Studies on the impacts of climate change typically focus
on changes to mean conditions. However, animals live in temporally variable
environments that give rise to different exposure histories that can
potentially affect their sensitivities to climate change. Ocean
deoxygenation has been observed in nearshore, upper-slope depths in the
Southern California Bight, but how these changes compare to the magnitude of
natural O2 variability experienced by seafloor communities at short
timescales is largely unknown. We developed a low-cost and spatially
flexible approach for studying nearshore, deep-sea ecosystems and monitoring
deepwater oxygen variability and benthic community responses. Using a
novel, autonomous, hand-deployable Nanolander® with an SBE MicroCAT and camera
system, high-frequency environmental (O2, T, estimated pH) and seafloor
community data were collected at depths between 100 and 400 m off San Diego, CA,
to characterize timescales of natural environmental variability, changes in
O2 variability with depth, and community responses to O2 variability. Oxygen variability was strongly linked to tidal processes,
and contrary to expectation, oxygen variability did not decline linearly
with depth. Depths of 200 and 400 m showed especially high O2
variability; these conditions may give rise to greater community resilience
to deoxygenation stress by exposing animals to periods of reprieve during
higher O2 conditions and invoking physiological acclimation during low
O2 conditions at daily and weekly timescales. Despite experiencing high
O2 variability, seafloor communities showed limited responses to
changing conditions at these shorter timescales. Over 5-month timescales,
some differences in seafloor communities may have been related to seasonal
changes in the O2 regime. Overall, we found lower-oxygen conditions
to be associated with a transition from fish-dominated to
invertebrate-dominated communities, suggesting this taxonomic shift may be a
useful ecological indicator of hypoxia. Due to their small size and ease of
use with small boats, hand-deployable Nanolanders can serve as a powerful
capacity-building tool in data-poor regions for characterizing environmental
variability and examining seafloor community sensitivity to climate-driven
changes.
Introduction
Natural environmental variability can affect the resilience or sensitivity
of communities to climate change. Communities and species living in variable
environments are often more tolerant of extreme conditions than communities
from environmentally stable areas (Bay and Palumbi 2014). For example, in
seasonally hypoxic fjords, temporal oxygen variability influences seafloor
community beta diversity patterns and can allow certain species to live for
periods of time under average oxygen conditions that are below their
critical oxygen thresholds (Pcrit) (Chu et al., 2018). In addition, the
anthropogenic signal of deoxygenation takes a longer time to emerge in
systems with higher natural oxygen variability (Long et al., 2016; Henson et al., 2017), such as eastern boundary upwelling systems. The natural variability
of dissolved oxygen at different timescales is therefore an important
environmental factor to consider when studying the impacts of deoxygenation
on communities.
While data on shallow-water O2 and pH variability have proven valuable
for interpreting faunal exposures (Hofmann et al., 2011b; Frieder et al., 2012; Levin et al., 2015), high-frequency measurements are rare below inner
shelf depths. Specifically, datasets on organismal and community responses
to environmental variability are rare for the deep sea; however, those that
exist are informative and illustrate a dynamic environment (Chu et al., 2018; Matabos et al., 2012). Studies from NEPTUNE (the North-East Pacific
Time-Series Undersea Networked Experiments) in BC, Canada, show that even at
800–1000 m, fish behavior is linked to variations in environmental
conditions across different temporal scales including day–night and internal
tide temporalizations (Doya et al., 2014) and seasonal cycles (Juniper et al., 2013). Combined high-frequency quantitative sampling of environmental and
biological data allows for the examination of which processes shape benthic
communities (Matabos et al., 2011, 2014).
Currently, tools for studying deepwater, seafloor ecosystems include
deep-submergence vehicles (human-occupied vehicles – HOVs, autonomous underwater vehicles – AUVs, and remotely operated vehicles – ROVs), towed camera sleds, and
trawls. These approaches typically require significant resource investment
and the use of large ships with winch capabilities. Moorings and cabled
observatories are also very useful; however, these are usually fixed to
specific sites and are typically costly. Eddy correlation techniques are
also used to measure noninvasive oxygen fluxes at the seafloor; however, they
require ROVs or scuba divers to deploy (Berg et al., 2009).
Untethered instrumented seafloor platforms, sometimes called “ocean
landers”, have a long and rich history (Ewing and Vine, 1938; Tengberg et al., 1995). These vehicles are self-buoyant, with an expendable descent
anchor that is released by surface command or onboard timer, allowing the
vehicle to float back to the surface. Autonomous landers have several
advantages for deep-sea research, such as lower cost combined with spatial
flexibility. Unlike moorings or cabled observatories, small landers
(<2 m high) can easily be recovered using small boats and
redeployed to new depths and locations (Priede and Bagley, 2000; Jamieson,
2016).
This study focuses on oxygen variability and community composition at depths
between 100 and 400 m along the nearshore environment in the Southern California
Bight (SCB). This depth zone is of interest because it encompasses the
oxygen-limited zone (OLZ) (O2<60µmol kg-1 as
defined in Gilly et al., 2013) and supports many important recreational and
commercial fish species, including many species of slope rockfish (genus
Sebastes), which may be vulnerable to deoxygenation (Keller et al., 2015, McClatchie
et al., 2010). The OLZ is a transition zone above the oxygen minimum zone
(OMZ, O2<22.5µmol kg-1), where dissolved oxygen
levels exclude hypoxia-intolerant species. The shallow upper OMZ boundaries
likely experience more temporal variability than lower boundaries because
upwelling is a generally shallow phenomenon (<200 m), and these
waters may be more biogeochemically responsive to changes in surface
production. Thus, seafloor communities in the OLZ may be highly responsive
to short-term and seasonal changes in oxygenation.
Upper-slope depths on the US West Coast appear to be especially affected by
global trends of oxygen loss (Levin, 2018), and long-term trends have been
captured by the California Cooperative Oceanic Fisheries Investigations
(CalCOFI) quarterly measurements over the past 70 years. Off Monterey Bay in
central California, depths between 100 and 350 m saw declines in oxygen of
1.92 µmol kg-1 yr-1 between 1998 and 2013 (Ren et al., 2018).
In the SCB, oxygen declines of 1–2 µmol kg-1 yr-1 have
been reported by several studies over a period of ∼30 years
(Bograd et al., 2008, 2015; McClatchie et al., 2010; Meinvielle and Johnson,
2013), with the largest relative changes occurring at 300 m (Bograd et al., 2008). The proposed mechanisms for observed oxygen loss include increased
advection of and decreasing oxygen in Pacific Equatorial Water (PEW)
(Meinvielle and Johnson, 2013; Bograd et al., 2015; Ren et al., 2018) and
increased respiration, which is suspected to contribute more to oxygen loss
at shallower depths (<150 m) (Booth et al., 2014; Bograd et al., 2015; Ren et al., 2018). Long-term declines in pH and aragonite saturation
state have also been demonstrated for the California Current System over
similar periods using ROMS simulations (Hauri et al., 2013). Understanding
the superposition of these long-term trends on natural high-frequency
variability will be key to evaluating biotic responses.
Despite well-documented long-term trends, no published high-frequency
deepwater O2 or pH measurements are available for depths below 100 m
in the Southern California Bight (SCB). Notably, daily, weekly, and even
seasonal low-oxygen extreme events (e.g., Send and Nam, 2012) are not captured
by the quarterly CalCOFI sampling frequency. Due to the physical
oceanography and variable bathymetry of the SCB, nearshore deepwater areas
on the shelf and slope are thought to experience high variability due to
localized wind-driven upwelling events (Send and Nam, 2012) and mixing from
internal waves (Nam and Send, 2011). In addition, the California Undercurrent
transports warm, saline, low-oxygen subtropical water northward along the
coast in the SCB and varies seasonally in strength, depth, and direction
(Lynn and Simpson, 1987), likely contributing to deepwater O2
variability.
The goals of this study were the following: (i) to increase sensor accessibility to
nearshore deepwater ecosystems through the development and testing of a
small autonomous Nanolander; (ii) to characterize deepwater O2 variability over hours, days, and weeks at upper-slope depths in the SCB
relative to mean deoxygenation trends over decades and identify the dominant
timescales and depths of variability between 100 and 400 m; (iii) to describe shelf
and slope assemblages using the Nanolander camera system; and (iv) to examine
if and how the seafloor community responds to O2 variability at short
timescales (daily, weekly, seasonal) in terms of community composition and
diversity.
