Zooplankton Distribution in Arabian Sea: Acoustic Analysis

Acoustics Australia
https://doi.org/10.1007/s40857-024-00336-w
ORIGINAL PAPER
Abundance Distribution Pattern of Zooplankton Associated
with the Eastern Arabian Sea Monsoon System as Detected
by Underwater Acoustics and Net Sampling
Shirin J. Jadhav1,2
· B. R. Smitha1
Received: 13 June 2024 / Accepted: 8 October 2024
© Australian Acoustical Society 2024
Abstract
The abundance distribution pattern of zooplankton associated with the pre-upwelling and late-upwelling phase is assessed
for the eastern Arabian Sea (EAS) summer system, using vessel-mounted moving Acoustic Doppler current profiler (ADCP)
and the in situ zooplankton samples collected using plankton nets. The distribution pattern of zooplankton is observed to be
regulated by physical factors such as coastal upwelling, circulation patterns, mesoscale eddies, regional stratification, the presence of subsurface chlorophyll-a maximum, etc. during different phases of the upwelling cycle. The volume backscattering
strength, a proximate factor for the zooplankton biomass, is computed after deriving the appropriate sound absorption coefficient, slant range, and backscatter noise. The linear relation derived by enumerating the backscatter-to-zooplankton biomass
relationship was stronger during the pre-upwelling phase (r 0.58) but weaker during the late-upwelling phase (r 0.25).
The findings underscore the potential of ADCP backscatter as a reliable indicator of zooplankton biomass within the mixed
layer depth of the EAS, especially in the stable, calm, early summer season. The derived equations for estimating biomass
are log(B) 5.39 + 0.05 Sv for pre-upwelling and log(B) 3.10 + 0.02 Sv for late-upwelling. The reduced correlation later
suggests that environmental changes, such as zooplankton size and composition shifts, may affect ADCP’s detection threshold,
necessitating careful interpretation. The study shows fish larvae act as dominant scatterers due to their gas-bearing properties,
reliably indicating proxies for zooplankton abundance across both upwelling phases. Fluid-like and elastic-shelled scatterers
vary between phases, reflecting shifts in zooplankton composition and their impact on acoustic backscatter. The analysis of
ADCP backscatter data tracks diel vertical migration (DVM) of zooplankton with significant concentrations at depths of up to
approximately 80 m during night-time. This study identifies distinct vertical migration velocities with zooplankton ascending
in the range of 7.2 cm/s during dusk and descending at 7.7 cm/s during dawn.
Keywords Zooplankton biomass · Acoustic Doppler current profiler · Arabian Sea · Acoustic backscatter
1 Introduction
The Arabian Sea (AS) is unique due to the seasonal reversal of monsoonal winds. The summer monsoon (SM) winds
from the southwest and the winter monsoon from the northeast regulate the regional processes and dynamics from
June to September and November to February, respectively. Wind reversal influences physical processes such
B Shirin J. Jadhav
[email protected]
1
Centre for Marine Living Resources and Ecology, Ministry of
Earth Sciences, Kochi 682508, India
2
Faculty of Marine Sciences, Cochin University of Science and
Technology, Kochi 682016, India
as coastal upwelling, convective mixing, strong currents,
fronts, filaments, and mesoscale eddies, resulting in significant biological impacts [44, 65–67, 71, 72]. As a result of
the reversal of winds, the upper layer circulation pattern
in the AS undergoes a biannual reversal. During the SM,
precipitation and river outflow influence the coastal hydrodynamics in the near-shore waters of the west coast of India.
Majorly, the seasonal vertical process of upwelling drives
significant changes in the physical, chemical, and biological
characteristics, making this region a prime area for interdisciplinary research. Alongshore winds primarily drive coastal
upwellings; Ekman transport causes the upward movement
of subsurface cold and nutrient-rich water to the surface.
Numerous studies have previously examined the physical
processes, forcing mechanism, spatiotemporal variability of
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upwelling, and its biological impact in the eastern Arabian
Sea (EAS) [61, 63, 72]. In addition, vertical mixing due to
cyclonic eddies brings nutrient-rich waters to the surface,
significantly increasing primary production by enhancing
phytoplankton growth, supporting zooplankton populations,
and ultimately sustaining higher trophic levels [56].
