Effects of current and future coastal upwelling conditions on the
fertilization success of the red abalone (Haliotis rufescens)
Charles A. Boch
*, Steven Y. Litvin
, Fiorenza Micheli
, Giulio De Leo
, Emil A. Aalto
, C. Brock Woodson
, Stephen Monismith
, and James P. Barry
Monterey Bay Aquarium Research Institute, 7700 Sandholdt Road, Moss Landing, CA 95039, USA
Hopkins Marine Station, Stanford University, Paciﬁc Grove, CA 93950, USA
College of Engineering, University of Georgia, Athens, GA 30602, USA
Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA
*Corresponding author: tel: 831-775-1849; fax: 831-775-1620; e-mail: email@example.com
Boch, C. A., Litvin, S. Y., Micheli, F., De Leo, G., Aalto, E. A., Lovera, C., Woodson, C. B., Monismith, S., and Barry, J. P. 2017. Effects of current and
future coastal upwelling conditions on the fertilization success of the red abalone (Haliotis rufescens). – ICES Journal of Marine Science,
Received 30 August 2016; revised 10 January 2017; accepted 29 January 2017.
Acidiﬁcation, deoxygenation, and warming are escalating changes in coastal waters throughout the world ocean, with potentially severe con-
sequences for marine life and ocean-based economies. To examine the inﬂuence of these oceanographic changes on a key biological process,
we measured the effects of current and expected future conditions in the California Current Large Marine Ecosystem on the fertilization suc-
cess of the red abalone (Haliotis rufescens). Laboratory experiments were used to assess abalone fertilization success during simultaneous ex-
posure to various levels of seawater pH (gradient from 7.95 to 7.2), dissolved oxygen (DO) (60 and 180 mm
kg SW) and temperature (9, 13,
and 18 C). Fertilization success declined continuously with decreasing pH but dropped precipitously below a threshold near pH 7.55 in cool
(9 C—upwelling) to average (13 C) seawater temperatures. Variation in DO had a negligible effect on fertilization. In contrast, warmer
waters (18 C) often associated with El Ni~
no Southern Oscillation conditions in central California acted antagonistically with decreasing pH,
largely reducing the strong negative inﬂuence below the pH threshold. Experimental approaches that examine the interactive effects of mul-
tiple environmental drivers and also strive to characterize the functional response of organisms along gradients in environmental change are
becoming increasingly important in advancing our understanding of the real-world consequences of changing ocean conditions.
Keywords: climate change, fertilization, Haliotis rufescens, hypoxia, multiple drivers, ocean acidiﬁcation, ocean warming, upwelling.
Fossil fuel CO
emissions are driving massive and rapid changes
in global temperature and ocean chemistry (Bakun, 1990;
Caldeira and Wickett, 2003;Sabine et al., 2004;Solomon et al.,
2007;Chan et al., 2008). These global scale impacts are leading to
a cascade of changes in ocean stratiﬁcation, transport, convection,
and other key processes at both regional and local scales. In the
California Current Large Marine Ecosystem (CCLME), shoaling
of the oxygen minimum zone (Stramma et al., 2010) has pro-
moted a reduction in the pH and dissolved oxygen (DO) content
of upwelled waters that are advected into nearshore habitats
(Chan et al. 2008;Feely et al., 2008;Bograd et al., 2008;Connolly
et al., 2010;Keeling et al., 2010;Deutsch et al., 2011;Booth et al.
2012;Walter et al. 2014). In addition, an increase in El Ni~
conditions in combination with global ocean warming has re-
sulted in recent increases in temperatures in coastal ecosystems of
the CCLME (Trenberth and Hoar, 1997;Lee and McPhaden,
2010;Cai et al., 2014). These growing changes in key ocean con-
ditions are predicted to directly affect the physiology of many
marine organisms, with potentially profound effects on the sus-
tainability of marine populations (Vaquer-Sunyer and Duarte,
2008;Portner and Farrell, 2008;Somero et al., 2016). To date,
CInternational Council for the Exploration of the Sea 2017. All rights reserved.
For Permissions, please email: firstname.lastname@example.org
ICES Journal of Marine Science (2017), doi:10.1093/icesjms/fsx017
however, most research has focused on the response of marine or-
ganisms to shifts in a single oceanographic parameter (reviewed
by Gattuso et al., 2015) and our understanding of biological re-
sponses to simultaneous changes in multiple ocean conditions is
poorly understood. Experimental approaches that assess the re-
sponse of species, assemblages, and marine communities to realis-
tic future environmental variation among multiple drivers (e.g.
Kroeker et al., 2013) are needed to reﬁne our ability to predict the
future stability of ecosystem function and the sustainability of
ocean-based economies (Gattuso et al., 2015).
The persistence of natural populations depends on successful
reproduction, and much research has focused on the effects of
ocean acidiﬁcation, hypoxia and temperature variation on fertil-
ization success and early development in marine species (re-
viewed by Byrne, 2011). Although several biological factors (e.g.
gamete concentration, sperm:egg ratios, gamete age; Babcock and
Keesing, 1999;Baker and Tyler, 2001;Huchette et al., 2004)or
changes in ocean conditions (e.g. pH; Kurihara and Shirayama,
2004;Havenhand et al, 2008) are known to affect fertilization
success, few studies have examined the effects of simultaneous ex-
posure to multiple environmental changes (e.g. Byrne et al,
2010). Thus, we are just beginning to address how simultaneous
exposure to multiple drivers may inﬂuence these key processes in
marine organisms, and importantly, whether non-linear re-
sponses and tipping points exist across chemo-physical drivers.
These questions are particularly relevant in systems where envir-
onmental conditions are highly variable in space and time, and
where this variability is predicted to increase under future climate
change scenarios, such as upwelling ecosystems (Bakun, 1990;
Sydeman et al., 2014;Bakun et al., 2015). Here, we addressed the
individual and interactive effects of current and expected future
pH, DO, and temperature on fertilization success using a near-
shore benthic invertebrate, the red abalone Haliotis rufescens,asa
model system. Red abalone (H. rufescens), naturally inhabit the
intertidal to a depth of ca. 30 m from Southern Oregon to
Central Baja California of the CCLME (Boolootian et al., 1962).
Abalone and other ecologically important and economically
valuable benthic invertebrates are unable to escape bottom hyp-
oxia because of their limited mobility. They are also are negatively
affected by high pCO
(low pH) that can interfere with shell de-
position and growth, and are impacted by high temperatures, dir-
ectly or indirectly, through the loss of their algal food resources
(Shepherd et al., 1998;Orr et al., 2005;Micheli et al., 2012;
Gazeau et al., 2013;Kim et al., 2013). The question of how differ-
ent combinations of drivers may independently or interactively
affect abalone and other species remains unanswered. However,
recent experiments indicate that exposure to even a limited range
of low pH and low DO may have deleterious effects on mortality
and growth of early stage of red abalone juveniles (Kim et al.,
2013). Therefore, to increase our understanding of the likely fu-
ture effects of climate-driven changes in ocean conditions for
marine species, it is crucial to explore the effects of simultaneous
exposure to key environmental variables on critical processes
such as fertilization.