MethodsNanolander development and deployment
Autonomous landers have been used successfully to observe abyssal and
deep-sea trench communities (e.g., Jamieson et al., 2011; Gallo et al., 2015);
however, these landers were large and required a ship with an A frame and
winch to deploy and recover. For this study, the goal was to develop a
deepwater lander that could easily be hand-deployed out of a small boat and
that was capable of continuously collecting hydrographic and fish and
invertebrate assemblage data from near the seafloor for several weeks at a
time. With this goal in mind, the “Nanolander” deep ocean vehicle (DOV) BEEBE, was developed and built
(Global Ocean Design, San Diego, CA) (Figs. 1a, 2). DOV BEEBE is named for
William Beebe (1877–1962), who illuminated the deep-sea world during his
Bathysphere dives (Beebe, 1934).
DOV BEEBE is an autonomous, hand-deployable Nanolander capable of operating
to 1000 m of depth. It is outfitted with a Sea-Bird MicroCAT-ODO environmental
sensor for collecting high-frequency measurements of near-seafloor
temperature, oxygen, salinity, and pressure, as well as a camera and light system
for collecting videos of seafloor communities. (a) Front and side views
showing the general design for DOV BEEBE (see additional details in Fig. 2). DOV BEEBE is
shown deployed at 30 m of depth in (b) and floating at the surface in (c) prior to recovery. BEEBE can easily be deployed and recovered by hand from a
small boat by as few as two people (d, e). Examples of the field of view
from the BEEBE camera system taken from (f) D100-DM-Fall at ∼100 m
off Del Mar Steeples Reef showing various rockfish species (genus
Sebastes) and (g) D200-LJ-2 at ∼200 m near the Scripps Reserve
showing the presence of cancer crabs and chimaeras. The drop arm with the
bait bag can be seen in the camera field of view.
A detailed schematic of the Nanolander DOV BEEBE components: (1) spectra
lifting bale; (2) high-density polyethylene (HDPE) centerplate; (3) ∼25 cm polyamide spheres stacked top, middle, and bottom (see the
description in Sect. 2.1); (4) sphere retainer; (5) auxiliary ∼18 cm flotation sphere; (6) oil-filled LED lights; (7) Sea-Bird MicroCAT-ODO in
the lower payload bay; (8) central fiberglass frame; (9) stabilizing
counterweight; (10) anchor slip ring; (11) expendable iron anchor (bar bell
weights); (12) burn-wire release and mount (one port side, one starboard side);
(13) EdgeTech hydrophone for acoustic command and tracking; (14) HDPE side
panels; and (15) surface recovery flag. Not shown: drop arm on front (see
Fig. 1b and e).
DOV BEEBE stands 1.6 m tall and is 0.36 m wide and 0.36 m deep. When DOV BEEBE is deployed,
the vertical distance from the base of the Nanolander to the seafloor is
∼51 cm (Fig. 1b). This distance is defined by the length of
the anchor chain connecting the lander release system to the expendable iron
anchor and may be shortened or lengthened. The Nanolander frame is made of
marine-grade high-density polyethylene (HDPE) (brand name Starboard)
reinforced with a fiberglass pultruded channel and angle beams for structure,
reducing in-water weight. HDPE has a specific gravity of <1, close
to neutrally buoyant. The specific gravity of fiberglass is 2/3 that of
aluminum, requiring less flotation to achieve neutral buoyancy. Both
plastic materials are impervious to saltwater corrosion. Alloy 316
stainless-steel fasteners hold the frame together.
Within the frame sit three plastic spheres that are 25.4 cm in diameter; the
spheres are made of injection-molded polyamide with 15 % glass fibers for
additional strength. The novel design aspects of the Nanolander include the
use of plastic spheres for both instrument housing and flotation, which
allow the vehicle to be smaller and lighter. Previous-generation landers,
such as the landers used for the DEEPSEA CHALLENGE Expedition (Gallo et al., 2015), used
syntactic foam for flotation, which is more expensive and requires a metal
support frame. While glass spheres have a deeper maximum operational depth
(to 11 km), the use of glass-filled polyamide spheres in the Nanolander has
machining advantages and decreases the price. The plastic spheres used
for DOV BEEBE are pressure-tolerant to 1 km; new spheres with 20 % glass content
are pressure-tolerant to 2 km.
All three main spheres of DOV BEEBE are used to support the electronics and
instrumentation required for deployment, data collection, and recovery. The
upper sphere houses an EdgeTech BART (Burn Wire Acoustic Release Transponder)
board, which is the prime means of communication with the Nanolander. A
transducer is bonded to the exterior of the upper sphere, positioned to
point upwards with a clear view to the surface. The EdgeTech BART board has
four preprogrammed commands that enable and disable acoustic responses and
initiate the burn command for recovery. An overboard transducer and an
EdgeTech deck box are used to communicate acoustically with the upper sphere
and allow distance ranging on the Nanolander. The battery for the BART board
is housed in the upper sphere with the BART board.
The middle sphere functions as a “battery pod” and houses the batteries
and a battery management system (BMS) that power the two external light-emitting diode (LED) lights.
The LED lights are powered by a circuit consisting of five components:
battery > BMS > relay > LED driver > LED lights. Rechargeable lithium-polymer batteries were used
and can be recharged through an external cable and charger system without
having to open the middle sphere. For all but one deployment, a 30 Ah (ampere hour) battery stack was used to power the LED lights, which was composed
of three 14.8 v–10 Ah units. For the last deployment, power capacity was
upgraded to 32 Ah by using two 14.8 v–16 Ah batteries. In each case, each
individual battery (10 or 16 Ah) had its own BMS
with a low-voltage cutout (LVCO) to ensure that battery discharge never
went below a critical threshold (12.0 v), which would damage the battery.
This battery system is novel and was developed specifically to fit within
the spatial constraints of the sphere and provide high power capacity.
The relay and LED drivers are contained within the lower sphere, which
houses all components of the camera system and includes the viewport. The
camera system uses a Mobius Action Camera with a time-lapse assembly, which
was modified by Ronan Gray (SubAqua Imaging Systems, San Diego, CA) and
William Hagey (Pisces Design, La Jolla, CA). The SphereCam manual is
provided as Supplement 1A. The camera system has 14 different time-lapse
options, including continuous video, time-lapse images, and time-lapse video
at preprogrammed intervals. For these deployments, a sampling interval of
20 s of video every 20 min was used. A sealed magnetic switch
triggers the camera system to begin programmed sampling at a predetermined
interval. An internal light-sensitive relay, pointed towards a camera
indicator LED, triggers the external LED lights to power on. The two LED
lights are attached to the body of the Nanolander and positioned on either
side of the middle sphere. All spheres were sealed with a Deck Purge Box
(Global Ocean Design, San Diego, CA) using a desiccant cartridge to remove
moisture and were held together by a vacuum of ∼7 psi
(∼0.5 atm).
Below the bottom camera sphere, DOV BEEBE has a mounted SBE 37-SMP-ODO instrument
(Sea-Bird Scientific) with titanium housing rated to 7000 m. The MicroCAT
CT(D)-DO is a highly accurate sensor designed for moorings and other
long-duration, fixed-site deployments. It includes a conductivity,
temperature, pressure, and SBE 63 optical dissolved oxygen sensor. Initial
sensor accuracy is ±3.0µmol kg-1 for oxygen measurements,
±0.1 % for pressure measurements, ±0.002∘C for
temperature measurements, and ±0.0003 S m-1 for conductivity
measurements; drift is minimal. The SBE MicroCAT was programmed to take
samples every 5 min for the length of the whole deployment.
A drop arm is mounted on the front of DOV BEEBE and is secured with a release during
deployment. Initially a galvanic release was used, but a stack of three to four “Wint-O-Green” lifesavers was found to be more time-efficient. The drop arm
served three functions: it stabilized the Nanolander when exposed to
current, it had a ∼15 cm cross-bar for visual sizing
reference, and it was used to attach bait for each deployment. The bait used
was composed of an assortment of previously frozen demersal fishes that are
part of the SCB upper-margin demersal fish community. Bait was secured
within a mesh cantaloupe bag and secured to the drop arm with zip ties for
each deployment. All bait had been eaten by recovery.
DOV BEEBE is positively buoyant in water and is deployed with ∼18 kg
of sacrificial iron weights. The weights are attached by a sliding link onto
a metal chain, which is secured on each side to the base of the Nanolander
using a burn wire (Fig. 2). The successful release of either burn wire allows
the metal link to slide off the chain and drop the weights, releasing the
Nanolander from the bottom. DOV BEEBE's estimated descent rate is ∼100mmin-1, and the ascent rate is ∼60mmin-1, following the
release of the weights. Once at the surface, DOV BEEBE floats ∼0.45 m
above the water and has a large flag, which assists with visual detection of
the Nanolander (Fig. 1c). In addition to the three main spheres, additional
smaller spheres were used, as needed, to increase buoyancy (Fig. 2).
