromhead, D., et al., The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna
(Thunnus albacares). Deep-Sea Res. II (2014), http://dx.doi.org/10.1016/j.dsr2.2014.03.019i
D. Bromhead et al. / Deep-Sea Research II ∎ (∎∎∎∎) ∎∎∎–∎∎∎
4
2011), the physical structure of the water column and advective
water mass movements (Boehlert and Mundy, 1994; Sabatés et al.,
2007), and spawning location (Davis et al., 1990). Yellowfin larvae
inhabit subsurface waters within the mixed layer at depthso50 m
and exhibit diel vertical migration (Boehlert and Mundy, 1994; Lauth
and Olson, 1996; Owen, 1997). Suboptimal physical conditions (e.g.
low dissolved oxygen levels, low water temperatures) restrict the
vertical and horizontal distribution utilized by early life stages of
tunas and thereby influence the spatial and temporal overlap with
their food and predators, which could potentially affect subsequent
larval fish survival (Blaxter, 1992; Breitburg et al., 1999).
Pelagic species, such as tuna, are considered in general to have
evolved in a relatively more stable pH environment than coastal
and reef species, and this, in addition to low blood pCO2 levels that
can be expected to result from their high rates of gas exchange,
may make them more vulnerable to changes in ocean pCO2 and pH
(e.g. Nilsson et al., 2012). That said, yellowfin tuna spawn in
equatorial (tropical) and subtropical waters from the far western
Pacific Ocean to the far eastern Pacific Ocean, and in both near
coastal waters and oceanic waters (Schaefer, 2001). Across this
very broad region there is considerable seasonal and vertical/
horizontal spatial variation in pH (Fig. 2) and pCO2.
The 5th IPCC assessment (IPCC, 2013) estimates a global average
decline in surface pH of 0.3–0.32 by 2100 compared to today under
the high-CO2 climate change scenario RCP8.5, but there will be spatial
variation around global mean values, both between different ocean
regions and vertically through the water column (Bopp et al., 2013).
In the eastern tropical Pacific Ocean, surface water pH is on average
lower than in the western tropical Pacific and the pH at 50 m depth
(in the eastern Pacific) is on average 0.54pH units less than at the
surface. Across the region of yellowfin spawning habitat, mean surface
water pH is predicted to decrease between 0.26 and 0.49pH units by
2100 relative to pre-industrial levels (under the high CO2 scenario in
Fig. 1) (Ilyina et al., 2013). In the eastern Pacific Ocean the pH in surface
waters is predicted to decrease from 8.05 to about 7.73 while in the
western Pacific Ocean, the mean decline in pH is projected to be
0.40pH units (with a maximum predicted decline of 0.46) (Ilyina et al.,
2013). Decreases in pH at depths accessible to yellowfin larvae may be
even higher and could result in further “compression” of suitable
habitat area. It is unknown whether the level of variability across the
tropical Pacific has been sufficient to have conferred some evolved
resilience in tropical tunas to predicted future levels of OA.
To advance our knowledge of the impacts of OA upon tuna
populations and fisheries, a pilot study was undertaken at the InterAmerican Tropical Tuna Commission’s (IATTC’s) Achotines Laboratory
located in the Republic of Panama. The objectives of the pilot study
were to develop and test experimental protocols to examine the
potential effects of OA on yellowfin tuna egg fertilization, egg and larval
development, growth, and survival, and on the rapid selection of
resistant genotypes. The study also aimed to use results from these
trials to assess levels of variability in tank specific survival responses to
help design future experimental trials with sufficient statistical sampling power. This paper describes each of these elements, with the
exception of egg fertilization and genetic components. It also discusses
the results in the context of current implications for potential impacts
on yellowfin populations and implications for future empirical and
modeling based approaches that will be required to fully assess the risk
that OA places on tropical tuna populations and associated fisheries.
2. Methods
2.1. Physical system design
Two separate experimental trials were conducted at the Achotines Laboratory (Fig. 3), in October and November of 2011, where
Fig. 3. The location of the Achotines Laboratory.
a broodstock population of yellowfin tuna has spawned on a neardaily basis since 1996 (Wexler et al., 2003; Margulies et al., 2007a).
The experimental system was housed in a semi-enclosed
laboratory and consisted of fifteen 840-L experimental tanks, each
nested inside a larger 1100-L tank filled with seawater to stabilize
the water temperature (although this still varied slightly with local
ambient conditions). Each experimental tank was an individual
open system with seawater inflow of 1.7 L min 1. Appropriate
levels of turbulence (to facilitate aeration of eggs and larval
feeding) were achieved utilizing air diffusers delivering the CO2–
air gas mix into each experimental tank. Bleed valves were used as
necessary to ensure the turbulence levels in all 15 tanks were
similar. Key environmental parameters (water flow, lighting, aeration and turbulence levels) were set to standard levels used at
Achotines Laboratory and continuously fine-tuned to ensure these
parameters remained as uniform as possible.
Four pH treatment levels (6.9, 7.3, 7.7 and 8.1) were targeted in
each trial with three replicate tanks per treatment level. These
levels were chosen based on results from the ocean-carbon-cycle
models using the IPCC A1B Scenario (e.g., the Hamburg Ocean
Carbon Cycle model, as in Ilyina et al., 2009) and reference to
published studies on predicted pH levels (e.g. Fig. 1, Ilyina et al.,
2013; Caldeira and Wickett, 2003) and take into account spatial
variation in predicted declines, not just the global average predicted declines. Target pH levels 7.3–8.1 encompass potential
mean ocean pH levels estimated for the current oceans and
predicted for future oceans (to the year 2300).