There have been several studies explaining the spatiotemporal pattern of the primary production associated
with upwelling, mainly based on chlorophyll-a (Chl-a) and
satellite-based observations. However, the primary consumer, zooplankton, is less explored as the only data source
is in situ sampling. Understanding the spatiotemporal variability of zooplankton is essential to address the impact of
changes in the aquatic habitat due to changing climatic factors like warming, stratification, decreasing dissolved oxygen
concentration, acidification, etc. Zooplankton is a significant contributor to carbon transport from lower to higher
trophic levels and has a crucial role in regulating the biological pump and the biogeochemistry of the habitat [1,
35, 75, 76]. The diel vertical migration (DVM), seasonal,
intra-seasonal, interannual variations, and other multi-scale
variabilities occur in response to various physical, chemical, and biological dynamics in space and time. Several
studies globally have explored the spatiotemporal variation
in zooplankton through net-based sampling and taxonomic
analysis. In the AS, research has focused explicitly on the
spatiotemporal variation of zooplankton in response to the
seasonal processes [4, 30, 31], primarily through the Joint
Global Ocean Flux Studies-India (JGOFS) and the Marine
Living Resources Programme (MLRP) of India. For instance,
Sanjeevan et al. [59] estimated the total seasonal zooplankton biomass in the mixed layer of the south and north EAS at
11.5 and 7.5 mtC/yr, respectively. The enumeration of these
organisms using acoustic techniques has also attained focus
in several ecosystems [11, 28].
To understand the complex ecological dynamics of the
marine ecosystem and its response to climatic oscillations,
high-resolution zooplankton data obtained through updated
techniques, such as underwater acoustics, are essential.
Applying these techniques at spatiotemporal scales corresponding to environmental parameters is crucial for ensuring
a comprehensive assessment of ecosystem dynamics and
potential yields. The study by [12] utilized a bottom-mounted
Acoustic Doppler current profiler (ADCP) to study zooplankton. Since then, numerous researchers have pioneered similar
approaches, leveraging the backscattered acoustic intensity
data obtained from ADCPs to explore both temporal and
spatial variations in the distribution of zooplankton biomass
and investigate DVM patterns [23, 24]. In the late 1990s, as
part of the JGOFS programme, the investigations on zooplankton dynamics utilized vessel-mounted (VM) ADCPs.
These extensive studies explored the vertical migration patterns [45, 70]. In a recent investigation by [2], a noteworthy
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analysis employed three moored ADCPs to delineate the seasonal variations in zooplankton biomass and standing stock
within the EAS. This study marks the first attempt to quantify mixed-layer zooplankton biomass in the EAS, employing
ship-based ADCP and in situ measurements.
Researchers have effectively exploited the variations in
echo intensity recorded by the ADCP, augmenting physical oceanographic datasets with information from postulated
biological sources collected simultaneously over the same
temporal and spatial scales. The integrated approach has
allowed us to explain the variations in zooplankton abundance and distributions related to the prevailing oceanographic conditions [58, 69]. Previously, most studies estimating zooplankton biomass using acoustic techniques were
based on narrowband (NB) ADCPs [7, 56] to measure acoustic backscatter. The NB system’s automatic gain control
output is firmly temperature dependent, and many studies
have been unable to determine absolute backscatter intensity
since output signal strength was unknown. Broadband (BB)
ADCP has an advantage over NB ADCP because it has lower
random fluctuations for current and echo intensity data [10,
28]. The fluctuation occurs typically due to the random position of the scatters in the ensonified region; depending on the
distribution, the individual echos add or subtract to form their
combined echos. For NB systems, the resulting echoes follow the Rayleigh pattern with random fluctuation in the range
of 5–6 dB, while in BB ADCP, it is below 1 dB. The returned
signal strength indicator (RSSI) outputs of BB ADCP are not
temperature dependent, enabling higher resolution along the
profile with little reduction of velocity precision [14]. One
of the general concerns in the estimation of zooplankton is
the limited size range of organisms, and in general, ADCPs
measure objects in the size range of millimetres to a few
centimetres.