Map of Nanolander DOV BEEBE deployments shown in relation to local
bathymetry and nearby stations sampled quarterly by the California
Cooperative Oceanic Fisheries Investigations (CalCOFI). Green diamonds
indicate Nanolander deployments, black circles indicate CalCOFI stations
with station labels, and isobaths show 100, 200, 300, 400, and 500 m depth
contours. Note that the green diamonds labeled D200-LJ-1/2 and D100-DM-F/S
represent two deployments each, but points overlap due to proximity.
Seven deployments were conducted during the study period ranging from 15 to 35 d at targeted depths of 100–400 m (Table 1). Two early deployments
(D200-LJ-1 and D200-LJ-2) were done near the Scripps Reserve off La Jolla,
CA, and five subsequent deployments (D100-DM-Fall, D200-DM, D300-DM, D400-DM,
D100-DM-Spr) targeted a nearby rockfish habitat – the Del Mar Steeples
Reef, CA (Fig. 3). Despite relatively close spatial proximity
(∼10 km), the local bathymetry differed between the LJ and DM
deployments (Fig. 3); the LJ deployments were close to a submarine canyon
feature, while the DM deployments were on a gradually sloping margin.
Environmental and camera-based community data were collected during six of
the seven deployments; only environmental data are available from the first
deployment (D200-LJ-1) due to a camera technical problem. We aimed to
conduct repeat deployments at each site to capture seasonal differences
between a period of relaxed upwelling (fall–winter) and a period of strong
upwelling (spring–summer); however, full sampling was not feasible due to
time and equipment constraints. Consequently, only one repeat deployment is
available for ∼100 m at Del Mar Steeples Reef (D100-DM-Fall
and D100-DM-Spr).
Information for seven deployments conducted with the Nanolander
DOV BEEBE including deployment dates, length, location, depth, and environmental
conditions for each deployment, total number of 20 s video samples
available for the community analysis, and camera and light performance for
each data deployment. [O2] <60µmol kg-1 is defined
as hypoxic, [O2] <22.5µmol kg-1 is defined as
severely hypoxic, and Ωarag<1 and Ωcalc<1 are defined as undersaturated with respect to
aragonite and calcite, respectively. The mean and range O2 percent
saturation as well as pO2 (kPa) for each deployment are provided in Supplement 1B. CV: coefficient of variation (i.e., the ratio of the standard
deviation to the mean); pHest is estimated pH calculated using
empirical relationships from Alin et al. (2012).
D200-LJ-1D200-LJ-2D100-DM-FallD200-DMD300-DMD400-DMD100-DM-SprDates17 Aug–1 Sep 20177 Sep–25 Sep 201729 Sep–3 Nov 20179–29 Nov 201712 Dec 2017–5 Jan 201823 Jan–8 Feb 20188–29 Mar 2018Deployment length∼15 d∼19 d∼35 d∼20 d∼24 d∼16 d∼21 dLocationScripps CoastalReserve (32.87108∘ N, 117.26459∘ W)Scripps CoastalReserve (32.87108∘ N, 117.26457∘ W)Del MarSteeples Reef(32.93765∘ N, 117.31675∘ W)Del MarSteeples Reef(32.93762∘ N, 117.3254∘ W)Del MarSteeples Reef(32.93633∘ N, 117.33422∘ W)Del MarSteeples Reef(32.93105∘ N, 117.34875∘ W)Del MarSteeples Reef(32.93765∘ N, 117.31675∘ W)Bottom depth (m)1791789919229539998Mean temp (∘C)10.079.8811.109.518.397.429.80Temp range (∘C)9.72–10.439.45–10.4410.35–12.268.94–10.217.99–8.776.97–7.899.39–10.30CV Temp (%)1.351.692.892.111.882.021.70Mean [O2] (µmol kg-1)70.7577.61132.0082.1049.3828.97103.95[O2] range (µmol kg-1)48.82–103.8749.41–108.26110.40–156.5063.33–102.9639.89–59.3621.19–38.4191.22–123.01CV [O2] (%)13.7212.925.079.827.0210.205.72Mean pHest7.6467.6557.7597.6587.5947.5537.70pHest range7.607–7.7047.605–7.7117.713–7.8147.625–7.6997.575–7.6137.538–7.5727.671–7.732CV pHest (%)0.220.230.20.190.090.080.15Conditions hypoxic (% time)12.64 %1.88 %0 %0 %100 %100 %0 %Conditions severely hypoxic (% time)0 %0 %0 %0 %0 %1.12 %0 %Conditions undersaturated (aragonite) (% time)99.65 %99.77 %0 %100 %100 %100 %92.80 %Conditions undersaturated (calcite) (% time)0 %0 %0 %0 %0 %97.30 %0 %Number of 20 s video samples for analysisN/A10098761012406594396Number of video samples with good visibilityN/A10098766566594396Amount of time before lights first failed (h)N/A5.614.775.632.263.302.21
N/A: not available.
Characterizing environmental variability on the shelf and upper slope
Upon recovery of the Nanolander, time series data from the MicroCAT were
analyzed to assess how environmental variability (O2, T, salinity)
changes with depth. Since partial pressure of oxygen may be more
biologically meaningful than oxygen concentration for understanding animal
exposures to oxygen, we also calculated oxygen partial pressure as in
Hofmann et al. (2011a). Oxygen and pH naturally covary along the
continental margin driven by respiration. To examine the variability of
carbonate chemistry parameters, pH, Ωarag, and Ωcalc were estimated using empirical equations derived for this region
in Alin et al. (2012); Ωarag and Ωcalc are the
calcium carbonate saturation state of aragonite and calcite, respectively,
and conditions favor calcium carbonate dissolution when Ω<1.
The mean and ranges of environmental conditions were compared across depths
and deployments to characterize differences in environmental variability
that seafloor communities were exposed to over short timescales. Probability
density distributions of environmental conditions were used to visualize
differences in environmental conditions for each deployment. The coefficient
of variation (CV) (i.e., the ratio of the standard deviation to the mean) was
calculated for environmental variables for each deployment as a standardized
measure of dispersion and compared across deployments and depths.
Additionally, the percent of measurements in which conditions were hypoxic
(O2<60µmol kg-1), severely hypoxic (O2<22.5µmol kg-1), or undersaturated with respect to
aragonite (Ωarag<1) or calcite (Ωcalc<1) was determined for each deployment.
Previous studies have found that changes in oxygen and pH in the Southern
California Bight are associated with changes in the volume of Pacific
Equatorial Water (PEW) transported in the California Undercurrent (Bograd et al., 2015; Nam et al., 2015). PEW is characterized by low-oxygen, warm, high-salinity conditions and is composed of two water masses, the 13 ∘C water mass (13CW) and the deeper Northern Equatorial Pacific Intermediate
Water Mass (NEPIW) (Evans et al., 2020). Spiciness, the degree to which water
is warm and salty, is a state variable that is conserved along isopycnal
surfaces (Flament, 2002) and can be used as a tracer for PEW (Nam et al., 2015). We calculated spiciness using the “oce” R package (Kelley and
Richards, 2017) and examined how oxygen concentration varies with temperature
and spiciness across depths and deployments. Spiciness is used to examine
differences in spatial variation between water masses, which otherwise may
not be apparent using isopycnal surfaces because the effects of warm
temperature and high salinity cancel each other out. “Spicier” water is
warmer and saltier.
To identify the dominant timescale of variability for oxygen, a spectral
analysis was conducted as in Frieder et al. (2012) on the oxygen time series
for each deployment. To look at diurnal and semidiurnal patterns, 1 d
was used as the unit of time, and the number of observations based on the
sampling frequency was 288. Spectral analyses were conducted on a detrended
time series using a fast Fourier transform. Results were displayed using a
periodogram, and the period of the dominant signal was compared across
deployments. The oxygen time series for each deployment was also decomposed
using the “stats” package (R Core Team, 2019) to look at the trend, daily,
and random signals that contribute to the overall data patterns.