The coastal waters supplying the Achotines Laboratory seawater system were close to pH 8.2 during the trial period so
ambient seawater was used for the high pH level with only air
used for aeration. The other three pH levels were maintained in
each set of replicate experimental tanks by varying the amount of
Please cite this article as: Bromhead, D., et al., The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna
(Thunnus albacares). Deep-Sea Res. II (2014), http://dx.doi.org/10.1016/j.dsr2.2014.03.019i
D. Bromhead et al. / Deep-Sea Research II ∎ (∎∎∎∎) ∎∎∎–∎∎∎
CO2 mixed with the air delivered through a mixing manifold for
each replicate distributing the gas mixture to air diffusers in each
tank. A Porter Cable C2002 air compressor delivered air through a
pressure regulator set at 2.1 bar (30 psi) to a precision electronic
gas-flow controller (Sierra 810 Series Mass-Trak) and then to the
mixing manifold. The air flow rate to each of the pH levels varied
from 2.3 to 6.9 lpm. The CO2 flowed from CO2 cylinders to a
pressure regulator set at 2.1 bar (30 psi) through an electronic gasflow controller and to the mixing manifold. The CO2 flow rate
varied from 0.04 to 0.34 lpm. The use of CO2 was critical to
modifying water chemistry (i.e., increasing carbonic acid, increasing H þ , lowering pH) in a manner consistent with CO2-induced
ocean acidification (Gattuso et al., 2010).
2.2. Water chemistry measurements and estimates
The pH was measured in each tank every 2–3 h during the
trials (Table 1). Measurements of pH (NBS scale) were taken using
a handheld Royce Model 503pH/CO2 Analyzer with matching
Royce pH probe, after checking initial readings for consistency
against two other pH meters (a handheld Hach HQ40d Portable
pH, Conductivity, Dissolved Oxygen Multi-Meter and a tabletop
Accumet XL25 Dual Channel pH/Ion Meter). New pH sensors were
used and calibrated using Hach pH calibration solutions. Temperature, salinity, and dissolved oxygen levels were recorded approximately every 8 h, and alkalinity every 2–3 days. Alkalinity was
measured with a LaMotte Alkalinity chemical test kit Model WATDR. The pCO2 (and a range of other carbonate system parameters)
was estimated by inputting measured levels of pH, temperature,
salinity and alkalinity into the Excel based CO2SYS macro (http://
cdiac.ornl.gov/oceans/co2rprt.html; Lewis and Wallace, 1998).
Means and 95% confidence intervals for pH (measured directly
in tanks) and pCO2 (estimated) are provided in Table 1. Means and
95% confidence intervals for water quality variables (temperature,
dissolved oxygen, % oxygen saturation, salinity and alkalinity) and
estimated carbonate chemistry variables are provided in the
Appendix. Variation within and between tanks for non-carbonate
variables (e.g. temperature, salinity and oxygen) was low across
both trials, and temporal variation in these parameters occurred
simultaneously across tanks. Mean pH and pCO2 varied between
tanks within target pH treatments (i.e. tanks targeted at pH
7.7 ultimately settled around slightly differing mean pHs) resulting
in a spread of mean tank pCO2 values ranging from 356 μatm (pH
8.233) to 10,644 μatm (pH 6.87) in trial 1 and from 471 μatm (pH
8.11) to 9691 μatm (pH 6.91) in trial 2.
Temporal variation in pH was highest in tanks associated with
target pH treatment level 7.3 (both trials) and in lower pH tanks
5
from day 5 of feeding in trial 2 (see Appendix). However, with pH
being a log scale, variation in pCO2 tended to increase at lower pH
treatment levels and there is a clear positive correlation between
standard deviation of pCO2 and mean pCO2, with SD being higher
at higher mean pCO2 (Appendix). A comparison of mean pCO2 with
CV of pCO2 shows the CV of tanks in target treatment pH 7.3 to be
higher than for other treatments (Appendix).
2.3. Egg and larval rearing
Each of the two trials was continuous and spanned three
developmental phases: egg phase, yolk-sac larval phase, and
first-feeding larval phase (to 7 days after first feeding). Fertilized
eggs were collected two hours after spawning from the broodstock
tank and randomly stocked at a density of 177 eggs/L (14,000 eggs
total per tank) in each of 15 cylindrical egg-incubation nets (79-L
volume) nested one per experimental tank. Spawning time was
visually determined by broodstock behavior and the presence of
eggs at the tank surface. Stocking density was made consistent in
the following manner: After determining hatch tank volume, eggs
were mixed evenly through the tank (via gentle upwelling) prior
to removing three 300-mL samples. The eggs were counted in
each of the three samples and the mean was used to calculate the
number of eggs per litre in the hatch tank volume. The same
amount of eggs were stocked randomly in each of the 15 tanks.
Yolk-sac larvae were then dispersed from the egg-incubation nets
into their respective experimental tanks 2 h after 50% of the eggs
had hatched. The yolk-sac phase in yellowfin tuna larvae continues
until approximately 50–70 h after hatching depending on water
temperature. Yolk-sac-stage and feeding larvae were maintained
in the same experimental tanks until early on the seventh day of
feeding (approximately 8.5 days post-hatching; 9.38 days total
from egg transfer), when the trials were terminated.
Larvae were fed cultured Brachionus plicatilis (rotifers) at densities
of 3–5 rotifers/mL three times daily. Dense blooms of unicellular algae
(500,000–750,000 cells/mL; “green water”) were maintained in each
tank to facilitate rearing (Margulies et al., 2007b).