Here we examined the effects of variation in seawater pH, DO,
and temperature on the fertilization success of red abalone H.
rufescens. These oceanographic parameters are highly variable in
upwelling affected areas of nearshore habitats in the CCLME, and
their range of variability is shifting in response to climate change
(Sydeman et al., 2014) and as such, future upwelling is expected
to be increasingly stressful for coastal species (Somero et al.,
2016). We employed a hybrid (regression/factorial) experimental
approach to assess abalone fertilization success across a gradient
of seawater pH (regression, 40þpH levels), combined with a fac-
torial approach for DO (two levels: 6 and 2 mg/l DO) and tem-
perature (three levels: 9, 13, and 18C). This novel hybrid design
allowed us to characterize the functional response of fertilization
over a large range of pH, under multiple co-varying conditions,
and assess potential thresholds to changes in pH beyond current
Based on previous studies, we hypothesized that decreasing pH
may have a negative effect on fertilization (Kroeker et al., 2010;
Gazeau et al., 2013). However, potential interactions with DO
and temperature were difﬁcult to predict because of the limited
range or the number of pH levels that have been previously exam-
ined (Byrne et al., 2010). While decreasing pH may reduce fertil-
ization success, an additional driver may not further affect
fertilization or the independent effects of a secondary driver may
dominate, thus decoupling fertilization success from the effects of
pH (dominant stressor model; Halpern et al., 2008). In a more
complex process, a secondary driver may interact with pH, mod-
ifying the decline in fertilization rates along the pH gradient by
ameliorating or exacerbating negative impacts (antagonistic and
synergistic effects, respectively; Crain et al., 2008). Finally, the ef-
fects of an additional environmental driver may be non-linear
along a pH gradient.
Preparation of seawater treatments
Two days before each experiment, seawater sources were pro-
duced using the nitrogen, air, and CO
gas control system
described in Barry et al. (2008) and stored at 13C in gas-tight 10
l mylar bags (Calibrated Instruments, Inc., NY, USA) with one-
way Luer Lock stopcocks. The target pH (total scale) and DO
concentration of the seawater sources (Supplementary Table S1)
were adjusted in order to achieve the ranges utilized in the fertil-
ization experiments (pH 7.95–7.2 and DO 6 and 2 mg/l) while ac-
counting for the process of adding gametes (see ‘Assessment of
fertilization success’ section below). After 24 h, pH (n ¼3 each;
UV-1601 spectrophotometer, Shimadzu, Kyoto, Japan) and total
alkalinity (n ¼8; TA, TitroLine 7000 open cell, potentiometric ti-
tration system, SI Analytics, Germany) of each seawater source
were measured and used to estimate dissolved inorganic carbon
concentrations (DIC, at 13C and 33 psu). In addition, DO con-
centrations were veriﬁed using an Aanderaa 3830 optode (Xylem
Inc., NY, USA). Based on DIC and TA values (Supplementary
Table S1 and S2), mixtures of each seawater source needed to
achieve the desired range of pH and DO for a particular experi-
ment (see Supplementary material ‘Preparation of seawater sour-
ces and Determination of volumetric mixtures of seawater
sources to achieve target pH and DO’ section) were determined
SYS (http://cdiac.ornl.gov/oceans/co2rprt.html). These
predetermined mixtures were then loaded into 50 ml gas-tight
glass syringes ﬁtted with three-way Luer Locks (“experimental
syringes”; Tomopal, Japan). To remove any bubbles, 15 ml of sea-
water was extruded and the syringe re-sealed, retaining 35 ml of
treatment seawater. Syringes were then stored overnight in a tem-
perature controlled seawater baths appropriate for the given ex-
periment (8.5, 13, or 18.5C, see description of experiments
below). In preliminary studies, we determined that the change in
2C. A. Boch et al.
carbonate chemistry and DO within experimental syringes was
negligible over several days.
Abalone spawning, gamete concentration determination,
and gamete density adjustment
Abalone were obtained from American Abalone Farms,
Davenport, CA, USA. For each experiment, six males (mean
shell length ¼86.4 mm 64.0 SD) and six females (mean shell
length ¼94.5 mm 65.8 SD) were conditioned in two tanks with
ﬂowing 13C seawater, 0:24 Light:Dark photoperiod, and fed
giant kelp, Macrocystis pyrifera,ad libitum for 2 weeks prior to the
day of experiment. On the day of each experiment, we separated
the male and female brooders into individual induction con-
tainers and used the tris-buffer, hydrogen peroxide, and tempera-
ture protocol to individually induce spawning (Morse et al.,
1977). We delayed the initiation of the protocol for males by
1.5h, relative to females, in order to synchronize spawning be-
tween sexes. Over the experiments, 95% of male and female aba-
lone spawned within a 1-h window.
Upon commencement of spawning, sperm and eggs from all
spawning individuals were pooled in separate 500 ml, autoclaved
glass beakers. Gamete collection was limited to 45 min after the ﬁrst
animal spawned to reduce the potential effects of gamete age on
fertilization success. To determine initial density of the pooled
sperm stock, micrographs of sperm stained with Lugol’s solution
were taken on a hemocytometer (Bright-Line, PA, USA) with an
Axioscop compound microscope (10, Zeiss, Germany, Olympus
ZH71 camera attachment and CellSens software). ImageJ (https://
imagej.nih.gov/ij/) software was then used to automatically enu-
merate sperm (n ¼2 samples from pooled sperm stock, see
Supplementary material ‘Estimation of sperm density via image
analysis’ section and Supplementary Figures S1A–C). To estimate
initial egg density, the number of eggs in 50 ml(n¼4) subsamples
of the pooled egg stock were counted on an Olympus SZH10 dis-
secting microscope. From these estimates and accounting for the
process of adding gametes into seawater treatments (ﬁnal volume
of 40 ml in each syringe, see ‘Assessment of fertilization success’
section below), sperm and egg stock densities were diluted with
control sea water to achieve a ﬁnal concentration of sperm and
eggs in each syringe of 10
and 60 ml
, respectively (see
Supplementary material ‘Determination of experimental sperm
density and exposure time’ section). During and after density ad-
justments, gamete stocks were maintained at 13C.