Short-term oxygen variability in the context of longer trends
To examine O2 variability over shorter (i.e., daily and weekly)
timescales compared to longer (i.e., seasonal, interannual, multidecadal)
timescales, we compared our Nanolander results with the annual rates of
oxygen loss reported for the SCB nearshore region (Bograd et al., 2008) as
well as conductivity–temperature–depth (CTD) casts from nearby CalCOFI station 93.3 28 (bottom depth ∼600 m; Fig. 3). CalCOFI
station 93.3 26.7 was also nearby but was too shallow for comparison with
the Nanolander deployments (Fig. 3). Quality-controlled CTD casts from
station 93.3 28 were available for a ∼16-year period (October 2003–November 2019), representing data from 61 cruises (calcofi.org). CTD
data were used to examine characteristics of the variability of temperature and
oxygen through the water column, including mean conditions, the standard
deviation, and the coefficient of variation across the 16-year period of
quarterly samples. Oxygen data at 100, 200, 300, and 400 m were extracted to
compare the distribution of observations across this 16-year period with the
high-frequency measurements from the ∼3-week Nanolander
deployments. Additionally, we tested for significant linear trends in
temperature or oxygen at 100, 200, 300, and 400 m to examine recent
(2003–2019) warming and deoxygenation trends at the CalCOFI station closest
to the Nanolander deployments.
Assessing community responses to oxygen variability
Video segments recorded by the camera system were annotated to analyze if
and how seafloor communities respond to environmental
conditions. A total of 4293 20 s video segments were collected and
annotated in total. For each 20 s video, both invertebrates and
vertebrates within the frame of view were identified to the lowest taxonomic
level and counted.
Since visibility was impaired during certain deployments due to high
turbidity, each video clip was categorized by visibility quality using the
following categories: 1 (can see the bottom, good visibility), 2 (can only
see the drop arm, poor visibility), or 3 (drop arm can no longer be seen, no
visibility). Only samples with a visibility category of 1 were utilized in
subsequent community analyses so that differences in community patterns were
not due to differences in visibility.
Nonmetric multidimensional scaling was used to assess community-level
differences across deployments. The R package “vegan” (Oksanen et al., 2017) was used for nMDS analysis, a Wisconsin double standardization was
performed, and counts were transformed using a square-root transformation.
These standardizations are frequently used when working with datasets with
high count values and have been found to improve nMDS results (Oksanen et al., 2017). Bray–Curtis dissimilarity was used as the input, and community
dissimilarities were mapped onto ordination space for the nMDS analysis.
Rare species (fewer than eight observations across all deployment samples) and
video samples with only one animal observation were removed from the
community matrix, resulting in a total number of 3357 video samples and 43
unique species included in the community analysis.
Since fishes are typically less hypoxia-tolerant than invertebrates
(Vaquer-Sunyer and Duarte, 2008), we hypothesized there would be a shift from
a fish- to an invertebrate-dominated seafloor community that correlated with
decreasing oxygen conditions. Samples from all deployments were categorized
as fish-dominant, equal, or invertebrate-dominant based on whether
there were more fishes or more invertebrates observed in each 20 s
video sample. These categories were then projected onto ordination space and
superimposed with oxygen contours using the ordisurf function in “vegan”.
Since low-oxygen conditions have been found to depress fish diversity (Gallo
and Levin, 2016), fish species accumulation curves relative to the number of
video samples were examined to look at differences in fish diversity across
deployments. We selected this metric of diversity since the number of video
samples differed across deployments (Table 1). Only video samples in which
fish were present were included in the calculation of the species
accumulation curves. A table of all fish species observed during the
deployments is included in Supplement 1C.
To test the ability of the Nanolander to capture short-term responses in
seafloor communities, we selected two deployments that had high
environmental variability (D200-LJ-2 and D200-DM). For these deployments,
samples were grouped into day (06:00–17:59 PST) and night (18:00–05:59 PST) categories as well as oxygen categories (high, intermediate, and
low). Oxygen categories were determined separately for each deployment
based on the deployment time series and were selected to showcase extremes:
high samples represented the highest 10 % of observed oxygen
conditions, and low samples represented the lowest 10 % of observed oxygen
conditions for the deployment. All other samples were categorized as
intermediate. An nMDS analysis was performed to look at differences in
communities in relation to diurnal patterns and oxygen conditions within the
timeframe of a single deployment. Rare species with fewer than three
observations across the deployment time series were removed from the
community matrices, resulting in a community matrix with 844 video samples
and 19 species for D200-LJ-1 and 645 video samples and 17 species for
D200-DM. These were used in the nMDS analysis.
ResultsNanolander performance
DOV BEEBE was found to be a reliable platform for deployment, recovery, and data
collection. Small boats were used for deployment and recovery (Fig. 1d and
e), and DOV BEEBE was easily transported by lab cart or car. The Nanolander framework
was robust and showed very few signs of wear following multiple deployments.
Spheres showed no signs of leakage or vacuum loss, and acoustic
communication worked well during all deployments.
Memory and power capacity often limit deployment times for long-term,
deep-sea deployments. In this study, the main technological limitation we
ran into was limited battery capacity to power the LED lights. As opposed to
8 h of estimated LED performance time, field performance ranged from 2.2
to 6.6 h total time, which meant that the total time of biological data
collection was shortened and ranged from 5.5 to 16.5 d, respectively
(Table 1). Memory and power were not issues for the camera system; the 128 GB micro-SD card was cleared and the battery pack was fully recharged
following each deployment. Video quality was high enough to allow
species-level identifications, and the light from the LEDs was sufficient to
light the field of view (Fig. 1f and g). The Sea-Bird MicroCAT-ODO also
performed without any issues and had sufficient battery and memory capacity
for all deployments. If not for power limitations to the LED lights, the
camera system and SBE MicroCAT would have allowed for longer sampling
(∼1 month and potentially longer). The basic Nanolander
itself can stay in situ for up to 2 years. Detailed descriptions of Nanolander
performance can be found in Gallo (2018).
Characteristics and drivers of oxygen variability across short
timescales
The natural variability of environmental parameters was assessed from time
series data collected during each deployment and compared across depths
(100, 200, 300, and 400 m) and season (fall compared to spring). Means and
ranges for temperature, oxygen, salinity, and pHest for each deployment
were determined (Table 1). At ∼100 m, conditions were never
hypoxic (i.e., <60µmol kg-1), although the mean oxygen
concentration was significantly lower during the spring upwelling season
deployment (D100-DM-Spr, mean O2=104µmol kg-1) compared
to the fall deployment when upwelling was relaxed (D100-DM-Fall, mean
O2=132µmol kg-1) (ANOVA, p<0.001); pHest
was also lower during the spring deployment (D100-DM-Spr, mean pHest=7.696) than during the fall deployment at ∼100 m
(D100-DM-Fall, mean pHest=7.759) (ANOVA, p<0.001), and
temperatures were on average 1.3 ∘C colder, consistent with
upwelling conditions (Table 1, Fig. 4). While conditions were never
undersaturated with respect to aragonite (Ωarag<1)
during the fall deployment, during the spring deployment, conditions were
undersaturated ∼93 % of the time (Table 1).
Mean and variance of near-seafloor temperature, oxygen
concentration, oxygen partial pressure, pHest, salinity, and spiciness.
The probability density of data collected for each deployment is shown, with
the color of the data distributions corresponding to each deployment (as
indicated in the color legend). The mean is indicated with a dotted line in
the same color, and exact values are given in Table 1; pHest is
estimated pH calculated using empirical relationships from Alin et al. (2012). Sampling dates for each deployment are given in Table 1.
At ∼200 m, hypoxic conditions (O2<60µmol kg-1) were encountered; however, conditions were only hypoxic for
relatively short portions of the deployment (∼13 % for
D200-LJ-1, ∼2 % for D200-LJ-2, and never hypoxic for
D200-DM) (Table 1). Conditions were almost always undersaturated with
respect to aragonite (Ωarag<1) (Table 1). At
∼300 m (D300-DM) and ∼400 m (D400-DM), mean
temperatures were colder than shallower depths, and mean oxygen and
pHest conditions were lower than shallower depths (Table 1, Fig. 4). At
both 300 and 400 m, conditions were continuously hypoxic, and at 400 m
(D400-DM) conditions were severely hypoxic (i.e., O2<22.5µmol kg-1) for ∼1 % of the time (Table 1). Both D300-DM and D400-DM were conducted during the fall–winter when
upwelling conditions are relaxed; therefore, deployments likely captured the
less extreme (higher oxygen, higher pH) conditions. At ∼300 m, conditions were undersaturated with respect to aragonite (Ωarag<1) but not calcite, whereas at ∼400 m,
conditions were also undersaturated with respect to calcite (Ωcalc<1) for most of the deployment (Table 1).