2.4. Sampling protocols
During each phase (egg, yolk-sac, and first-feeding), 15–17 fresh
samples were taken at various intervals from each tank to be
measured (total length, notochord length, body depth at pectoral,
body depth at vent) and processed for dry-weight determination. Samples collected during each developmental phase were
also fixed and stored for later analyses of tissue histology and
organ development, feeding success, genetic variability and otolith
Table 1
Mean pH and mean estimated pCO2 (μatm) for each tank and each trial (trials 1 and 2) with 95% CI indicated in parentheses. Target pH and experimental tank number are
also indicated.
pH targetted
8.1
8.1
8.1
7.7
7.7
7.7
7.3
7.3
7.3
6.9
6.9
6.9
Trial 1
Trial 2
Tank
Mean pH achieved (95% CI)
Mean pCO2 (95% CI)
Tank
Mean pH achieved (95% CI)
Mean pCO2 (95% CI)
13
3
8
15
5
6
12
2
9
1
10
11
8.23
8.19
8.21
7.51
7.62
7.54
7.36
7.35
7.33
6.93
6.87
6.9
348
391
366
2413
1767
2145
3738
3958
3763
9200
10,467
9806
15
5
6
13
3
8
12
2
9
1
10
11
8.11
8.09
8.1
7.63
7.63
7.63
7.24
7.26
7.19
6.96
6.91
6.99
460
467
464
1835
1599
1723
4731
4536
4931
8983
9523
8036
(8.22–8.25)
(8.18–8.2)
(8.2–8.22)
(7.49–7.52)
(7.6–7.64)
(7.53–7.55)
(7.33–7.39)
(7.31–7.39)
(7.3–7.35)
(6.91–6.94)
(6.85–6.89)
(6.88–6.92)
(339–357)
(384–398)
(355–376)
(2338–2487)
(1722–1811)
(2099–2191)
(3509–3966)
(3644–4272)
(3525–4001)
(8970–9430)
(10153–10780)
(9502–10111)
(8.1–8.12)
(8.08–8.11)
(8.09–8.11)
(7.61–7.66)
(7.62–7.65)
(7.61–7.65)
(7.21–7.26)
(7.23–7.28)
(7.17–7.21)
(6.94–6.98)
(6.9–6.92)
(6.98–7.01)
(444–476)
(450–483)
(448–480)
(1725–1946)
(1529–1668)
(1647–1799)
(4473–4990)
(4288–4784)
(4701–5160)
(8611–9355)
(9244–9802)
(7747–8325)
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(Thunnus albacares). Deep-Sea Res. II (2014), http://dx.doi.org/10.1016/j.dsr2.2014.03.019i
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6
development, however the results from these response variables
are not presented in this paper.
2.5. Data analyses
By current conventions, pCO2 is the preferred variable used to
examine ocean acidification impacts in these types of experiments
(Riebesell et al., 2010). Our analyses assessed three key questions:
(1) Is there a significant relationship between pCO2 and each of
the key response variables (time to hatching completion, final
day larval survival and growth), over the range of pCO2
explored? If so, what is the functional form (shape) of that
relationship?
(2) For response variables showing a statistically significant overall relationship with pCO2, at what level of pCO2 does the
response first become statistically significant?
(3) What is the level of individual variability in larval responses
and how many tanks and experiments would be needed to be
80% sure of detecting an effect of the identified size.
The overall relationship between each response variable and
mean pCO2 was evaluated using either generalized linear models
(GLM) or generalized additive mixed models (GAMMs), treating
mean pCO2 as a continuous numerical quantity rather than a
treatment effect, due to variance in mean pCO2 between tanks
within target treatments. Both linear and non-linear relationships
were explored. Where GLMs were used, the sample-size adjusted
Akaike Information Criterion (AICc; Akaike, 1973; Burnham and
Anderson, 2002) was used to select between linear and non-linear
models for each response variable. Due to differences in ambient
seawater conditions in Achotines Bay (i.e. the seawater intake)
between trials, data collected from each trial were modeled
separately to explicitly recognize and compare treatment effects
within each of the two trials. All statistical analyses were carried
out in R version 2.12.1 (R Development Core Team, 2010) using the
base and mgcv (Wood, 2004, 2006) packages.
In addition, for those response variables for which there was a
significant trend with increasing mean pCO2 (see Section 3), a
classical test of all pair-wise comparisons was done using the pH
treatment level (comprising the set of tanks targeted at specific
pHs) as the independent factor. Results of the all pair-wise
comparison tests indicates which treatments were most influential with respect to the overall trends. The multiple comparisons
testing was conducted using Tukey contrasts with a 95% familywise confidence level (Hsu, 1996) and implemented in R with the
base and multcomp (Hothorn et al., 2008) packages.
2.5.1. Egg stage duration
Egg-stage duration in hours was determined from the time of
egg transfer until 2 h following the time at which 50% of the eggs
had hatched. Based on hatch rate estimates that have been
conducted 2 hours following 50% hatching, very few (o5%) live
unhatched eggs remain in the tanks at this time (Margulies et al.,
2007a). To determine the time at 50% hatching, we collected a
sample of eggs from the incubation tank at 15-min intervals,
beginning about 12–15 h (depending on water temperature) after
the estimated time of spawning. GLMs were used to assess the
relationship between mean pCO2 and egg stage duration.
2.5.2. Survival
Each trial was terminated on the seventh and final day of
growth (six full days of feeding), and each tank was slowly drained
of water. All surviving larvae were removed by beaker and counted
and the expected survival was estimated for each tank after
adjusting for previous sample removals (Margulies, 1989; Wexler
et al., 2011). A series of nested cubic spline regression models were
fitted to expected survival values with mean pCO2 as the independent variable, and the final model for each trial was selected
using AICc. Nested models examined were: a constant only model;
a linear model; and a non-linear model with 2 degrees of freedom.