Figure 1. General procedure during the fertilization experiments. A. Known concentrations of sperm (white cloud) are loaded after egg
injection (white ﬂecks) into 50 ml gas-tight syringes (Tomopal, Japan) with speciﬁed pH, DO, and temperature levels. Seawater is kept water
tight in syringes with 3–way Luer Lock valve and connector. B. A ﬁnal 1 ml of seawater is injected into the syringes to ﬂush any gametes in
the valves. Then all the syringes are incubated for 600 seconds in the appropriate temperature tank. C. 20 ml out of the total 40 ml in each
syringe are loaded into a modiﬁed 50 ml syringe container to measure the pH (SentrON-Line 8100-100 ISFET probe connected by RS232 cable
to a logging computer), dissolved oxygen and temperature (NeoFox hyoxy probe, sensing patch and temperature probe connected via USB to
a logging computer). D. Sample micrograph showing unfertilized eggs (single cells indicated by red arrows) and 4-cell stage fertilized eggs
(remainder of the cells) after 600 seconds of treatment followed by 4-hours of incubation at non-stressful levels.
Effects of current and future coastal upwelling conditions 3
Assessment of fertilization success
To access fertilization success, sperm and eggs from the adjusted
density pooled gamete stocks were injected into 50 ml experimen-
tal syringes pre-loaded with volumetric mixtures of seawater
sources (see ‘Preparation of seawater treatments’ section above)
to achieve the range of pH and DO for a given experiment and
held at the appropriate temperature (8.5, 13, or 18.5C, see de-
scription of experiments below). A 2 ml of egg stock solution, fol-
lowed by 2 ml of sperm stock solution, were injected into the
experimental syringe using separate 5ml gas-tight syringes with
one-way Luer Locks (Figure 1a). Subsequently, 1 ml of control
seawater was injected from a separate 5 ml syringe to ﬂush any
gametes remaining in the Luer Lock into each experimental syr-
inge (see Supplementary Tables S1 and S2 for ﬁnal ﬂush source),
bringing the ﬁnal total volume to 40 ml. The order which experi-
mental syringes were inoculated with gametes was randomized
with the exception of controls (see descriptions of experiments
below), which were conducted at the start, ﬁnish and across regu-
lar intervals during each experiment.
After gamete injection, each experimental syringe was held in a
second temperature bath (Figure 1b, 9, 13, or 18C, see descrip-
tion of experiments below) and eggs were exposed to sperm for a
duration of 600 s (see Supplementary material ‘Determination of
experimental sperm density and exposure time’ section).
Subsequent to the exposure period, 20 ml of each experimental
syringe was expunged into a 50 ml falcon tube, with the bottom
removed and replaced with Nitex 90 mm mesh, then immersed re-
peatedly in control seawater (pH 7.95, DO 6.0 mg/l, 13C) to
wash away excess sperm and terminate the exposure period. Each
tube was held in bins ﬁlled with ﬂowing control seawater for 4 h,
at a depth that maintained each tube approximately two-third
ﬁlled, to allow fertilized eggs to develop. Samples were then
washed into 20 ml glass scintillation vials and ﬁxed with 500 mlof
10% formalin. For each sample, >100 cells were examined using
an Olympus SZ40 dissecting microscope to access proportional
fertilization success, with eggs reaching a two-cell stage or greater
counted as fertilized (Figure 1d).
Measurement of post-fertilization experimental
syringe pH and DO
The remaining 20 ml of sample was held in the sealed experimen-
tal syringe (<3h) at 13
C until pH and DO were measured.
A SentrON-Line 8100-100 ISFET probe (Sentron, NL) was used
to estimate pH (total scale) and a NeoFox spectrometer
(OceanOptics, FL, USA) system to measure DO and temperature
(Figure 1c, for details see supplementary material ‘Determination
of post fertilization experimental syringe pH and DO’ section).
Effects of ocean acidiﬁcation and hypoxia on
The range of current environmental variability along the central
California coast within the CCLME was used to determine the
range of treatments levels for pH, DO, and temperature over
which abalone fertilization success was measured. Records of
temperature, DO, and pH from a depth of 6 m off Hopkins
Marine Station were made during the spring upwelling period
(observations in April–June, 2013 season) (Figure 2), indicating
extremes during upwelling conditions of pH 7.5, DO 60 mmol/
kg SW (approximately 2.0 mg/l DO), and 9C. Although it is
difﬁcult to predict the time scales and magnitude of changes in
the composition of upwelled waters in response to rising atmos-
(Feely et al., 2004,2008) we used a pH offset of -
0.3 pH (reference for global surface water pH change) units
below the current upwelling minimum as a pH minimum (7.2)
for our experiments. Thus, to assess the effects of current up-
welling conditions, as well as scenarios reﬂecting future acidiﬁ-
cation and deoxygenation, we evaluated fertilization success
across 40 levels of pH ranging from 7.95 to 7.2 pH crossed
with two DO concentrations representing normoxic and hyp-
oxic conditions (5.7 and 1.97 mg/l DO, respectively) at 13C.
Effects of acidiﬁcation and ocean warming on
To assess the effects of pH and temperature typical for present-
day upwelling (Figure 2b, pH 7.5, 9C) and future ocean acidiﬁ-
cation and warming scenarios (pH 7.2–7.5, 18C), we evaluated
fertilization success across a range of pH, 7.95 to 7.2 pH,
across two temperatures (n ¼45 for 9 and 18C). It is important
to note that experimental syringes were stored at 8.5, and 18.5C
overnight prior to inoculation (see ‘Preparation of seawater treat-
ments’ section) in order to achieve targeted temperatures (9, and
Figure 2. pH, dissolved oxygen, and temperature (1 m above the
bottom, 25 April, 2013 – 14 June, 2013) from a near shore upwelling
zone (6 m depth; 36.624 N; 121.905 W). A. pH (total scale)
versus temperature (SeaFET pH sensor, Satlantic, Halifax, Canada). B.
Dissolved oxygen versus temperature (SBE 16 CTDO, Sea-Bird
Electronics, Inc., Washington, USA). The extreme pH and dissolved
oxygen and associated temperature during upwelling at this site are
noted in both panels.
4C. A. Boch et al.
18C, respectively) while accounting for the temperature of the
seawater used in the process of adding gametes (13C).
Subsequently, experimental syringes were held at 9 and 18C
(Figure 1b), as appropriate, during the period of gamete exposure
To evaluate proportional fertilization success as a function of
measured pH in combination with DO or temperature groups,
response data were logit transformed and evaluated for any sig-
niﬁcant response to pH, DO, and temperature using Generalized
Linear Models (GLMs) in R (Warton and Hui, 2011).