While we expected that O2 variability would decrease with depth,
instead we found that the greatest variability in oxygen conditions over
these short timescales was observed at ∼200 m (Table 1). All
three deployments from ∼200 m showed broad probability
density distributions of environmental conditions (Fig. 4) and large ranges
in oxygen and pHest for the deployment period (Table 1). The average
daily range in oxygen concentration (i.e., daily maximum–daily minimum) was
highest for D200-LJ-2 (∼34µmol kg-1), followed by
D200-LJ-1 (∼31µmol kg-1), followed by D200-DM
(∼24µmol kg-1). The average daily oxygen range for
both ∼100 m deployments was lower (∼20µmol kg-1 for D100-DM-Fall and ∼14µmol kg-1 for D100-DM-Spr). The coefficient of variation (CV) for oxygen at
∼200 m was twice as high as for the ∼100 m
deployments (Table 1). While deployments at ∼300 m (D300-DM)
and ∼400 m (D400-DM) had much narrower probability density
distributions of environmental conditions (Fig. 4), the ranges in oxygen and
pHest at ∼400 m were only slightly smaller than at
∼300 m (Table 1). The CV for oxygen was higher at
∼400 m (10.20 %) compared to ∼300 m
(7.02 %) (Table 1). The average daily range in oxygen concentration was
∼11µmol kg-1 for D300-DM and ∼8µmol kg-1 for D400-DM. Temperature did not exhibit the same
pattern of variability as oxygen, with the highest variability (CV) observed
during D100-DM-Fall (∼100 m) (Table 1). Variability in
pHest (CV) was almost twice as high at shallower depths (<200 m) as at ∼300 or ∼400 m (Table 1).
Using a spectral analysis, we found that the dominant frequency underlying
oxygen variability for all deployments was close to the semidiurnal tidal
period (∼12.4 h) (Supplement 1D). When the time series were
decomposed into their additive components (i.e., daily trend, underlying
trend, and random noise), time series for all depths showed a clear diurnal
and semidiurnal signal (Supplement 1E). Thus, oxygen variability on the
outer shelf and upper slope is mainly driven by tides. The relative
amplitude of the dominant signal in the periodogram decreases with
increasing depth, suggesting that the strength of the tidal signal weakens
with depth. Oxygen conditions tend to increase during ebb tide as the tide
retreats and decrease during flood tide as the tide rises (Supplement 1F).
Oxygen variability does not appear to increase with tidal amplitude; for
D100-DM-Spr, D200-LJ-1, and D200-LJ-2 the daily oxygen range appears to be
negatively correlated with the daily tidal range (Supplement 1F).
The 2017–2018 near-bottom dissolved oxygen concentration in
the Southern California Bight shown in relation to temperature (a) and
spiciness (b). Data points represent samples taken every 5 min with
the SBE MicroCAT-ODO sensor during the seven deployments. Deployments are
distinguished by color, as indicated in the color legend. Sampling dates for
each deployment are given in Table 1.
The oxygen concentration was found to be significantly positively correlated
with temperature for all deployments (LR, p<0.001); however, the
explanatory power of the regressions differed across depths (100, 200, 300,
and 400 m), and the slopes of the regressions differed between locations
(Scripps Reserve and Del Mar Steeples Reef). At depths deeper than 200 m,
there was less variance around the linear trend in oxygen. The highest
amount of oxygen variance explained by the linear regression with
temperature was found for D400-DM (∼400 m, R2=0.90),
and the lowest amount was for D200-DM (∼200 m, R2=0.41).
The two deployments conducted near the Scripps Reserve (D200-LJ-1 and
D200-LJ-2) had steeper slopes (Fig. 5) than deployments on the Del Mar
Steeples Reef (D100-DM-Fall, D200-DM, D300-DM, D400-DM, D100-DM-Spr), which
may be related to bathymetric differences of the sites. Deployments near the
Scripps Reserve were in a narrow, deep tendril of the Scripps canyon system,
which is surrounded by shallower bathymetry, while the Del Mar deployments
were on a gradually sloping margin (Fig. 3).
Oxygen was significantly correlated with spiciness for all deployments (LR,
p<0.001); however, the slopes and explanatory power of this
relationship differed across depths (100, 200, 300, and 400 m) and season
(fall and spring) (Fig. 5). D100-DM-Fall and D100-DM-Spr were conducted at
the same location at ∼100 m but during fall and spring,
respectively, and exhibited differing relationships between oxygen and
spiciness (Fig. 5). In the fall, the relationship between spiciness and
oxygen at ∼100 m was weak and positive with low explanatory
power (R2=0.31). In contrast, during spring, dissolved oxygen was
negatively correlated with spiciness, and the linear fit had high
explanatory power (R2=0.81). At ∼200 m (D200-LJ-1,
D200-LJ-2, D200-DM), spiciness and the oxygen concentration were also negatively
correlated, with high explanatory power for the linear fits (R2=0.98, 0.92, and 0.61, respectively) (Fig. 5). At deeper depths
(∼300 and 400 m), the relationship between spiciness and
oxygen was significant (LR, p<0.001), but the correlation was
positive with high explanatory power of the linear fit (D300-DM R2=0.61, D400-DM R2=0.68).
Seafloor community differences and relationship to oxygen conditions
Community data were collected using the camera system during six deployments
(Table 1), representing a total of 4293 20 s videos that were
annotated for organismal observations. Unexpected differences in visibility
were observed across deployments. Clear conditions were present for
deployments D200-LJ-2, D100-DM-Fall, D400-DM, and D100-DM-Spr. During D200-DM
at Del Mar Steeples Reef, visibility deteriorated throughout the deployment.
The following deployment, D300-DM, which was at ∼300 m at Del
Mar Steeples Reef, had very poor visibility. For D300-DM, less than 2 % of
samples had good visibility, 78 % had impaired visibility, and 20 % had
severely impaired visibility due to high sediment turbidity.
The community at the Del Mar Steeples Reef at ∼100 m
(D100-DM-Fall and D100-DM-Spr) was characterized by high numbers of rockfish
(Sebastes spp.), especially halfbanded rockfish (S. semicinctus), but also included less-observed
rockfish species such as the flag rockfish (S. rubrivinctus), bocaccio (S. paucispinis), rosy rockfish
(S. rosaceus), and green-striped rockfish (S. elongatus). Other commonly observed fishes included
the pink seaperch, Zalembius rosaceus, combfish, Zaniolepis spp., and the spotted cusk-eel Chilara taylori. Invertebrates
were not abundant but included an unidentified gastropod, the tuna crab,
Pleuroncodes planipes, a yellow coral, and others. Except for the singular yellow coral,
all other invertebrates were mobile. Seafloor communities for D100-DM-Fall
and D100-DM-Spr were very similar but were distinct from most other
deployments (Fig. 6a).
Seafloor community analyses using DOV BEEBE video samples. (a) Nonmetric
multidimensional scaling (nMDS) plot showing seafloor community similarity
across six deployments. Points represent the Bray–Curtis similarity of
square-root-transformed counts of animals observed in each 20 s video
sample (n=3357) from each deployment (n=6). Points are color-coded
by deployment, and a convex hull demarcates each deployment community. (b) The
same nMDS as in (a), but points are color-coded by whether the seafloor
community for each 20 s video sample was dominated by invertebrates
(blue), vertebrates (gray), or an equal proportion of vertebrates and
invertebrates (gold). Blue contours indicate a relationship with oxygen
concentration (µmol kg-1). (c) Species accumulation curves showing
differences in fish diversity across deployments. (d–g) Nonmetric
multidimensional scaling plots showing differences in seafloor community
composition as a function of day versus night (d–f) and oxygen conditions (e, g) for two deployments: D200-LJ-2 (d, e) and D200-DM (f, g). In (d) and (f) yellow points represent daytime samples (06:00–17:59) and purple
points represent nighttime samples (18:00–05:59). In (e) and (g), low-oxygen
(red) and high-oxygen (blue) conditions represent the lowest and highest
10th percentile of oxygen conditions encountered during each deployment
time series. Ellipses represent grouping by category and show 50 %
confidence limits. 2D stress is the same for (d) and (e) and for (f) and (g).
See Table 1 for camera deployment details.