The final survival count in Tank “15” of trial 2 was excluded from the
analyses because, while larval densities were observed to be very
high up until the final night of the trial, an unexplained sudden mass
mortality event occurred in this tank in the hours prior to final
survival counts (contrasting sharply to gradual mortality rates in all
other tanks).
2.5.3. Growth
Generalized Additive Mixed Models (GAMMs) were used to
explore the relationship between pCO2 and size on the seventh
and final day of growth, modeling “tank” as a random effect (to
account for multiple measurements from the same tank), with the
two trials modeled separately. Two size indicators were analyzed
(dry weight and standard length) in separate models. Smooth
terms were based on thin plate regression splines and the degree
of smoothing was selected by cross-validation (Wood, 2006).
2.5.4. Power analysis
One of the key objectives for this pilot study was to gain an
understanding of individual variability in larval survival responses
to better design future experimental trials and ensure greater
statistical power/sensitivity. Firstly, the size of the effect that we
wish to be able to detect was identified. Secondly, we simulated
from the observed errors to identify how many tanks and experiments would be needed to be 80% sure of detecting this size of an
effect. For survival we would need to identify a 20% reduction in
survival between egg transfer and 6 days given a change in pH
from 8.2 to 7.8. This translates into 300 fewer survivors given an
average survival of 1380 larvae at pH of 8.2. For the power analysis
we simulated 10,000 trials comparing survival at 8.2 and 7.8, for
each count of tank pairs between 3 and 15. Survival in each tank
was sampled from a normal distribution with standard deviation
estimated for the tank effect in the trial, and a difference of 300
between the expected values in pH 8.2 vs 7.8. The data were
analyzed using a generalized linear model with normal errors, and
statistical power was estimated from the proportion of trials
showing statistical significance at the 5% level. The coefficient of
variation of the estimated treatment effect was also estimated.
3. Results
3.1. Egg stage duration
In Trial 2, spawning occurred one hour earlier in the night
(compared to trial 1) and the final transfer of eggs also occurred
one hour earlier. Hatching occurred within 17–18 h in Trial 1 and
within 19–20 h in Trial 2 from the times that eggs were transferred
to the treatment tanks (Fig. 4). AICc based GLM model selection
identified a significant positive linear relationship between mean
pCO2 and hours until complete hatching in both trials (Fig. 4 and
Table 2). For trial 1, hatch times at mean pCO2 values less than
3000 μatm were very similar but hatch times at pCO2 49500 μatm were increased (relative to those o3000 μatm). Pairwise
analyses were not conducted due to the lack of intermediate
treatments. For trial 2, pair-wise comparisons (Table 3) indicate
that the mean hatch time at pH 8.06 (pCO2 ¼ 533) was significantly
less than that at each of mean pHs 7.69 (pCO2 ¼1573), 7.25
(pCO2 ¼ 5251) and 6.99 (pCO2 ¼9602).
Please cite this article as: Bromhead, D., et al., The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna
(Thunnus albacares). Deep-Sea Res. II (2014), http://dx.doi.org/10.1016/j.dsr2.2014.03.019i
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were smaller on average than larvae reared at pCO2 close to
present day levels (356–477 μatm), in both trials. Pair-wise comparisons for both trials (Table 3) indicated a significant difference
in dry weights between larvae reared in the control treatment
(mean pCO2 368 in Trial 1 and 464 in Trial 2) and the highest
pCO2 treatments (mean pCO2 9824 in Trial 1 and 8847 in
Trial 2) but not between larvae reared at the control and intermediate pCO2 levels. The significant overall trends (from the
GAMM) are therefore mainly due to the highest pCO2 treatment
effects.
21
20
19
hatch hours
7
18
17
16
15
0
2000
4000
6000
8000
10000
12000
14000
mean pCO2
Fig. 4. Predicted linear relationship between mean pCO2 and hours to complete
hatching (Trial 1, black line) (Trial 2, red line). Dashed lines represent 95%
confidence intervals; open circles indicate the data used to fit the models. (For
interpretation of the references to color in this figure legend, the reader is referred
to the web version of this article.)
3.3.2. Standard length
GAMMs applied to larval standard length data indicated a
significant negative non-linear relationship between size (standard
length) of larvae and mean pCO2, in trial 1 and a significant
negative linear relationship between size (standard length) of
larvae and mean pCO2 in trial 2 (Fig. 7 and Table 5). Larvae from
higher pCO2 tanks were on average larger in trial 2 than trial 1.
Pair-wise comparisons for trial 2 (Table 3) indicated that the
significant overall decreasing trend (from GAMM analyses) in
standard length was influenced by the difference in mean lengths
between the control pCO2 464 and pCO2 8847 (the highest and
lowest pCO2 treatments). However, in trial 1, larvae reared in the
treatment tanks with mean pCO2 2108 and mean pCO2 4732
were significantly smaller than those reared in the control treatment (pCO2 368) (Table 3).
3.4. Power analyses
Table 2
Summary model statistics for the egg stage duration analysis assessing the
relationship between mean pCO2 and time (h) until complete hatching in each of
two separate experimental trials.
