Methods
Unless specified otherwise, measurements were taken on board the R/VSikuliaq , cruise number SKQ201617S, from 07 January 2017 through
13 January 2017 at a single station 16.5°N 106.9°W, which was located in
an oligotrophic region of the Eastern Tropical North Pacific Oxygen
Deficient Zone (ETNP Station P2; Figure 1A). Data are compared against
measurements taken at 16.5°N 152.0°W on 08 May 2015, collected on the
GO-SHIP CLIVAR/CARBON P16N Leg 1 Cruise (CCHDO Hydrographic Cruise
33RO20150410). This station was at the same latitude as ETNP Station P2,
west of the ODZ, but was not anoxic (P16 Transect Station 100; Figure
S1).
Water property
measurements
We measured water properties of temperature, salinity, fluorescence,
oxygen concentration and turbidity using the shipboard SeaBird 911 CTD.
Auxiliary sensors included a WetLabs C-Star (beam attenuation and
transmission) and a Seapoint fluorometer. Data were processed with
Seabird Software, (programs–data conversion, align, thermal mass,
derive, bin average and bottle summary) using factory supplied
calibrations. Data was analyzed and visualized in R (Team 2011).
Processed data are available under NCEI Accession number 1064968 (Rocap
et al., 2017).
Water mass analysis
Evans et al. (2020) previously employed optimum multiparameter analysis
to map the percent identity of the water observed at each depth to three
water masses: the 13 Degree Celsius Water (13CW), North Equatorial
Pacific Intermediate Water (NEPIW), and Antarctic Intermediate Water
(AAIW). We subset and examine only the portion of these data that
correspond to our site.
Acoustic Measurements
Backscattering signals from the
ship-board EK-60 were collected and archived by UNOLS as raw data files.
We used Echopype software (Lee et al., 2021) to convert these raw files
to netcdf files, which were down-sampled to five minute time-step
resolution, saved as a text file, and later visualized in R. The
acoustic data appeared to be off by one hour and so one hour was
subtracted from all time measurements. This correction resulted in
zooplankton vertical migrations being synchronized with the diel light
cycle as was recorded on board the ship by JAC.
Particle size
measurements
Particle size data were collected by an Underwater Vision Profiler 5
(UVP) that was mounted below the CTD-rosette and deployed for all CTD
casts shallower than 2500 m. A UVP is a combination camera and light
source that quantifies the abundance and size of particles from 100 μm
to several centimeters in size (Picheral et al., 2010). UVP data were
processed using the Zooprocess software, which prepares the data for
upload to the Ecotaxa database (Picheral et al., 2017); data from all
UVP instruments are located on this online database for ease of access.
Detailed descriptions for installation of the Zooprocess software can be
found on the PIQv website
(https://sites.google.com/view/piqv/zooprocess-uvpapp). Zooprocess uses
the first and last image number selected by the user in metadata to
isolate the downcast and process this subset for both particle size
distribution and image data. The processed files and metadata are then
uploaded to a shared FTP database where it is available for upload to
Ecotaxa. This project required the extra step of filtering out images
due to the discovery of an issue with the lighting system, where only
one of the two LEDs would illuminate, resulting in an incomplete sample.
The filtering procedure is documented in a link available at the same
location as the Zooprocess download. Images where only a single light
illuminated were removed from the dataset before it was uploaded on to
Ecotaxa. Once uploaded to Ecotaxa, data were downloaded from EcoPart
(the particle section of the database) in detailed TSV format, and
analyzed in R. The UVP provided estimates of abundances of particles in
different size-bins, as well as information about the volumes over which
those particle numbers had been calculated. Particles were categorized
into bins starting at 102-128 μm in size, with the width of each
particle size bin 1.26 times larger than the previous bin. We observed
particles in 26 distinct size bins, with largest, mostly empty, bin
covering particles from 26-32 mm.
The instrument is capable of
observing smaller particles (down to 60 μm), but these tend to be
underestimated and so we only consider particles ≥102 μm in this
analysis. The instrument can in principle also measure larger particles
(up to the field of view of the camera), though these tend to be scarce
enough to be not detected. In this paper, we do not have an upper size
cut-off for our analysis and rather implement statistics that are robust
to non-detection of scarce large particles (section 5.5.1). Visual
inspection of images larger than 1 mm suggests that these large
particles are primarily “marine snow” but about 5% are zooplankton.
We did not quantify the size distribution of these images.