Deployments D200-LJ-2 and D200-DM were in different locations (Table 1, Fig. 3), and the communities observed were very different (Fig. 6a) despite
similar depth and environmental conditions (Fig. 4). Soft sediment
characterized the benthos at both sites, but D200-LJ-2 was near a submarine
canyon, while D200-DM was on a gradually sloping margin (Fig. 3). The
community at D200-LJ-2 included eelpouts (Lycodes spp.), spotted cusk-eels (C. taylori), California
lizardfish (Synodus lucioceps), and crabs (Cancer spp.), as well as more typical deepwater species
such as Dover sole (Microstomus pacificus), spotted ratfish (Hydrolagus colliei), and dogface witch eels
(Facciolella equatorialis). In contrast, rockfish (Sebastes spp.), combfishes (Zaniolepis spp.), and Pacific sanddab
(Citharichthys sordidus) were commonly observed during D200-DM, and the community was dominated by
tuna crabs (P. planipes) and pink urchins (Strongylocentrotus fragilis), which were present in high abundances.
Conversely, during D200-LJ-2, no pink urchins were observed, and tuna crabs
were less abundant. Spot prawns (Pandalus platyceros) were common community members observed
during both D200-LJ-2 and D200-DM but were not observed during any other
deployments.
Only one deployment was conducted at each of the two deeper depths
(∼300 and 400 m), and both deployments were near the Del
Mar Steeples Reef. Due to high turbidity, the bottom was only visible in a
few samples from D300-DM. From these, it appeared that the community was
dominated by tuna crabs (P. planipes) and pink urchins (S. fragilis) though in lower abundances
than at D200-DM. Fish were rarely observed but included Pacific hake
(Merluccius productus), rockfish (Sebastes spp.), Pacific hagfish (Eptatretus stoutii), and hundred-fathom codling (Physiculus rastrelliger). D300-DM
showed similarity to the seafloor communities observed during D200-DM and
D400-DM (Fig. 6a).
D400-DM represented the deepest deployment (∼400 m) and had
excellent visibility. The community was dominated by pink urchins (S. fragilis), but
these were present in lower abundances than at D200-DM. Low numbers of tuna
crabs (P. planipes) were also present. Fish were rare, but the fishes most commonly
observed were Pacific hagfish (E. stoutii), blacktip poacher (Xeneretmus latifrons), dogface witch eels
(F. equatorialis), Dover sole (M. pacificus), and shortspine thornyhead (Sebastolobus alascanus). Both fish and invertebrates
were less active and showed less movement in D400-DM than at shallower
deployments.
We also looked at a community-level metric in relation to environmental
oxygen conditions: community dominance by invertebrates or fishes. We
hypothesized that higher-oxygen conditions would be characterized by fish
dominance compared to lower-oxygen conditions, which would be characterized
by invertebrate dominance. Deployments D200-DM, D300-DM, and D400-DM were
characterized by invertebrate dominance for either all or most (>98 %) samples. In contrast, D200-LJ-2, D100-DM-Fall, and D100-DM-Spr were
characterized by mixed communities, with fish-dominated communities more
characteristic for D100-DM-Fall and D100-DM-Spr. In general, fish-dominated
communities were more characteristic of higher-oxygen conditions when
looking across all deployments (Fig. 6b), but we could not determine if this
was specifically due to oxygen or other environmental covariates.
We were also able to examine differences in fish diversity across
deployments using the Nanolander video samples. Species accumulation curves
show differences in fish species diversity across deployments, with
D100-DM-Fall having the highest number of observed fish species, followed by
D200-LJ-2, D100-DM-Spr, D200-DM, and D400-DM, the latter two of which had the same number of
unique fish species (Fig. 6c). The decline in fish diversity between
D100-DM-Fall and D100-DM-Spr may be related to changes in environmental
conditions between fall and spring, since the location is the same (Table 1).
Community-level changes within deployments were also examined for evidence
of diurnal differences and differences related to oxygen concentration.
D200-LJ-2 and D200-DM, which exhibited the highest oxygen variability and
each had ∼14 d time series of camera samples (Table 1, Fig. 6), were selected for further analysis. Clear diurnal differences were
observed for both deployments (Fig. 6d, f), showing that at 200 m,
communities are intimately linked to diurnal rhythms. Daytime communities
were characterized by more combfishes (Zaniolepis spp.), hake (M. productus), small pelagic fishes such
as the northern anchovy (Engraulis mordax), blacktip poachers (X. latifrons), and crabs (Cancer spp.), while
nighttime communities were characterized by more lizardfish (S. lucioceps), spot prawns
(P. platyceros), spotted ratfish (H. colliei), and hagfish (E. stoutii). Tuna crabs (P. planipes) and pink urchins (S. fragilis)
showed no diurnal differences.
In contrast to clear diurnal differences, seafloor communities showed little
evidence of responsiveness to changing oxygen conditions during the two
deployments examined; however, some community-level differences do emerge
when examining the highest- and lowest-oxygen conditions that were
encountered during the deployment time series (Fig. 6e, g). At
∼200 m, crabs (Cancer spp.), spot prawns (P. platyceros), and lizardfish (S. lucioceps) were more
common community members during the high-oxygen extremes, while tuna crabs
(P. planipes) and Dover sole (M. pacificus) were more common during low-oxygen extremes. For both
deployments, the video time series lasted ∼14 d, and a
longer time series or more extreme oxygen variability may show more
community-level differentiation in relation to oxygen extremes. Overall, our
results show that at short timescales (2 weeks or less), seafloor
communities responded to diurnal differences more than to high-frequency
oxygen variability.
Discussion
The California Current System is expected to experience the impacts of
hypoxia and ocean acidification on seafloor communities sooner than many
other regions of the world (Alin et al., 2012) because upwelling brings deep,
oxygen-poor, and CO2-rich waters into nearshore ecosystems along the US
West Coast (Feely et al., 2008). Species in the SCB region may be
particularly vulnerable to deoxygenation-induced habitat compression because
the depth of the 22.5 µmol kg-1 oxygen boundary (i.e., upper OMZ
boundary) occurs at a shallower depth than in northern California, Oregon,
and Washington (Helly and Levin, 2004; Moffitt et al., 2015). This study shows
that even during the relaxed upwelling season, seafloor communities at
∼400 m can be periodically exposed to OMZ conditions,
communities at ∼300 m are continuously exposed to hypoxic
conditions, and communities at ∼200 m are periodically
exposed to hypoxic conditions. In the spring, upwelling of 13 ∘C
water lowers oxygen conditions at 100 m, but conditions were never
hypoxic in our study. Seafloor communities differed across the sampled
environmental conditions, with communities living in lower-oxygen areas
characterized by invertebrate dominance and decreased fish diversity.
Comparing oxygen variability: short-term to long-term trends
Our Nanolander data show that at 100 m, benthic communities are exposed to
∼4–7 µmol kg-1 differences in oxygen conditions
at semidiurnal timescales and ranges of 7–34 µmol kg-1 at daily
timescales (Supplement 1E, F). At ∼200 m, benthic
communities experienced higher oxygen variability of 10–12 µmol kg-1 at semidiurnal timescales and ranges of 15–46 µmol kg-1 at daily timescales (Supplement 1E, F). In contrast, semidiurnal
and diurnal variability at 300 and 400 m was reduced (Fig. 4, Table 1). At
300 and 400 m, a tidal signal still influenced oxygen conditions, but this
signal was weaker and oxygen varied only ∼2µmol kg-1 at semidiurnal timescales (Supplement 1E). At daily timescales,
oxygen varied between 8 and 15 µmol kg-1 at 300 m and between 3 and 12 µmol kg-1 at 400 m (Supplement 1F).
Across weekly timescales, at 100 m, oxygen conditions ranged by
∼32µmol kg-1 during deployment D100-DM-Spr and 46 µmol kg-1 during deployment D100-DM-Fall (Table 1). More extreme
event-based decreases in oxygen have been reported near our study site at
the Del Mar mooring (a continuous oceanographic monitoring mooring on the
100 m isobath; Nam et al., 2015) but were not captured during any of our
deployments. At ∼200 m, the range in oxygen conditions across
weekly timescales was similar to or higher than at 100 m and was 55 µmol kg-1 for D200-LJ-1, 59 µmol kg-1 for D200-LJ-2, and 40 µmol kg-1 for D200-DM. Similarly, the CV for oxygen was always higher
during the 200 m deployments (13.72 %, 12.92 %, and 9.82 %) compared
to the 100 m deployments (5.07 % and 5.72 %) (Table 1). For the 300 m
deployment, oxygen variability across weekly timescales was lower than
observed at ∼200 m; the range in oxygen conditions was
∼19µmol kg-1 and the CV was 7.02 % (Table 1). At
400 m, the range in oxygen conditions was ∼17µmol kg-1, similar to 300 m, but the CV was higher (10.20 %) because mean
oxygen conditions were lower at this depth. High-spatial-resolution sampling
of the eastern tropical North Pacific OMZ documents considerable
submesoscale oxygen variability with better oxygenated holes (Wishner et al., 2019); such patchiness could account for some of the variability we observed
at ∼400 m.