Model set pCO2 range (μatm) Model term tested Pr(4 |t|)
Trial 1
330–13480
pCO2 (linear)
pCO2 (cs: df¼ 2)
0.0000 ( þ )
0.0121 ( þ);
0.0000 ( þ )
Constant ( 1)
Trial 2
525–10993
pCO2 (linear)
Constant ( 1)
pCO2 (cs: df¼ 2)
AICc
3.28
1.24
19.6
0.0426 ( þ )
o 0.135; o0.190
7.94
9.45
12.5
3.2. Survival
Survival by day 7 of feeding was found to decrease with
increasing pCO2 in trial 1 (Fig. 5 and Table 4) with AICc selecting
a significant non-linear relationship between mean pCO2 and
larval survival. In contrast to trial 1, we did not detect a significant
relationship between survival and mean pCO2 in trial 2. The pairwise comparisons of mean survival among treatments in trial 1
(Table 3) indicates that mean survival at the control level (pH ¼
8.23, pCO2 ¼368) was not significantly different to survival at pH
7.56 (pCO2 2108) but was significantly greater than survival at pH
7.35 (pCO2 4732) and pH 6.90 (pCO2 8847). No pair-wise comparisons were conducted for trial 2 survival because the overall
trend was not significant (Table 4).
3.3. Growth
3.3.1. Dry weights
GAMMs applied to larval dry weight data indicated a significant
negative linear relationship between size (dry weight) of larvae
and mean pCO2, in both trials 1 and 2, with the effect of pCO2 on
size (slope of relationship) greater for larvae from trial 1 (Fig. 6 and
Table 5). Larvae reared at the highest pCO2 levels (48000 μatm)
The power analysis of larval survival from trial 1 determined
that the standard deviation of the survival data residuals for larva
(on day 7 of feeding) was 200. Given a single experiment with
tanks at 8.2 and 7.4, an 80% confidence of detecting a size effect of
300 between the 2 treatments at the p o0.05 level would require
10 tanks per trial, which would also give an expected cv of about
33%. Simulations using between 3 and 15 tanks per treatment
estimated the power levels provided in Table 6.
4. Discussion
Our study tested the effect of a range of pH ( 6.9 to 8.2) and
pCO2 (330–10,467 μatm) conditions upon yellowfin tuna eggs and
larvae. This range is broad enough to take into account current and
future (to the year 2300) spatial variability in water chemistry
across the yellowfin tuna spawning habitat range in the Pacific.
Experimental tanks with mean pCO2 of less than 2500 μatm
provided conditions that are relevant to the assessment of “near
future” (i.e. year 2100) impacts on yellowfin tuna. Over longer time
frames (e.g. to 2300) or with further increases in CO2 emissions,
pCO2 may increase further and pH decrease further (e.g. by
0.7 pH units according to Caldeira and Wickett, 2003), in which
case our experimental tanks with mean pCO2 between 2500 and
5000 μatm are relevant. In the current study, it was clear that the
strongest and most consistent impacts across larval yellowfin early
life history processes occurred at the highest pCO2 levels tested
(48500 μatm) and that these levels are outside those predicted to
occur in the next 300 years. However, there was evidence of
significantly reduced survival at mean pCO2 levels ofZ 4730 μatm
(trial 1), significantly reduced larval size at mean pCO2 Z2108
(trial 1) and prolonged egg hatch time (trial 2) at mean pCO2
levels Z1573 μatm, that are relevant to near future predicted
levels. Significant effects at near future pCO2 levels were not,
however, consistently predicted in both trials and the reasons for
this inconsistency are discussed below. The results will assist in
the future design of controlled experiments that will estimate
Please cite this article as: Bromhead, D., et al., The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna
(Thunnus albacares). Deep-Sea Res. II (2014), http://dx.doi.org/10.1016/j.dsr2.2014.03.019i
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8
Table 3
Results of all pair-wise comparisons for each quantity and experiment. The differences in mean levels and adjusted p-values (in parentheses) are provided. Note that the
mean pH and pCO2 conditions up until hatching in Trial 2 differed to the mean conditions to the end of Trial 2 (when weights and lengths were measured) and so hatch times
and size measures for Trial 2 are reported in separate tables.
pCO2 Comparison (actual means)
Survival
Standard length
Dry weight
Trial 1 – Survival, length and weight
8.1–7.7
8.23–7.56
8.1–7.3
8.23–7.35
8.1–6.9
8.23–6.90
7.7–7.3
7.56–7.35
7.7–6.9
7.56–6.90
7.3–6.9
7.35–6.90
368–2108
368–4732
368–8847
2108–4732
2108–8847
4732–8847
363.7(0.320)
870.0(0.010)
955.7(0.006)
506.3(0.121)
592.0(0.066)
85.6(0.971)
0.42(0.004)
0.36(0.017)
0.68( o 0.001)
0.06(0.970)
0.26(0.147)
0.32(0.052)
19.6(0.367)
11.5(0.777)
40.4(0.005)
8.1(0.908)
20.7(0.319)
28.8(0.081)
Comparison (pH targets)
pH comparison (actual means)
pCO2 comparison (actual means)
Hatch time
Trial 2 – Hatch time
8.1–7.7
8.1–7.3
8.1–6.9
7.7–7.3
7.7–6.9
7.3–6.9
8.06–7.69
8.06–7.25
8.06–6.99
7.69–7.25
7.69–6.99
7.25–6.99
533–1573
533–5251
533–9602
1573–5251
1573–9602
5251–9602
0.61(0.001)
0.36 (0.026)
0.69 (o 0.001)
0.25 (0.126)
0.08 (0.830)
0.33 (0.038)
Comparison (pH targets)
pH Comparison (actual means)
pCO2 Comparison (actual means)
Standard length
Dry weight
Trial 2 – Length and weight
8.1–7.7
8.1–7.3
8.1–6.9
7.7–7.3
7.7–6.9
7.3–6.9
8.10–7.63
8.10–7.23
8.10–6.95
7.63–7.23
7.63–6.95
7.23 - 6.95
464–1719
464–4732
464–8847
1719–4732
1719–8847
4732–8847
0.16 (0.536)
0.18 (0.430)
0.49 (o 0.001)
0.02 (0.998)
0.33 (0.059)
0.31 (0.099)
4.4 (0.946)
7.1 (0.817)
25.5 (0.016)
2.7 (0.989)
21.1 (0.082)
18.4 (0.173)
Comparison (pH targets)
pH Comparison (actual means)
Table 4
Summary model statistics for survival analyses by generalized linear model (GLM).