Homogeneity of the data was assessed via visual inspection of re-
siduals versus GLM predicted logit transformed data
(Supplementary Figure S3). These GLM models were then incor-
porated into Segmented Model analysis to determine the exist-
ence of a threshold or a breaking point, and any changes in the
response slopes as a function of pH and any additional factors
(Muggeo, 2003,2008). Additional comparison of GLM and
Segmented Model ﬁts were assessed using corrected Akaike
Information Criterion (AICc) with a reduction of AICc >10 by
the Segmented Model used as a conservative estimate for report-
ing a signiﬁcant improvement relative to the GLM and further
evidence for non-linearity and a possible threshold.
Seawater treatment measurements
Overall, the mean difference between predicted pH and measured
pH was 0.03 60.03 pH units for the ocean acidiﬁcation and hyp-
oxia experiment and 0.08 60.06 pH units for the ocean acidiﬁca-
tion and ocean warming experiment. For the former experiment,
the mean DO for the control group was 5.70 60.35 mg/l DO,
5.72 60.35 mg/l for the High DO group, and 1.97 60.33 mg/l
for the hypoxic Low DO group. For the latter experiment, the
temperature in the incubation bins during the fertilization time
remained stable at 13.6, 9.3, and 17.9C for the Control, Low,
and High Temperature groups (Table 1). Measured pH showed a
high correlation with predicted pH values from CO
SYS for both
experiments with r
¼0.99 and r
¼0.91 (Figure 3a and b). The
value in the second regression experiment is attributed
to 3 pH outliers that were likely caused by inconsistent volume
mixture during preparation.
Fertilization response to ocean acidiﬁcation and hypoxia
Overall, fertilization success decreased from 59 to 4% as pH
decreased from 7.9 to 7.18 for the low DO group (1.97 mg/l
DO). For the normoxic group (5.72 mg/l DO), fertilization
dropped from 58 to 3% as pH decreased from 7.84 to 7.15. In the
control group (7.81–7.89 pH, 5.70 mg/l DO), fertilization suc-
cess ranged from 59 to 38%. These results are shown in Figure 4a.
Results of the GLM model (Table 2, Model A) showed that the
pH DO interaction was not signiﬁcant, indicating that DO did
not interact with pH to affect variation in fertilization success.
Further evaluation with Segmented Model analysis revealed a sig-
niﬁcant threshold at a pH of 7.56 (60.03 SE) and a signiﬁcant
change in the intercept and slope below this threshold estimate
[Table 3(A)]. These results indicate a greater drop in fertilization
success per unit of pH change below this point. AICc comparison
Figure 3. Measured vs. predicted pH. A. Correlation between SentrOn-Line 8100-100 ISFET probe (ion-sensitive ﬁeld-effect transistor;
Sentron, Netherlands) measured pH output (total scale) versus CO2Sys predicted pH (total scale) from source water mixing at 13 C. B.
Correlation between SentrOn-Line 8100-100 ISFET probe (ion-sensitive ﬁeld-effect transistor; Sentron, Netherlands) measured pH output (y-
axis) versus CO2Sys predicted pH from source water mixing for 9, 13, and 18 C. For both panels, open circles represents each sample
treatment from the experiment (n=92 and n=108 respectively). Dashed line is the linear regression line ﬁt and the solid black line represents
1 to 1 unity. SentrON-Line 8100-100 probe was connected via RS232 cable to a PC and the data logged via DataLogger Suite software.
Table 1. Dissolved oxygen and temperature measurements from
Experiment Group n mean SD SE
A. Ocean acidiﬁcation
Control 12 5.70 0.35 0.10
Low DO 40 1.97 0.33 0.05
High DO 40 5.72 0.35 0.06
B. Ocean acidiﬁcation
Control 5 13.56 0.06 0.11
Low Temp. 5 9.30 0.25 0.02
High Temp. 4 17.90 0 0
n¼number of samples. Dissolved oxygen (mg/l) during experiment A was
measured using NeoFox Hyoxy dissolved oxygen sensor (OceanOptics, FL,
USA). Temperature (C) during experiment B was measured using Taylor
Digital Probe Thermometer 9842 (Taylor Precision Products, New Mexico,
USA). Temperature samples were taken every 20 minutes over 80 minutes
(the full duration of experiment B).
Effects of current and future coastal upwelling conditions 5
of GLM with Segmented Model ﬁts showed that the Segmented
Model was a signiﬁcant improvement over the GLM (SM, Table
3, AICc >10) indicating a non-linear response of fertilization
success to decreasing pH.
Fertilization response to ocean acidiﬁcation and
Similar to the ocean acidiﬁcation and hypoxia experiment at
13C, fertilization rates decreased with decreasing pH when gam-
etes were exposed to the upwelling-like temperature of 9C
(Figure 4b). At 9C, fertilization decreased from 65 to 6% as
pH decreased from 7.85 to 7.14, with the Segmented Model ana-
lysis indicating a threshold at a pH of 7.52 (60.02 SE), below
which with a much steeper reduction in fertilization occurred. In
contrast to 9 and 13C treatment and an equivalent range of pH
exposure, fertilization success decreased from 74 to 21% as pH
decreased from 7.95 to 7.1 at 18C(Figure 4c). The results from
the GLM evaluation showed a signiﬁcant increase in the intercept
and a decrease in the slope when the 18C exposure interacts with
the range of pH examined (Table 2B,p<0.001). Further evalu-
ation with the Segmented Model analysis showed a lack of break-
ing point or a signiﬁcant change in the intercept and slope at the
warmer temperature [Table 3(C)] thus indicating the linear pre-
diction evaluated by GLM model is an appropriate predictor of
fertilization rates at this temperature. AICc comparison of GLM
with Segmented Model ﬁts showed that the Segmented Model
was not a signiﬁcant improvement over the GLM predictions
supporting the results of the Segmented Model analysis
(Supplementary Table S3, AICc <10).
This is the ﬁrst evidence of direct and interactive effects of vari-
ation in pH, DO and temperature on fertilization success in red
abalone. Speciﬁcally, the results indicate that: (i) fertilization gen-
erally decreases with declining pH, with the presence of a thresh-
old and the difference in the rate of change in fertilization success
dependent on the temperature and pH interaction; (ii) warming
above ambient temperature interacts with pH, and ameliorates
the negative impact of low pH; (iii) DO has no discernable effect
on fertilization success, at least within the range of oxygen vari-
ation investigated in this study.