While we were only able to conduct one seasonal comparison, we observed that
at 100 m between the fall and spring deployments, mean oxygen conditions
decreased from 132 to 104 µmol kg-1 (Table 1), and there was
little overlap in the oxygen measurements across the two deployments (Figs. 4, 7e). The most extreme high-oxygen conditions observed during the
spring deployment (D100-DM-Spr) were equivalent to the most extreme
low-oxygen conditions observed during the fall deployment (D100-DM-Fall)
(Fig. 4).
CalCOFI data from nearby station 93.3 28 provide additional context on the
characteristics of oxygen variability across seasonal and interannual
timescales. When temperature and oxygen profiles from ∼16 years of quarterly CalCOFI cruises are examined, we see that the highest
temperature variability occurs in the upper water column (<50 m),
and variability below ∼150 m is relatively low (Fig. 7a, b).
In contrast, absolute oxygen variability (i.e., standard deviation) is
greatest between 50 and 150 m (Fig. 7c), and the coefficient of variation for
oxygen (CV) actually increases below 100 m (Fig. 7d).
Comparing short-term environmental variability from DOV BEEBE deployments
to longer-term trends using CTD casts at a nearby CalCOFI station (93.3 28.0).
Mean temperature (a) and oxygen (c) conditions through the water column
(0–500 m) using CalCOFI CTD casts from October 2003 to November 2019; light and
dark colors indicate the variance around the mean and represent ±1 and
2 SD, respectively. Panels (b) and (d) show how the coefficient of variation
(CV) for temperature and oxygen changes through the water column. Dotted
lines in (a)–(d) indicate 100, 200, 300, and 400 m depths, and data are
extracted for these depths for (e)–(j). In (e)–(h), violin plots show the data
distribution of oxygen measurements from ∼16 years of CalCOFI
quarterly cruises compared to ∼3-week Nanolander deployments
at 100 (e), 200 (f), 300 (g), and 400 m (h). Violin plots show the
mean ±1 SD (white) and ±2 SD (black). Panels (i) and (j) examine
changes in temperature (i) and oxygen (j) conditions through time at 100,
200, 300, and 400 m. Dotted lines indicate nonsignificant linear
relationships; solid lines indicate significant trends (p<0.05).
Comparing our high-frequency Nanolander deployment results to oxygen
measurements across these ∼16 years of quarterly CalCOFI
cruises, we observe that the range in oxygen measurements at ∼100, 300, and 400 m only captured a small portion of the variability
measured across the ∼16-year time period. In contrast, for
the ∼200 m deployments, similar variance occurring over seasonal and interannual time periods was exhibited by the short-term deployments (Fig. 7e–h). Oxygen variability in the SCB is also
affected by the El Niño–Southern Oscillation (ENSO), with oxygen
conditions lower during La Niña periods (Nam et al., 2011). During the
Nanolander deployments (August 2017–March 2018), the monthly Niño3.4
index was always negative (-0.21 to -1.04; cpc.ncep.noaa.gov) but weaker
than the La Niña conditions described in Nam et al. (2011). Our
deployments therefore captured a neutral ENSO–weak La Niña state.
Interannual variability due to ENSO is captured in the data distribution
from the CalCOFI cruises.
Across multidecadal scales, dissolved oxygen at ∼100 m in the
SCB dropped from 1984 to 2006 at a rate of 1.25–1.5 µmol kg-1 yr-1 (Bograd et al., 2008). Over a period of ∼20 years, this rate of oxygen loss equates to the seasonal difference at 100 m
between the spring upwelling season and the fall. Thus, if this rate of
oxygen loss continues, in 20 years, fall conditions could resemble current
spring conditions. At 200 m, oxygen declines of 1–1.25 µmol kg-1 yr-1 have been reported (Bograd et al., 2008), suggesting that
if this same rate of oxygen decline continues, the mean oxygen conditions at
these depths (which ranged from ∼70 to 82 µmol kg-1 from our data) will be continuously hypoxic in 10–20 years. Currently,
communities are exposed to hypoxic conditions during the fall for <15 % of the time in our time series (Table 1) but may experience hypoxic
conditions more frequently in the spring. The greatest relative long-term
changes in oxygen in the SCB have been reported at 300 m and represent an
absolute change of 0.5–0.75 µmol kg-1 yr-1 (Bograd et al., 2008). Conditions at 300 m were always hypoxic during our deployment and
may become more extreme in the future. At 400 m, oxygen decreases of
0.25–0.5 µmol kg-1 yr-1 have been reported (Bograd et al., 2008), and if these trends continue, in 13–26 years, this depth zone may
become the upper boundary of the OMZ.
While we have related our results to reported trends for the SCB from Bograd
et al. (2008), it is unclear if these trends will continue, since
multidecadal oxygen trends associated with the Pacific Decadal Oscillation
(PDO) may reverse (McClatchie et al., 2010). In the 1950s and 1960s, oxygen
levels were also very low in the SCB, and conditions at ∼250 m were as low as or lower than those reported in the early 2000s (McClatchie et al., 2010). Additionally, projections for the California Current System
suggest winds near the equatorward boundary may weaken as winds strengthen
in the northern region (Rykaczewski et al., 2016), leading to less coastal
upwelling and higher-oxygen conditions in the SCB.
In recent years (2003–2019), at the CalCOFI station closest to the
Nanolander deployments (93.3 28), no significant linear deoxygenation trends
were detected at 100 or 200 m, but significant deoxygenation trends were
detected for 300 and 400 m (300 m: LR, R2=0.10, p<0.001;
400 m: LR, R2=0.21, p<0.001) (Fig. 7j). No significant
warming trends were detected at these depths during this period (Fig. 7i).
At 300 m, oxygen declined by 0.89 µmol kg-1 yr-1 during the
∼16-year time period, leading to a total oxygen loss of 14.25 µmol kg-1 across the time series, and at 400 m oxygen declined by
0.94 µmol kg-1 yr-1, leading to a total oxygen loss of
15.11 µmol kg-1 over the ∼16 years. Comparatively,
the range of oxygen conditions experienced over the ∼3-week
Nanolander deployment was ∼19µmol kg-1 at 300 m
and ∼17µmol kg-1 at 400 m.
Implications of environmental variability for seafloor communities
A recent FAO report on climate change impacts on deep-sea fish and fisheries
(FAO, 2018) developed an index of exposure to climate hazard, which
represents the mean changes in an environmental variable relative to its
historical variability (defined by the standard deviation in the historic
period). Higher historic variability reduces the index of exposure to
climate hazard. Other studies also suggest that conditions of higher
environmental variability may have a protective effect on the vulnerability
of species to climate change. Frieder et al. (2014) concluded that
high-frequency pH variability was an underappreciated source of pH-stress
alleviation for invertebrates that were sensitive to low pH conditions. In a
hypoxic fjord, slender sole, Lyopsetta exilis, were also observed to persist for short
periods under mean oxygen conditions that were lower than their critical
oxygen threshold (Pcrit), which was likely facilitated by high oxygen
variability around the mean and movements in and out of critically hypoxic
waters (Chu et al., 2018). Many marine fish species show physiological
plasticity in metabolic rate, gill surface area, and blood–oxygen binding
curves in relation to short-term changes in oxygen conditions (Mandic et al., 2009; Nilsson, 2010; Richards, 2010; Dabruzzi and Bennett, 2014).
Based on our results, benthic communities at ∼200 m may be
partially buffered from the negative effects of deoxygenation due to the
substantial high-frequency variability of oxygen experienced over daily and
weekly timescales. While at 400 m overall oxygen variability is lower than
at 200 m, the amount of oxygen variability relative to the mean is similar
to that at 200 m, suggesting variability may provide some reprieve to
benthic communities at 400 m from low mean oxygen conditions and
deoxygenation trends. At 400 m, conditions were severely hypoxic (O2<22.5µmol kg-1) for ∼1 % of the
deployment time (Table 1), suggesting that even though this community is
above the depth frequently associated with the upper boundary of the OMZ
(450 m), it is already periodically exposed to OMZ conditions. Recent rapid
deoxygenation trends at 400 m were also observed at nearby CalCOFI
station 93.3 28 from 2003 to 2019 (Fig. 7j).