2500
Mean pCO2 range (μatm) Model term tested Pr( 4|t|)
Model
Set
2000
survival
Trial 1
pCO2 (cs: df¼ 2)
348–10467
pCO2 (linear)
constant ( 1)
1500
Trial 2
Constant ( 1)
pCO2 (linear)
pCO2 (cs: df¼ 2)
460–9563
1000
AICc
0.0011 (þ ); 176.8
0.0258 ( þ)
0.0055 ( þ ) 179.2
185.2
0.579
0.263;
0.818
181.8
185.4
189.1
500
140
120
0
2000
4000
6000
8000
10000
mean pCO2
Fig. 5. Predicted relationship between mean pCO2 and larval survival after 7 days
of growth (Trial 1, black line) (Trial 2, red line). Dashed lines represent 95%
confidence intervals; points indicate the data used to fit the models. (For
interpretation of the references to color in this figure legend, the reader is referred
to the web version of this article.)
100
Dry weight (ug)
0
80
60
40
interaction effects (discussed below) and the functional forms of
the relationships. This information should permit population
dynamics models to include acidification effects when forecasting
species distribution and abundance.
Inconsistent results between trials are likely to result from
differing experimental conditions. We observed reduced survival
of yellowfin larvae after 7 days of feeding in the first trial with
increasing mean pCO2. Compared to control (present day pCO2)
conditions, significantly lower survival became apparent at pCO2
treatment levels of 4732 μatm or higher. There was no statistical
relationship between survival and mean pCO2 in the second trial,
due in large part to the high level of intra-treatment variability in
20
0
0
2000
4000
6000
8000
10000
Mean pCO2
Fig. 6. Predicted relationship between mean pCO2 and dry weight for Trial 1 (black
lines and points) and Trial 2 (red lines and points). Dashed lines represent 95%
confidence intervals; circles indicate the data used to fit the models. (For interpretation
of the references to color in this figure legend, the reader is referred to the web version
of this article.)
Please cite this article as: Bromhead, D., et al., The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna
(Thunnus albacares). Deep-Sea Res. II (2014), http://dx.doi.org/10.1016/j.dsr2.2014.03.019i
D. Bromhead et al. / Deep-Sea Research II ∎ (∎∎∎∎) ∎∎∎–∎∎∎
uncommon but have been observed previously in high larval
density tanks at Achotines after 4–5 days of feeding. High survival
in that tank, if it had been included in the analysis, would have
provided a declining survival trend with increasing pCO2 in trial 2.
We therefore believe that the results from Trial 1 were obtained
under more stable conditions and are more likely to represent
average larval responses to increasing pCO2 levels, than the results
from Trial 2. It will be important to undertake further trials to test
this conclusion.
The egg stage and growth results were somewhat more consistent between the two trials. Mean larval sizes (dry weights and
standard lengths) after 7 days of feeding declined with increasing
mean pCO2 in both trials, with significant effects upon larval lengths
apparent at lower pCO2 levels (i.e. mean pCO2 2108 or above) in
trial 1 than in trial 2 (effects significant for pCO2 8847 only). Slower
larval growth during the first week of feeding may subsequently
impact survival by affecting foraging success and by prolonging stage
durations, increasing larval size-dependent susceptibility to predation (review in Leggett and Deblois, 1994).
Egg stage duration increased with increasing pCO2, indicating a
one hour delay until hatching at the highest pCO2 levels tested.
Similar delays in hatching have also been observed during adverse
physical conditions of very low dissolved oxygen levels and
extremely high water temperatures (Wexler et al., 2011). While
the relationship was not, according to GLM, highly significant in
trial 2, pairwise testing indicated that hatching was delayed in all
treatments relative to the control (present day). In Trial 1 it was
not possible to achieve the desired spread of treatment pCO2s
(after 2 days) and it was not possible to test pCO2 levels between
3000 and 9000 μatm, meaning the shape of the relationship over
the range of pCO2 tested is poorly defined. Note that target pH
levels (and associated pCO2 levels) were not closely attained in any
of the tanks in the first 24 h of trial 1 (Table A1 in Appendix), and
the range and distribution of mean pCO2 across tanks at the
completion of egg hatching differed between the two trials. It
should be noted that it was not possible to determine hatching
rates in the current study due to uneven distribution of eggs in the
hatching tanks, but the influence of this on final survival estimates
should be looked at in the future.
The impact of increased pCO2 (or decreased pH) upon larval
yellowfin, particularly as observed in trial 1, needs to be placed in the
context of the impact of other natural and anthropogenic factors that
influence larval yellowfin growth and survival. Some of the most
potent biological and physical factors controlling the survival and
growth of yellowfin larvae during the first two weeks of life include
food abundance and quality (foremost), as well as physical factors
such as microturbulence (wind induced) and light intensity that
influence the distribution and physical characteristics of larval prey
(Margulies et al., 2007a). These biological and physical factors can
result in order-of-magnitude (2–5 ) effects on yellowfin larval
survival and significant effects on growth in experimental studies
(Margulies et al., 2007a). It is notable that the potential effects of
ocean acidification on survival and growth of larvae indicated in Trial
1 of this study are of roughly the same magnitude.