In the present effort, the authors focus on the EAS summer system to utilize the ADCP echo intensity data to assess
the upper layer zooplankton biomass and abundance distribution pattern associated with the coastal upwelling using
acoustic and net sampling. The analysis considers the preupwelling phase from May 27th to June 19th, 2005 and the
late-upwelling phase from August 17th to September 11th,
2005. The study also aims to explain the spatial variability
in abundance distribution patterns in response to prevailing
ecosystem processes. Understanding these patterns is essential, given the significance of the coastal upwelling ecosystem
and its support of rich biomass and fisheries.
Acoustics Australia
2 Data & Methods
2.1 Vertical Profiles on Environmental Parameters
Temperature and density profiles were obtained using a
conductivity-temperature-depth (CTD) sensor (SBE Model
911 PLUS, Sea-Bird Inc.). Chl-a concentrations were determined spectrophotometrically. Filtered water samples collected at pre-fixed locations and depths were extracted with
90% acetone following the methodology described by [48].
Absorbance of the extracts was measured using a doublebeam Perkin-Elmer UV–Visible spectrophotometer.
2.2 Satellite Data
The study utilized daily oceanic currents data from
the Copernicus database, specifically the MULTIOBS_GLO_PHY_NRT_015_003 dataset available at:
https://data.marine.copernicus.eu/product/MULTIOBS_
GLO_PHY_MYNRT_015_003/. We obtained data at a
depth of 15 m from this dataset, which offers global total
velocity fields derived from a combination of altimetryderived geostrophic velocities and modelled Ekman surface
currents. The dataset has a spatial resolution of 0.25° and
a temporal resolution of one day. In addition, wind data
were sourced from the ERA5 dataset available at https://
climate.copernicus.eu/, which provides hourly estimates
for atmospheric variables. This fifth-generation reanalysis
dataset, developed by the Copernicus Climate Change
Service, combines model data with observations [22]. The
data have been regridded to a regular lat-lon grid of 0.25°.
Weekly sea level anomaly data were acquired from AVISO,
specifically from the Delayed Time—Global—Altimetry
dataset available at https://las.aviso.altimetry.fr/las/UI.vm
2.3 Ekman Transport
The offshore Ekman transport (M x E) was computed using
a bulk aerodynamic formula based on [32] and [50].
Mx E τy
, where τ y ρa Cd W V
f
(1)
Mx E represent Ekman transport due to alongshore components of wind, τ y is the alongshore wind stress, ρa is the
density of air (1.225 kg/m3 ), Cd is the drag coefficient which
varies with wind speed based on [33] and [80], W is the resultant wind magnitude, V is meridional components of wind
and ‘ f ’ is the Coriolis parameter (2sinϕ).
Fig. 1 Study region-EAS, highlighting the locations of zooplankton
sampling and ADCP data capture
2.4 Zooplankton Sampling, Processing, and Biomass
Estimation Method
Zooplankton were sampled at 39 stations during the preupwelling phase (Cruise No. 235) and 53 stations during
the late-upwelling phase (Cruise No. 237) onboard FORV
Sagar Sampada using a Multi Plankton Net (MPN), as shown
in Fig. 1. The sampling employed a MPN with a diameter
of 0.25m2 and a mesh size of 200 μm. To determine the
MLD, vertical CTD profiles were cast at each sampling station prior to net sampling. The net was vertically hauled up to
the mixed layer depth (MLD) determined using the criterion
where in 0.03 kg/m3 density difference from the surface [9]
is observed. Samples collected from the MLD were made to
filtration, draining, and addition to a known volume of water
to estimate the displacement volume, and the biomass was
derived. Zooplankton biomass was then estimated using the
empirical relation formula given by [40]: 1-ml displacement
volume equals 0.075 g of the dry weight. Post-collection,
the samples were preserved in a buffered seawater solution
containing 4% formaldehyde to facilitate subsequent counting and identification processes, which was completed in the
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Table 1 Summary of ADCP data collection parameters
Parameter
Pre-upwelling
Late-upwelling
ADCP model
RDI 76.8 kHz
Ocean Surveyor
RDI 76.8 kHz
Ocean Surveyor
Number of bins
50
100
Bin size
16 m
8m
Data acquisition
software
VMDAS
VMDAS
Post-processing
software
WinADCP
WinADCP
Ensemble interval
1 min
1 min
Depth of ADCP on
ship
5.6 m
5.6 m
Blanking distance
8m
8m
Vertical resolution for
initial bin
14 to 30 m below
the surface
14 to 22 m below
the surface
Maximum detection
range
814 m (approx.)