Additionally, we note that between 200 and 300 m, there may be a boundary
between two different water masses with implications for deoxygenation
trends. The correlation between spiciness and oxygen concentration is
negative at 200 m (indicative of high input of 13CW, which is a component of
Pacific Equatorial Water, PEW) and then positive at 300 m (Fig. 5). Since
changes in the volume of PEW have been implicated in the decreases in oxygen
observed in the SCB (Booth et al., 2014; Bograd et al., 2015), it is
worthwhile to note that increased input of this water mass could have a
nonlinear effect on oxygen conditions in this area: increasing oxygen
conditions at deeper depths, while decreasing them at shallower depths.
The high turbidity observed at 300 m may be due to shoaling and breaking
nonlinear internal waves that can form bottom nepheloid layers (McPhee-Shaw,
2006; Boegman and Stastna, 2019). On the Peruvian margin, energy dissipation
from tidally driven internal waves has been shown to influence the
distribution of epibenthic organisms by increasing the suspension, transport,
and deposition of food particles (Mosch et al., 2012). High
turbidity conditions were also observed during two separate ROV dives
at ∼340 m off Point Loma (unpublished, NDGallo), suggesting
high turbidity conditions may be the norm at these depths on the upper slope
in the SCB.
Observations of community responses to high-frequency environmental
variability
Using DOV BEEBE, we were able to describe outer shelf and upper-slope assemblages and
examine if and how the seafloor community responds to O2 variability
at short timescales (daily, weekly, seasonal) in terms of community
composition and diversity. Unexpectedly, we did not see strong evidence of
seafloor community-level responses to daily and weekly oxygen variability.
Seasonal differences were observed for D100-DM-Fall and D100-DM-Spr, but it
is unclear if these were driven by oxygen, other upwelling-related
environmental covariates such as temperature, pH, and productivity, or
seasonal behavioral shifts associated with spawning or other activities.
Pronounced and rapid (<2 weeks) community-level responses to hypoxia
may occur in certain cases. For example, the Del Mar mooring has recorded
strong event-based changes in dissolved oxygen (Nam et al., 2015) whereby
oxygen rapidly increased or decreased over a short time period (<2 weeks). Rapid changes such as these that are outside the typical regime
of oxygen variability may lead to more immediate community responses.
Unfortunately, we did not capture any such events during our deployments.
Second, when oxygen conditions are near taxon-specific physiological
thresholds, even small changes in oxygen can have large community-level
effects (Levin et al., 2009; Wishner et al., 2018). In our deployments, spot
prawn (P. platyceros) and crabs (Cancer spp.) were more strongly associated with the highest-oxygen
conditions during the D200-LJ-2 and D200-DM deployments, suggesting the
oxygen conditions may be close to a critical threshold for these species.
Tolerances to hypoxia are species-specific with high intraspecies
variability, so longer time series may better detect community-level
changes. Given that we saw limited community-level responses at daily and
weekly timescales, future deployments could sample less frequently (one
camera sample taken every hour or every 2 h) but over longer time
periods (∼4–8 weeks) to examine community-level responses to
environmental variability.
The lack of community-level response to diurnal and weekly oxygen
variability seen in our data may not be surprising given that animals have
several ways that they can respond to stressful conditions, which would not
affect community-level abundance, diversity, or composition patterns. For
example, fish can become less active and reduce metabolic demands (Richards,
2009, 2010), or they can decrease feeding behavior (Wu, 2002; Nilsson, 2010) during
periodic hypoxia. We observed that animals were less active in the deeper
deployments, but it is unclear if this is due to the lower-oxygen conditions
or other environmental covariates.
Concluding remarks
Ocean deoxygenation is a global concern, with changes in oxygen conditions
potentially impairing the productivity of continental shelves and margins
that support important ecosystem services and fisheries. Nanolanders are
a powerful tool to examine short-term, fine-scale fluctuations in nearshore
dissolved oxygen and other environmental parameters, as well as associated
ecological responses that are rarely recorded otherwise. Oxygen variability
was strongly linked to tidal processes, and contrary to expectation,
high-frequency oxygen variability did not decline linearly with depth.
Depths of 200 and 400 m showed especially high oxygen variability, and
seafloor communities at these depths may be more resilient to deoxygenation
stress because animals are exposed to periods of reprieve during higher
O2 conditions and may undergo physiological acclimation during periods
of low O2 conditions at daily and weekly timescales. Despite
experiencing high oxygen variability, seafloor communities showed limited
responses to changing conditions at these short timescales. However, our
deployments did not capture any large acute changes in environmental
conditions that may elicit stronger community responses; future studies
using this platform could allow for such observations.
The Nanolander DOV BEEBE is configured to collect paired physical, biogeochemical,
and biological data in the deep sea over multiple days, which is a rarity
except for in areas with developed ocean observatories. We found that DOV BEEBE
performed well over the course of the deployments and allowed us to study
seafloor community responses to short-term environmental forcing. Our
deployment lengths were limited by battery capacity to power the LED lights;
all other elements would have allowed for longer sampling duration. Specific
ways to extend future deployment lengths are currently being explored and
include using higher-efficacy LEDs, integrating additional
batteries to power the LED lights into newly devised Nanolander side pods,
improving circuit performance that powers the LED lights by using new camera
controllers and solid-state relays, and using low-light cameras, such as the
Sony 7S II, which reduce the light required to illuminate the field of view.
Longer deployment lengths would be advantageous for capturing ecosystem
responses to environmental variability across timescales (hours to months).
Many of the areas where large decreases in oxygen have been observed occur
in developing countries, such as along the western and eastern coast of
Africa (Schmidtko et al., 2017). Large oxygen losses have also been observed
in the Arctic (Schmidtko et al., 2017), where the seafloor habitat is
understudied. Due to their compact design, small landers such as DOV BEEBE can
provide a cost-effective and easily deployable tool for studying nearshore,
deep-sea ecosystems and thus expand the capacity of developed and developing
countries to monitor and study environmental changes along their coastlines.
For continental margins and seafloor habitats, a global array of
Nanolanders, similar in scope to the Argo program, could be envisioned.
These would provide coupled physical, biogeochemical, and ecological
measurements, which would greatly expand our understanding of temporal and
spatial heterogeneity in nearshore deep-sea ecosystems and seafloor
community sensitivity to environmental change.
Code and data availability
Code and data are posted on Zenodo (10.5281/zenodo.3897966, Gallo et al., 2020).
It is intended that elements of the Nanolander design will be released as
open-source hardware at https://www.globaloceandesign.com (last access: 24 July 2020) within 1 year of
publication.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-3943-2020-supplement.
Author contributions
NDG, LAL, KH, and NCW designed the research plan; KH designed and built the
Nanolander; NDG, KH, and HY carried out the field deployments; NDG, HY, and AN
performed the video annotation and data analysis; NDG wrote the paper,
and all authors contributed to editing the paper.
Competing interests
Author Kevin Hardy is the owner of the company Global Ocean Design, which
currently sells Nanolanders similar to DOV BEEBE.
Special issue statement
This article is part of the special issue “Ocean deoxygenation: drivers and consequences – past, present and future (BG/CP/OS inter-journal SI)”. It is a result of the International Conference on Ocean Deoxygenation , Kiel, Germany, 3–7 September 2018.
Acknowledgements
Global Ocean Design LLC provided
internal R&D resources. The Fisheries Society for the British Isles and
the UCSD Graduate Student Association provided travel support to present
these results at scientific meetings. This work would not have been possible
without the support of a tremendous number of people who helped with
Nanolander deployments and recoveries, especially Phil Zerofski, Brett Pickering, Rich Walsh, Jack Butler, Mo Sedaret, Lilly McCormick, Andrew Mehring, Ana Sirovic, Rebecca Cohen, Ashleigh Palinkas, and Jen McWhorter. We are
forever grateful to Javier Vivanco and others at Baja Aqua
Farms for recovering and returning DOV BEEBE after it drifted into Mexican waters
following an unsuccessful recovery. NG's PhD committee members, Brice Semmens, Richard Norris, Ronald Burton, David Victor, and Ralph Keeling, provided feedback
on the research. We thank Kevin Stierhoff for feedback on the paper.
Financial support
This research was made possible by generous funding from several
sources: the Mullin Fellowship, a Mildred E. Mathias Research Grant, the Edna B. Sussman Fellowship, the Mia J. Tegner Fellowship, Friends of the
International Center Scholarship, the DEEPSEA CHALLENGE Expedition, the National Science
Foundation Graduate Research Fellowship, and the Switzer Environmental
Leadership Fellowship to Natalya D. Gallo. Natalya D. Gallo is currently supported
by a NOAA QUEST grant to Brice X. Semmens.
Review statement
This paper was edited by Tina Treude and reviewed by SungHyun Nam and one anonymous referee.
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