Both negative and resistance to low pH on marine inverte-
brate fertilization success have been reported in the literature
but these diverse outcomes are likely due to the range of pH
examined and to the limited understanding of the fertilization
mechanism that is being affected. Similar to our study, reduc-
tions in fertilization success with decreasing pH have been re-
ported for several species of molluscs and echinoderms
(Kurihara and Shirayama, 2004;Moulin et al., 2011;Barros
et al., 2013;Frieder, 2014;Scanes et al., 2014). In these studies,
low fertilization success was evident as gametes were exposed to
pH levels below 7.6. In contrast, fertilization success remained
high with decreasing pH for echinoderms (Heliocidaris tubercu-
lata, Heliocidaris erythrogramma, Tripneuestes gratilla,
Centrostephanus rodgersii, Patirriella regularis) and the abalone
Haliotis coccoradiata (Byrne et al., 2010). Although fertilization
success in these organisms was found to be resistant when
exposed to pH levels ranging from 8.2 to 7.6, the authors sug-
gested that this resistance might change under more severe levels
of pH. In a mechanistic context, these previous studies suggested
Figure 4. Abalone fertilization response to multiple stressors. A.
Open circles represent proportional fertilization at pH (total scale)
ranging from 7.9 to 7.2 and 6.0 mg/l dissolved oxygen. Black
solid dots represent responses to pH ranging from 7.9 to 7.2 and
1.5 mg/l dissolved oxygen. For all the treatments in this experiment,
temperature was maintained at 13 C. Solid black line represents
segmented model ﬁt with dashed lines representing lower and upper
95% conﬁdence limits. B. Proportional fertilization success (solid
blue circles) to pH ranging from 7.9 to 7.2 and 9 C. Solid black
line represents segmented model ﬁt with dashed lines representing
lower and upper 95% conﬁdence limits. C. Proportional fertilization
success (squares) to pH ranging from 7.9 to 7.2 and 18 C. Solid
black line represents GLM ﬁt with dashed lines representing lower
and upper 95% conﬁdence limits. For panels B-C, dissolved oxygen
was maintained at normoxic levels. For all panels,y-axis represent
proportional fertilization success and x-axis represent the measured
pH in each experimental sample (n¼92 for C and n¼108 for C). For
panels A and B, black solid circles represent break-point estimates
with error bars.
6C. A. Boch et al.
that lower pH alters sperm swimming behavior and or sperm
kinetics, ultimately negatively affecting fertilization outcomes.
For example, a reduction of sperm swimming speed and percent
motility was found to be signiﬁcantly correlated with reduced
fertilization success for H. erythrogramma at a pH level of 7.7
(Havenhand et al., 2008). However, these results become con-
founding when compared with the negligible effects of pH re-
ported by Byrne et al. (2010) for the same species and therefore,
indicate that fertilization processes may be more complex. For
example, changes in pH may also affect sperm attractant chem-
icals released by eggs (Riffell et al., 2002), the activation process
during egg fertilization via interference of lysine dissolution of
the egg membrane (Kresge et al., 2001), or the increase in H þ
may ionically interfere with the explosive wave of calcium neces-
sary for signalling egg activation and initiation of mitotic div-
ision (reviewed by Whitaker, 2006). Thus, while there is
evidence for negative effects of low pH on fertilization success,
the underlying physical or biological mechanisms require further
evaluation—e.g. via biochemical tracing experiments—to fully
understand when pH induces negative versus resistant
The ameliorating effect of warming over the negative effects of
pH below a threshold point has not been previously reported in
invertebrate fertilization studies but this may be due to differ-
ences in our experimental design relative to prior studies. For ex-
ample, in order to evaluate the effects of changing temperature
and pH, Byrne et al. (2010) examined fertilization success with a
4-degree warming from ambient (20C) coupled with 0.6 U de-
crease in pH from ambient (8.2 pH) for the tropical abalone H.
coccoradiata and a 6-degree warming from ambient (20C)
coupled with 0.6 U decrease in pH from ambient (8.2 pH) for
several species of echinoids. Under these conditions, fertilization
success was found to be resistant to both drivers and without ap-
parent interaction—i.e. fertilization rates remained high under all
conditions. Haliotis coccoradiata and the echinoids examined by
Byrne et al. (2010) are distributed at latitudes where near-shore
temperatures average 20C and where optimal fertilization suc-
cess in these organisms have demonstrated to be 20C(Wong
et al., 2010) and as such, an experimental temperature exposure
of 18–26C may not adequately capture the interactive effects of
warming temperature and lower pH. That is, an ameliorating ef-
fect may be only observed if the experimental treatments include
both warming and cooling comparisons of the same magnitude
from the optimal temperature in combination with a fuller range
of pH. Thus, experiments that examine co-variates moving in
both directions from the optimal may reveal different patterns of
multiple driver effects.
The negligible effect of low DO or hypoxia on fertilization
observed in this study was unexpected. Oxygen is a critical driver
of metabolic processes at multiple organismal scales. As oxygen is
the terminal electron acceptor during mitochondrial energy pro-
duction, loss of available oxygen would be expected to reduce the
metabolic energy needed for ﬂagellum activity or sperm propul-
sion. Thus, a reduction in sperm motility is expected to have
negative consequences for sperm:egg interactions and ultimately
fertilization success. For example, low levels of DO have been re-
ported to reduce sperm swimming kinetics in marine species
(Shin et al., 2014;Graham et al., 2016). However, as the overall
role of oxygen in the metabolism involves creating a proton gra-
dient, a decrease in seawater pH, or related changes in seawater
carbonate parameters, may disrupt this proton gradient, or the ef-
fects of pH may dominate any effects of oxygen variation, at least
over the scales examined. Indeed, Graham et al. (2016) observed
an increase in the swimming speed of sea urchin sperm under
lower pH and hypoxic conditions. However, despite the antagon-
istic interactive effects of pH and hypoxia on sperm motility, they
also observed a synergistic decrease in fertilization under these
combined drivers. Those results differ from our ﬁndings and may
be indicative of differing species-speciﬁc responses. Furthermore,
seawater conditions to which our adult brooders were acclimated
to during reproductive development—which we did not
characterize—may also inﬂuence fertilization outcomes. Negative
impacts of seawater conditions during this phase are unlikely be-
cause the aquaculture tanks are highly aerated and the brooders
are cultured separately and under low densities to minimize oxi-
dative stress (Boch, pers. commun. with American Abalone
Farms). Thus, while it may be possible that the effects of seawater
pH may be greater than the effects of DO, future experiments
Table 2. Statistical results for proportion fertilized in each experiment.
Model Estimate SE z-value p-value
A. Model for pH and DO experiment: (Intercept) 24.69 1.16 21.22 ***
y¼pH þDO Group þpH * DO Group þepH 3.19 0.16 20.41 ***
Low DO 1.66 1.61 1.03 NS
pH x Low DO 0.23 0.22 1.07 NS
Null Deviance: 1316.42 (79 d.f.)
Residual Deviance: 464.05 (76 d.f.)
B. Model for pH and temperature experiment: (Intercept) 20.83 0.89 23.37 ***
y¼pH þTemp. Group þpH * Temp. Group þepH 2.67 0.12 22.46 ***
High Temp. 16.75 1.13 14.88 ***
pH x High Temp 2.13 0.15 14.15 ***
Null Deviance: 2829.6 (89 d.f.)