There are also physical factors that impact yellowfin larval
growth and survival that are predicted to change in the future as a
result of climate change and whose impacts may interact with
ocean acidification. Water temperature and dissolved oxygen
survival. The two trials were conducted in different months and
under conditions that differed in a number of ways. Firstly, while
dissolved oxygen in trial 1 was relatively stable ( 8.2 to 8.5 mg/L),
there was a drop in dissolved oxygen from 8.5 to 7.5 mg/L in the
middle of trial 2. While these levels are above those previously
reported to impact yellowfin growth and survival (Wexler et al.,
2011), the possibility of an interaction with pCO2 (or pH) cannot be
discounted. Secondly, during trial 2 a phytoplankton bloom (of
unknown species) occurred near the experimental water intake in
Achotines Bay and the impact of this upon the chemistry of the
water sourced from Achotines Bay is unknown. Thirdly, broodstock
spawning (eggs produced) dropped sharply in the days just prior
to the start of trial 2 and the relative contributions of individual
broodstock to spawning in each trial may have differed, as may
broodstock condition, and affected the mean viability of offspring
in each trial. Fourthly, technical problems led to a loss of pH
control across tanks in the lowest pH treatment (pH 6.9) towards
the end of trial 2. Finally, there was a sudden mortality event
(crash) in tank 15 (pH 8.1) on the last night of trial 2, associated
with a very high density of larvae throughout the trial in the same
tank, which may have caused an ammonia spike. Such events are
Table 5
Summary model statistics for dry weights and standard length analyses. “edf”
indicates the estimated degrees of freedom.
Growth measure
Trial
edf – s (mean pCO2)
F
Dry weight
1
2
1
1
11.65
13.00
0.0008
0.0004
Standard length
1
2
2.15
1
13.35
20.69
o 0.00001
0.00001
p value
Standard length (mm)
5.0
4.5
4.0
3.5
3.0
0
2000
4000
6000
8000
9
10000
Mean pCO2
Fig. 7. Predicted relationship between standard length (mm) and mean pCO2 in
trial 1 (black lines and points) and trial 2 (red lines and points) with dashed lines
indicating 95% CIs. Open triangles indicate the data used to fit the models. (For
interpretation of the references to color in this figure legend, the reader is referred
to the web version of this article.)
Table 6
Power levels based on simulations with between 3 and 15 tanks.
Tanks/treatment
3
4
5
6
7
8
9
10
11
12
13
14
15
Power
Mean CV
0.24
0.57
0.36
0.51
0.45
0.46
0.54
0.43
0.63
0.4
0.7
0.37
0.75
0.35
0.8
0.33
0.84
0.32
0.87
0.3
0.9
0.29
0.92
0.28
0.94
0.27
Please cite this article as: Bromhead, D., et al., The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna
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levels are recognized as key factors influencing early life history
stages in fish and have been studied in yellowfin tuna. Wexler
et al. (2011) determined that the optimal range of temperatures for
rapid growth and moderate to high survival of larval yellowfin
tuna (at 2 days of feeding) was 26–31 1C, with survival significantly reduced outside this range (despite growth rates continuing
to increase above 31 1C). This temperature range encompasses
surface temperatures observed across much of the yellowfin
spawning habitat in the Pacific Ocean (indeed it should not be
surprising that yellowfin might choose to spawn in conditions that
increase the survival likelihood of their larvae). The same study
observed that yellowfin larval survival is reduced when dissolved
oxygen levels drop below 2.65 mg O2/L, but these levels are
typically well below the levels found in the upper mixed layer
where yellowfin larvae reside (Wexler et al., 2011). Events such as
upwellings (with lower temperatures and dissolved oxygen) can
however reduce the vertical habitat of the larvae (Wexler et al.,
2011). Thus yellowfin larvae appear well adapted to cope with the
range of temperatures and oxygen that typically occur within their
normal habitat. The current experiments provided no evidence
that survival would vary within the current seasonal range of pH
in surface waters off Achotines ( pH 7.9–8.1), noting that effects
on survival in trial 1 were only apparent at pH 7.56 (pCO2
4732 μatm), a level that might only be reached after 300 years.
However, consideration will need to be given in future yellowfin
tuna experiments to the potential interactive effects of ocean acidification with temperature and oxygen, which will also vary under
future climate change (Gruber, 2011). Ocean acidification and other
parameters such as temperature have already been shown to interact
for some species (Nowicki et al., 2012; Munday et al., 2009a; Enzor
et al., 2013). Hypoxic zones in the ocean may increase in the future as
a result of climate driven changes in temperatures and deep ocean
mixing (Hofman and Shellhuber, 2009). Survival during the onset of
feeding in yellowfin tuna larvae is greatly affected by short-term
oxygen deficits at water temperatures Z26 1C (Wexler et al., 2011).
The mean pCO2 was confounded with both dO2 and variance in pCO2
across tanks. The level of variance in dO2 was small and the lowest
observed level of dO2 was substantially higher than levels required to
depress larval yellowfin growth or survival (Wexler et al., 2011).
However, larval sensitivity to dO2 might in fact increase under
elevated pCO2 conditions and this needs to be tested in future
experiments.
This study has highlighted the need for further research into the
potential impacts of ocean acidification upon yellowfin tuna and
tropical tunas in general. In particular, the current results can be used
to help design more intensive experimental trials. Power analyses of
variability in survival responses within and between tanks/treatments
indicated that more replicate tanks per treatment are needed to
increase the statistical power and sensitivity of the experiments, so
that survival relationships can be identified and described. This was
less of an issue for the growth analyses for which sample numbers
were an order of magnitude higher. It will also be important to seek
full randomization (across rows and columns of the block design) in
future experiments to ensure greater confidence in the statistical
outputs, and to include within future experimental designs a lower
pCO2 treatment (1000 μatm) to test yellowfin larval responses to
lower “near future” pCO2 levels. Aside from improving experimental
design, research on other fish species has indicated that a number of
additional key factors will need to be considered when assessing the
potential for ocean acidification to impact yellowfin tuna populations.