Flux measurements
Free floating, surface tethered particle traps were used to quantify
carbon fluxes from sinking particles. Arrays, consisting of a surface
float and two traps, were deployed and allowed to float freely during
which time they collected particles. Trap deployments began on 07
January, concurrently with the beginning of the UVP sampling, and
continued through 12 January. Trap recovery began on 08 January and
continued through 13 January. Trap depths spanned the photic zone and
mesopelagic, with the shallowest at 60 m and the deepest at 965 m. Trap
deployments lasted between 21 and 91 hours, with deeper traps left out
for longer, to collect more biomass. Two types of traps were deployed.
One set of traps, generally deployed in shallower water, had a solid
cone opening with area 0.46 m2. The second set had
larger conical net with opening of 1.24 m2 area made
of 53 μm nylon mesh similar to the description in Peterson et al.
(2005). All equipment were combination incubators and particle traps,
but in this study we only use trap data. No poisons were used, and both
living and dead zooplankton, or ‘swimmers’, were manually removed prior
to POC analysis.
Sediment trap material was filtered immediately upon trap recovery onto
pre-combusted GF-75 45 mm filters (nominal pore size of 0.3 µm) and
preserved until further analysis at -80°C. These filters were split into
several fractions for other analyses not discussed here. Total carbon
content of particles in each trap were measured by isotope ratio mass
spectrometry. Elemental analyses for particulate carbon and nitrogen
quantities as well as 13C and 15N
isotopic compositions were conducted at the U.C. Davis Stable Isotope
Facility (http://stableisotopefacility.ucdavis.edu) on acidified
freeze-dried trap samples to capture organic elemental contributions.
Carbon was below mass spectrometry detection limits in four traps –
these traps were excluded from further analysis. Traps at similar depths
did detect carbon, lending confidence to the idea that these
non-detections were technical in nature, due to splitting of samples for
multiple analyses, rather than reflecting environmental conditions.
Analysis
Particles were binned by depth with 20 m resolution between the surface
and 100 m, 25 m resolution between 100 m and 200 m depths and 50 m
resolution below 200 m. This increasing coarseness of the depth bins
helped account for more scarce particles deeper in the water column,
while maintaining higher depth resolution near the surface. To perform
this binning, particle numbers, and volumes of water sampled of all
observations within each depth bin were summed prior to other analyses.
Most analyses focused on the mesopelagic, defined here as the region
between the base of the secondary chlorophyll maximum layer (160 m —
hereafter the base of the photic zone), which is within the ODZ, and
1000 m.
Two normalized values of particle numbers were calculated. In the first,
particle numbers were divided by volume sampled, to generate values inparticles/m3 . In the second, particles were
divided by both volume sampled and the width of the particle size-bins
to generate values in particles/m3/mm .
Particle size
distribution
We determined the slope and intercept of the particle size distribution
spectrum by fitting a power law to the data, which is a common function
for fitting particle size distributions (Buonassissi & Dierssen, 2010).
Because large particles were infrequently detected, we used a general
linear model that assumed residuals of the data followed a
negative-binomial (rather than normal) distribution. We fit the equation
\(\ln\left(\frac{E\left(\text{Total}\,\text{Particles}\right)}{\text{Volume}*\text{Binsize}}\right)=b_{0}+b_{1}\,ln\left(\text{Size}\right)\)(Eqn 1).
to solve for the Intercept (\(b_{0}\)) and particle size distribution
slope (\(\text{PSD}=b_{1}\)). On the left-hand side of Eqn 1.E(Total Particles) refers to the expected number of particles in
a given depth and particle size bin assuming a negative binomial
distribution of residuals (Date, 2020; Ooi, 2013). Volumeindicates the volume of water sampled by the UVP, or in the case of
depth-binned data, the sum of the volumes of all UVP images in that
depth interval. Binsize indicates the width of the particle-size
bin captured by the UVP. Thus, if particles between 0.1 and 0.12 mm are
in a particle size bin, the Binsize is 0.02 mm. On the right-hand
side of Eqn 1, Size corresponds to the lower bound of the
particle size-bin. We use the lower bound of a particle size-bin, rather
than its midpoint, because, due to the power-law particle size
distribution slopes, the average size of particles in each size-bin is
closer to the size-bin’s lower bound.
Estimating particle flux
We estimated particle flux throughout the water column, by fitting
particle data to trap measurements. We assumed that particle flux in
each size bin (j) followed the equation
\(Flux=\sum_{j}{[\frac{\text{Total}\,\text{Particle}s_{j}}{\text{Volume}*\text{Binsiz}e_{i}}*C_{f}*({\text{Size}_{j})}^{A}]}\)(Eqn. 2)
Such that flux at a given depth is the sum of all size-bin specific
values.