Residual Deviance: 1514.4 (86 d.f.)
A. GLM model evaluation of pH and dissolved oxygen effects with 5.9 mg/l dissolved oxygen group as the reference (surface water) data and 1.9 mg/l group as
the comparative hypoxic group. Both data are constant at 13 C. B. GLM evaluation for pH and temperature dual stressor experiment with 9 C group (upwell-
ing) as the reference temperature and 18 C data as the comparative group (ocean warming). y= proportional fertilization response; e¼error term; DO ¼dis-
solved oxygen; Temp. ¼temperature; * ¼p<0.05; ** ¼p<0.01; *** ¼p<0.001.
Effects of current and future coastal upwelling conditions 7
should control for conditions during reproductive development
to limit pre-existing exposures and to clarify experimental
Prevailing theory based on aerobic scope suggests that expos-
ure to anomalously high temperature and a secondary driver such
as decreasing pH conditions would have a synergistic or an addi-
tive effect on fertilization success—i.e. dual stressors are predicted
to narrow the window of biological performance (Portner and
Farrell, 2008). However, our study shows that the effects of pH
can be dominant over a secondary stressor such as hypoxia and in
addition, the effects of ocean warming can ameliorate the effects
of decreasing pH for fertilization success. Furthermore, it is
unclear how warming temperatures in combination with hypoxic
exposure may affect fertilization success. Altogether, our experi-
mental results suggest that a synergistic or an additive response
may not adequately describe the full scope of biological perform-
ance under multiple drivers or stressors—at least in the context
of fertilization success. Based on our and other experimental
studies on multiple environmental drivers, we instead suggest
that the effects of multiple drivers can be complex and lead to re-
sistance, dominance or amelioration. Importantly, our results
show that non-linear responses and thresholds can also occur,
highlighting the need to examine biological responses across con-
tinuous stressor gradients.
Table 3. Break-Point estimation.
A. Ocean acidiﬁcation and hypoxia (all data)
Segmented model y¼pH þUþpsi þe
Estimate SE z-value p-value
(Intercept) 32.78 1.68 19.55 ***
pH 4.28 0.23 18.85 ***
U3.10 0.47 6.67 NA
Null seviance: 1316.42 (79 d.f.)
Residual deviance: 413.25 (76 d.f.)
Segmented model BP estimate 7.56 60.03 SE
Davies test BP estimate 7.56***
Estimate SE LCI (95%) UCI (95%)
Slope segment 1 4.29 0.23 3.83 4.74
Slope segment 2 1.18 0.41 0.37 1.99
B. Ocean acidiﬁcation and warming (9 C dataset)
Segmented model y¼pH þUþpsi þe
Estimate SE z-value p-value
(Intercept) 35.62 2.20 16.19 ***
pH 4.69 0.30 15.69 ***
U4.13 0.49 8.36 NA
Null deviance: 1094.28 (44 d.f.)
Residual deviance: 495.05 (41 d.f.)
Segmented model BP estimate 7.52 60.02 SE
Davies test BP estimate 7.52***
Estimate SE LCI (95%) UCI (95%)
Slope segment 1 4.69 0.30 4.08 5.29
Slope segment 2 0.56 0.39 0.24 1.35
C. Ocean acidiﬁcation and warming (18 C dataset)
Segmented model y¼pH þUþpsi þe
Estimate SE z-value p-value
(Intercept) 2.44 1.01 2.41 *
pH 0.32 0.14 2.33 *
U 1.69 0.71 2.39 NA
Null deviance: 977.09 (44 d.f.)
Residual deviance: 933.94 (41 d.f.)
Segmented model BP estimate 7.72 60.06 SE
Davies test BP estimate 7.73*
Estimate SE LCI (95%) UCI (95%)
Slope segment 1 0.32 0.14 0.04 0.6
Slope segment 2 2.01 0.70 0.61 3.42
For each experiment, data are evaluated according to Table 2 GLM results with Segmented Model regression and Davies Test Breaking-Point estimation
(Muggeo, 2008). A. Results for ocean acidiﬁcation + hypoxia experiment data evaluated as a single dataset. B. Results for ocean acidiﬁcation + 9 C experiment
data evaluated as an independent dataset. C. Results for ocean acidiﬁcation + 18 C experiment data evaluated as an independent dataset. U ¼difference in
slopes of the two segments; psi = breaking point estimate at each step with standard error; e¼error term; BP ¼Breaking Point; S.E. ¼6standard error; NA ¼
Not Applicable. LCI ¼Lower Conﬁdence Interval; UPI ¼Upper Conﬁdence Interval; * ¼p<0.05; ** ¼p<0.01; *** ¼p<0.001.
8C. A. Boch et al.
Understanding the inﬂuence of multiple co-varying environ-
mental factors on the success of red abalone populations or simi-
lar marine organisms with complex life cycles requires that we
disentangle the individual and combined effects of variation in
key environmental drivers. In addition, it requires an understand-
ing of the net outcomes that integrate the vulnerability of each
life history stage. For example, although we found that fertiliza-
tion rates increased with higher temperatures and decreased with
upwelling-like conditions, H. rufescens have been found to have
higher gonadal development under cooler phases of the
California current (Vilchis et al., 2005). Furthermore, male aba-
lone exhibited a signiﬁcant reduction of sperm production after 6
months of exposure to 18C(Rogers-Bennett et al., 2010). Thus,
if abalone populations, which have been observed to be gravid
from a few months to year-round (Boolootian et al., 1962) re-
spond to selection from the positive, negative, and other effects of
environmental variation on gamete production and fertilization,
the net demographic outcome may either be lessened or ampliﬁed
by the ensemble of environmental drivers acting differentially on
each life stage.
Our integrated approach to examine the effects of multiple en-
vironmental drivers provide new insights concerning the expected
consequences of future changes in ocean conditions for abalone
populations that were unlikely to be detected in single factor ex-
periments. For the CCLME, our results suggest that red abalone
fertilization success is at a possible tipping point under current
upwelling events—i.e. 7.5 pH and 9–13C. Our results also sug-
gest that the effects could be further detrimental to fertilization
success if ocean acidiﬁcation causes a further reduction in sea-
water pH below 7.5. As gaps in our understanding remain, ex-
panding integrated approaches will be critical to disentangle the
effects of climate change and natural variability in multiple envir-
onmental drivers as we endeavor to predict and manage changes
in marine populations.
Supplementary material is available at the ICESJMS online ver-
sion of the manuscript.
We are grateful to Kurt Buck, Patrick Whaling, Dale Graves,
Joshua Lord, Jody Beers, Peter Hain, Tom Ebert, and numerous
volunteers who helped with the experiments.