Recent research has demonstrated that exposure of broodstock to
elevated pCO2 prior to spawning may reduce or remove negative
impacts of elevated pCO2 upon behavior of larvae derived from
subsequent spawnings (Miller et al., 2012), a mechanism that should
also be assessed in yellowfin. Similarly, investigations into the capacity
of yellowfin tuna to adapt (through selection for more resistant
individuals) should also continue. The very high fecundity and
relatively short generation time of yellowfin tuna may permit them
to adapt more rapidly than less fecund, longer lived species. It will be
important to determine the extent to which inter-individual variation
mediates different selection responses (Schlegel et al., 2012). Some
species show significant individual variability in somatic and behavioral responses to elevated pCO2 (e.g. Munday et al., 2010; Munday et
al., 2012; Ferrari et al., 2011), and in one study resistant individuals
suffered lower predation-based mortality rates when released in the
field (Munday et al., 2012) suggesting potential for rapid selection and
adaptation and higher robustness of populations to ocean acidification
(Frommell et al., 2012). However selection processes may weaken at
higher pCO2 levels, if fewer individuals are resistant (Munday et al.,
2010; Ferrari et al., 2011). It is also unknown whether resistant
individuals traits are heritable (Munday et al., 2012). Genetic analyses
of samples taken from the current trials are in progress to assess
whether some genotypes are more robust to changes in pCO2, and
these results should provide insights on the designs needed to assess
this important question. Results of our supplemental sampling on the
feeding patterns, otolith morphology and histological condition of
internal tissues of larvae will also permit us to further investigate how
acidification affects early life stages. Analyses of samples taken from
yolk-sac and early feeding larvae will allow us to assess at what point
in development any effects on growth first became apparent.
Sub-lethal effects on behavior also warrant attention. Behavioral
changes have been observed in larval reef fish (including responses to
predator cues and capacity to find and consume prey – see Section 1)
due to the altered functioning of a key brain neurotransmitter receptor
Gamma-Amino Butyric Acid (GABA)-A, that results from exposure to
elevated CO2 (Nilsson et al., 2012). GABA-A is highly conserved across
marine fish species, raising the possibility that behavioral changes
could be common across many fish species. Noting that such
behavioral effects have already been demonstrated for one reef species
to lead to increased predation in the field (Munday et al., 2010), we
consider that behavioral impacts of elevated CO2 may have significant
implications for the recruitment of affected species. Similarly, investigating the impacts of increasing pCO2 on sperm motility and fertilization success is warranted (e.g. Havenhand et al., 2008; Frommel
et al., 2010).
Empirical research such as presented here can allow models
such as SEAPODYM (Lehodey et al., 2008) to be parameterised to
include acidification effects and subsequently enable scientists and
tuna fishery managers to better understand how these changes in
ocean chemistry will alter the distribution and abundance of
yellowfin tuna (Hobday et al., 2013). Environmental data are used
in SEAPODYM to functionally characterize the habitat of the
population depending on its thermal, biogeochemical, and forage
preferences (Lehodey et al., 2008, 2010). To this end, it will be
critical that further empirical trials are conducted to more clearly
identify the functional form of the relationship between pH (or
pCO2) and larval survival, and in particular, identify any interactions between pH (or pCO2) and other key physical oceanographic
factors such as temperature and oxygen, so as to provide relevant
information for appropriately altering the spawning-habitat index
and adjust local mortality rates in SEAPODYM. Subsequent population level predictions of ocean acidification impacts will enhance
the capacity of regional fisheries management organizations to
make more-informed decisions regarding the management of the
highly valuable tropical tuna resources, particularly with regard to
attaining key sustainability-related objectives (Bell et al., 2013).
Acknowledgments
We would like to thank the laboratory staff of the IATTC
Achotines Laboratory (L. Tejada, S. Cusatti, D. Ballesteros, D. Solis,
Please cite this article as: Bromhead, D., et al., The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna
(Thunnus albacares). Deep-Sea Res. II (2014), http://dx.doi.org/10.1016/j.dsr2.2014.03.019i
D. Bromhead et al. / Deep-Sea Research II ∎ (∎∎∎∎) ∎∎∎–∎∎∎
C. Vergara, L. Castillo, D. Dominguez, D. Ramires, A. Ortega, D.
Perez, H. Espinoza, Y. Ballesteros, W. Reluz, D. Mancilla) for direct
support during the experimental trials as well as assembly and
maintenance of the experimental system, three anonymous reviewers
for their insightful comments and suggestions that helped improve
this manuscript, P. Munday from James Cook University for comments
on the introduction and the IATTC’s Director, G. Compeán, and Chief
Scientist, R. Deriso, for their support. The research was funded under
Cooperative Agreement NA09OAR4320075, Project 661553 from the
National Oceanic and Atmospheric Administration with the Joint
Institute for Marine and Atmospheric Research (JIMAR), University of
Hawaii (UH).
Appendix A. Supporting information
Supplementary data associated with this article can be found in
the online version at: http://dx.doi.org/10.1016/j.dsr2.2014.03.019.
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Please cite this article as: Bromhead, D., et al., The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna
(Thunnus albacares). Deep-Sea Res. II (2014), http://dx.doi.org/10.1016/j.dsr2.2014.03.019i