We used the optimize() function in R ’s stats package to
identify values for the Cf and Acoefficients in Eqn 2. that yielded closest fits of the UVP estimated
flux to each particle trap.
We also estimated the exponent of the particle size to biomass exponent\(\alpha\) and size to sinking speed exponent \(\gamma\) per the
equations \(\text{Biomas}s_{j}\sim Size_{j}^{\alpha}\,\) and\(\text{Spee}d_{j}\sim{S\text{iz}e}_{j}^{\gamma}\). This is done by
assuming a spherical drag profile, in which case \(A=\alpha+\gamma\)and \(\gamma=\alpha-1\) (Guidi et al., 2008); with “A” referring
to the exponent in Eqn 2.
Size specific
information
We separately analyzed total particle numbers, particle size
distribution, and particle flux for particles larger than or equal to
500 μm, and those smaller than 500 μm, to determine the relative
contributions of these two particle classes to particle properties.
500 μm was chosen as it has been previously defined as the cutoff point
between microscopic “microaggregates” and macroscopic “marine snow”
(Simon et al., 2002).
Variability
To explore the timescales of temporal variability in the POC flux, we
determined how well the flux at each depth horizon can be described by
the sum of daily and hourly temporal modes. This was achieved by fitting
the general additive model of form
\(\text{Flu}x^{1/5}\sim s\left(\text{Depth}\right)+s\left(\text{Day}\right)+s\left(\text{Hour}\right)\)(Eqn. 3)
This model explored whether estimated flux levels appeared to vary by
decimal day and decimal hour, holding the effects of depth constant, in
the 250 m to 500 m region. The smooth terms s for Depthand Day were thin plate splines, while the s term forHour was a cyclic spline of 24-hour period.
Smoothing for Comparison to Model
Results
Normalized particle abundance data, from the only UVP cast that
traversed the top 2000 m of the water column, taken on January 13 at
06:13, was smoothed with respect to depth, time, and particle size using
a general additive model of the form
\(\ln\left(\frac{E\left(\text{Total}\,\text{Particles}\right)}{\text{Volume}*\text{Binsize}}\right)\,\sim\,s\left(Depth,\,ln\left(\text{Size}\right)\right)\)(Eqn. 4)
In this case, there is a single, two-dimensional, smooth term, rather
than additive one-dimensional terms as in Eqn. 3 so that the smooth term
can consider interactions between the two parameters, rather than
assuming that the terms are additive. The predicted particle numbers at
each particle size and depth, as well as particle size distribution
spectra, and estimated particle masses of all particles smaller than
500 μm and all particles larger than or equal to 500 μm were then
compared to each of Weber and Bianchi’s (2020) models, corresponding to
our H1-H3 .
Modeling remineralization and
sinking
To quantify disaggregation, our goal was to compare the particle
size-abundance spectrum at each depth to a prediction of the null
hypothesis, that it is simply governed by the effects of sinking and
remineralization reshaping the spectrum observed shallower in the water
column. This prediction is generated using the
particle remineralization and
sinking model (PRiSM), modified from DeVries et al. (2014), which we
applied to the shallower spectrum as an initial condition. The
difference between the null hypotheses prediction and observation
indicates the role of processes not accounted for in PRiSM, such as
disaggregation, aggregation, and active or advective transport of
particles with a different size spectrum than the ones seen at the
deeper depth.
In practice we expanded the previous numerical implementation of PRiSM
to allow for particle size distribution spectra with particle-size bins
that match those obtained by the UVP, and to return estimates of the
number of particles in those same size bins (Text S1). The model accepts
inputs of particle size distributions at each depth, and changes in
particle flux between each depth-bin and the next, deeper, depth-bin.
The model optimizes a particle remineralization rate that would result
in that observed flux loss. It finally returns a “predicted” particle
size distribution spectrum that has total flux equal to the flux of the
observed deeper spectrum that would be expected if the shallower
spectrum only sank and remineralized. In cases where flux increased with
depth, particles are assumed to put on mass rather than lose mass
following a negative remineralization rate.
Here, “negative
remineralization” stands in for chemoautotrophy, active transport, and
other processes that result in flux increases with depth. While these
processes likely have more complex effect on the particle size
distribution than is accounted for in our model, we note that flux
increases with depth are very rare, and that allowing for negative
remineralization allows our null model to be robust in those rare cases.