This study was supported by the US NSF-OA Programme (award
no. OCE-1416934) and through the US NSF-CNH Programme
(award no. DEB-1212124), and support from the David and
Lucile Packard Foundation.
Babcock, R., and Keesing, J. 1999. Fertilization biology of the abalone
Haliotis laevigata: laboratory and ﬁeld studies. Canadian Journal
of Fisheries and Aquatic Sciences, 56: 1668–1678.
Baker, M. C., and Tyler, P. A. 2001. Fertilization success in the com-
mercial gastropod Haliotis tuberculata. Marine Ecology Progress
Series, 211: 205–213.
Bakun, A. 1990. Global climate change and intensiﬁcation of coastal
upwelling. Science, 247: 198–201.
Bakun, A., Black, B. A., Bograd, S. J., Garc
ıa-Reyes, M., Miller, A. J.,
Rykaczewski, R. R., and Sydeman, W. J. 2015. Anticipated effects
of climate change on coastal upwelling ecosystems. Current
Climate Change Report, 1: 85–93.
Barros, P., Sobral, P., Range, P., Chicharo, L., and Matias, D. 2013.
Effects of sea-water acidiﬁcation on fertilization and larval devel-
opment of the oyster Crassostrea gigas. Journal of Experimental
Marine Biology and Ecology, 440: 200–206.
Barry, J. P., Lovera, C., Okuda, C., Nelson, E., and Pane, E. 2008. A
gas-controlled aquarium system for ocean acidiﬁcation studies.
OCEANS 2008 – MTS/IEEE Kobe Techno-Ocean, Kobe. pp. 1–5.
Bograd, S. J., Castro, C. G., Di Lorenzo, E., Palacios, D. M., Bailey,
H., Gilly, W., and Chavez, F. P. 2008. Oxygen declines and the
shoaling of the hypoxic boundary in the California Current.
Geophysical Research Letters, 35: L12607.
Boolootian, R. A., Farmanfarmaian, A., and Giese, A. C. 1962. On the
reproductive cycle and breeding habits of two western species of
Haliotis. Biological Bulletin, 122: 183–193.
Booth, J. A. T., McPhee-Shaw, E. E., Chua, P., Kingsley, E., Denny,
M., Phillips, R., Bograd, S. J., et al. 2012. Natural intrusions of
hypoxic, low pH water into nearshore marine environments on
the California coast. Continental Shelf Research, 45: 108–115.
Byrne, M., Soars, N. A., Ho, M. A., Wong, E., McElroy, D.,
Selvakumaraswamy, P., Dwojanyn, S. A., and Davis, A. R. 2010.
Fertilization in a suite of coastal marine invertebrates from SE
Australia is robust to near-future ocean warming and acidiﬁca-
tion. Marine Biology, 157: 2061–2069.
Byrne, M. 2011. Impact of ocean warming and ocean acidiﬁcation on
marine invertebrate life history stages: vulnerabilities and poten-
tial for persistence in a changing ocean. Oceanography and
Marine Biology: An Annual Review, 49: 1–42.
Cai, W., Borlace, S., Lengaigne, M., Van Rensch, P., Collins, M.,
Vecchi, G., Timmermann, A., et al. 2014. Increasing frequency of
extreme El Nino events due to greenhouse warming. Nature
Climate Change, 4: 111–116.
Caldeira, K., and Wickett, M. E. 2003. Anthropogenic carbon and
ocean pH. Nature, 425: 365.
Chan, F., Barth, J. A., Lubchenco, J., Kirincich, A., Weeks, H.,
Peterson, W. T., and Menge, B. A. 2008. Emergence of anoxia in
the California Current Large Marine Ecosystem. Science, 319: 920.
Connolly, T. P., Hickey, B. M., and Cochian, W. P. 2010. Processes
inﬂuencing seasonal hypoxia in the northern California Current
System. Journal of Geophysical Research, 115: C03021.
Crain, C. M., Kroeker, K., and Halpern, B. S. 2008. Interactive and
cumulative effects of multiple human stressors in marine systems.
Ecology Letters, 12: 1304–1315.
Deutsch, C., Brix, H., Ito, T., Franzel, H., and Thompson, L. 2011.
Climate-forced variability of ocean hypoxia. Science, 333:
Feely, R. A., Sabine, C. L., Lee, K., Berelson, W., Kleypas, J., Fabry, V.
J., and Millero, F. J. 2004. Impact of anthropogenic CO
system in the oceans. Science, 305: 362–366.
Feely, R. A., Sabine, C. L., Hernandez-Ayon, J. M., Ianson, D., and
Hales, B. 2008. Evidence for upwelling of corrosive “acidiﬁed”
water onto the Continental Shelf. Science, 320: 1490–1492.
Frieder, C. 2014. Present-day nearshore pH differentially depresses
fertilization in congeneric species. Biological Bulletin, 226: 1–7.
Gattuso, J. P., Magnan, A., Bille, R., Cheung, W. W. L., Howes, E. L.,
Joos, F., Allemand, D., et al. 2015. Contrasting futures for ocean
and society from different anthropogenic CO
arios. Science, 349: aac4722.
Gazeau, F., Parker, L. M., Comeau, S., Gattuso, J. P., and O’Connor,
W. A. 2013. Impacts of ocean acidiﬁcation on marine shelled mol-
luscs. Marine Biology, 160: 2207–2245.
Graham, H., Rastrick, S. P. S., Findlay, H. S., Bentley, M. G.,
Widdicombe, S., Clare, A. S., and Caldwell, G. S. 2016. Sperm
motility and fertilization success in an acidiﬁed and hypoxic en-
vironment. 73: 783–790.
Effects of current and future coastal upwelling conditions 9
Halpern, B. S., McLeod, K. L., Rosenberg, A. A., and Crowder, L. B.
2008. Managing for cumulative impacts in ecosystem-based man-
agement through ocean zoning. Ocean and Coastal Management,
Havenhand, J. N., Buttler, F. R., Thorndyke, M. C., and Williamson,
J. E. 2008. Near-future levels of ocean acidiﬁcation reduce fertil-
ization success in a sea urchin. Current Biology, 18: R651–R652.
Huchette, S. M. H., Soulard, J. P., Koh, C. S., and Day, R. W. 2004.
Maternal variability in the blacklip abalone, Haliotis rubra leach
(Mollusca: Gastropoda): effect of egg size on fertilization success.
Aquaculture, 231: 181–195.
Keeling, R. F., Kortzinger, A., and Gruber, N. 2010. Ocean deoxygen-
ation in a warming world. Annual Review of Marine Science, 2:
Kim, T. W., Barry, J. P., and Micheli, F. 2013. The effects of intermit-
tent exposure to low-pH and low-oxygen conditions on survival
and growth of juvenile red abalone. Biogeosciences, 10:
Kresge, N., Vacquier, V. D., and Stout, C. D. 2001. Abalone lysine:
the dissolving and evolving sperm protein. BioEssays 23: 95–103.
Kroeker, K. J., Kordas, R. L., Crim, R. N., and Singh, G. G. 2010.
Meta-analysis reveals negative yet variable effects of ocean acidiﬁ-
cation on marine organisms. Ecology Letters, 13: 1419–1434.
Kroeker, K. J., Kordas, R. L., Crim, R., Hendriks, I. E., Ramos, L.,
Singh, G. S., Duarte, C. M., and Gattuso, J. P. 2013. Impacts of
ocean acidiﬁcation on marine organisms: quantifying sensitivities
and interaction with warming. Global Change Biology, 19:
Kurihara, H., and Shirayama, Y. 2004. Effects of increased atmos-
on sea urchin early development. Marine Ecology
Progress Series, 274: 161–169.
Lee, T., and McPhaden, J. 2010. Increasing intensity of El Ni~
no in the
central-equatorial Paciﬁc. Geophysicla Research Letter, 37:
Micheli, F., Saenz-Arroyo, A., Greenley, A., Vazquez, L., Montes, J. A.
E., Rossetto, M., and De Leo, G. A. 2012. Evidence that marine re-
serves enhance resilience to climatic impacts. PLoS One, 7:
Morse, D. E., Duncan, H., Hooker, N., and Morse, A. 1977.
Hydrogen peroxide induces spawning in mollusks, with activation
of prostaglandin endoperoxide synthase. Science, 196: 298–300.
Moulin, L., Catarino, A. I., Claessens, T., and Dubois, P. 2011. Effects
of seawater acidiﬁcation on early development of the intertidal
sea urchin Paracentrotus lividus (Lamarck 1816). Marine
Pollution Bulletin, 62: 48–54.
Muggeo, V. M. R. 2003. Estimating regression models with unknown
break-points. Statistics in Medicine, 22: 3055–3071.
Muggeo, V. M. R. 2008. Segmented: an R package to ﬁt regression
models with Broken-Line Relationships. R News, 8/1: 20–25.
Orr, J. C., Fabry, V. J., Aumont, O., Bopp, L., Doney, S. C., Feely, R.
A., Gnanadesikan, A., et al. 2005. Anthropogenic ocean acidiﬁca-
tion over the twenty-ﬁrst century and its impact on calcifying or-
ganisms. Nature, 437: 681–686.
Portner, H. O., and Farrell, A. P. 2008. Physiology and climate
change. Science, 322: 690–692.
Riffell, J. A., Krug, P. J., and Zimmer, R. K. 2002. Fertilization in the
sea: the chemical identity of an abalone sperm attractant. The
Journal of Experimental Biology, 205: 1439–1450.
Rogers-Bennett, L., Dondanville, R. F., Moore, J. D., and Vilchis, L. I.
2010. Response of red abalone reproduction to warm water, star-
vation, and disease stressors: implications of ocean warming.
Journal of Shellﬁsh Research, 29: 599–611.
Sabine, C. L., Feely, R., Gruber, N., Key, R. M., Lee, K., Bullister, J. L.,
Wanninkhof, R., et al. 2004. The oceanic sink for anthropogenic
. Science, 305: 367–371.
Scanes, E., Parker, L. M., O’Connor, W. A., and Ross, P. M. 2014.
Mixed effects of elevated pCO
on fertilization, larval and juvenile
development and adult responses in the mobile subtidal scallop
Mimachlamys asperrima (Lamarchk, 1819). PLoS One, 9: e93649.
Shepherd, S. A., Turrubeates-Morales., and Hall, K. 1998. Decline of
the abalone ﬁshery at La Natividad, Mexico: Overﬁshing or cli-
mate change? Journal of Shellﬁsh Research, 17: 839–846.
Shin, P. K. S., Leung, J. Y. S., Qiu, J. W., Ang, P. O., Chiu, J. M. Y.,
Thiyagarajan, V., and Cheung, S. G. 2014. Acute hypoxic exposure
affects gamete quality and subsequent fertilization success and
embryonic development in a serpulid polychaete. Marine
Pollution Bulletin, 85: 439–445.
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Avery, K.
B., Tigonor, M., and Miller, H. L. Eds. 2007. Contribution of
Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change. Cambridge
University Press, Cambridge, United Kingdom and New York,
Somero, G. N., Beers, J. M., Chan, F., Hill, T. M., Klinger, T., and
Litvin, S. Y. 2016. What changes in the carbonate system, oxygen,
and temperature portend for the Northeastern Paciﬁc Ocean: A
physiological perspective. BioScience, 66: 14–26.
Stramma, L., Schmidtko, S., Levine, L., and Johnson, G. C. 2010.
Ocean oxygen minima expansions and their biological impacts.
Deep-Sea Research I, 57: 587–595.
Sydeman, W. J., Garcia-Reyes, M., Schoeman, D. S., Rykaczewski, R.
R., Thompson, S. A., Black, B. A., and Bograd, S. J. 2014. Climate
change and wind intensiﬁcation in coastal upwelling ecosystems.
Science, 345: 77–80.
Trenberth, K. E., and Hoar, T. J. 1997. El Ni~
no and climate change.
Geophysical Research Letters, 24: 3057–3060.
Vaquer-Sunyer, R., and Duarte, C. M. 2008. Thresholds of hyp-
oxia for marine biodiversity. Proceedings of the National
Academy of Sciences of the United States of America, 105:
Vilchis, L. I., Tegner, M. J., Moore, J. D., Friedman, C. S., Riser, K.,
Robbins, T. T., and Dayton, P. K. 2005. Ocean warming effects on
growth, reproduction, and survivorship of southern California
abalone. Ecological Applications, 15: 469–480.
Walter, R. K., Woodson, C. B., Leary, P. R., and Monismith, S. G.
2014. Connecting wind-driven upwelling and offshore stratiﬁca-
tion to nearshore internal bores and oxygen variability. Journal of
Geophysical Research Oceans, 119: 3517–3534.
Warton, D. I., and Hui, F. K. C. 2011. The arcsine is asinine: the ana-
lysis of proportions in ecology. Ecology, 92: 3–10.
Whitaker, M. 2006. Calcium at fertilization and in early development.
Physiology Review, 86: 25–88.
Wong, E., Davis, A. R., and Byrne, M. 2010. Reproduction and early
development in Haliotis coccoradiata (Vetigastropoda: Haliotida).
e. Invertebrate Reproduction and Development, 54: 77–87.
Handling editor: David M. Fields
10 C. A. Boch et al.