814 m (approx.)
shore lab. The samples were subjected to taxonomic analysis entirely for small concentrations (< 3 ml/m3 ) and were
considered 50% or 25% for large samples.
2.5 Acoustic Data Collection and Processing
Echo amplitude data were acquired during ocean surveys
conducted in the pre-upwelling and late-upwelling phases,
utilizing an RDI 76.8 kHz Ocean Surveyor BB ADCP.
Configuration settings for pre-upwelling included 50 bins,
whereas late-upwelling utilized 100 bins. Bin sizes were
defined as 16 m and 8 m for the pre-upwelling and lateupwelling phases, respectively. Data acquisition was executed through Moving Vessel Data Acquisition Software
(VMDAS) and subsequently underwent post-processing on
a bin-by-bin basis with a 1-min ensemble using WinADCP.
The ADCP was affixed to the ship at a depth of 5.6 m, and
an 8 m blanking distance was considered. In pre-upwelling
phase, this configuration yielded a vertical resolution for
the initial bin, ranging from 14 to 30 m below the surface,
enabling a maximum detection range of 814 m. Similarly,
for late-upwelling, a comparable blanking distance was sustained, resulting in a vertical resolution for the initial bin,
extending from 14 to 22 m below the surface, and facilitating a maximum detection range of about 814 m. ADCP data
collection parameters are summarized in Table 1.
Objects larger than one-quarter of the wavelength are
detected as reflective, while smaller ones scatter sound,
as noted by [78]. Additionally, [38] identified a detection
threshold of 1 mm for a 153 kHz ADCP, highlighting its
capability to target smaller zooplankton. Building on this,
[19] suggested that acoustic devices could detect organisms
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as small as 0.5 mm at 307 kHz, approximately one-tenth of
the wavelength. Considering the speed of sound in seawater
at 1500 m/s, the lower detection limit for the 76.8 kHz ADCP
would be around 2 mm, making it practical for detecting target zooplankton in size range of a few millimetres to a few
centimetres. Our focus on mesozooplankton (0.2 to 20 mm)
is relevant as previous studies [3] highlight that the SM in
the SEAS is characterized by significant mesozooplankton
stocks compared to microzooplankton (0.02 to 0.2 mm).
2.6 Calculation of Volume Backscattering Strength
(Sv ) from ADCP
Volume backscattering strength (S v ) is defined as the ratio
of the intensity of sound scattered back towards the sound
source by a given volume to the intensity of the incident plane
wave [26]. It essentially quantifies the amount of sound scattered back relative to the initial sound wave. This value can be
conceptualized as the cumulative sum of the backscattering
cross sections of individual scatterers within a unit volume.
Consequently, it demonstrates a direct correlation with the
numerical density of scatterers present in the volume under
examination. In the operational implementation of the sonar
equation, additional pivotal variables have been integrated
and reconfigured to ascertain the backscatter coefficient, as
elucidated by [10]. Subsequently, the accurate depiction of
this equation, supported by [46], represents the most recent
approach utilized for S v calculation. In this study, we adopted
the latest and validated formulation of this equation given
below, as proposed by [46], for the precise computation of
S v in decibels referenced to (4π m) −1 .
Sv C + 10log (Tx + 273.16)R 2 − L D B M − PD BW + 2α R
+ 10log(10 K c (E−Er )/10 − 1)
(2)
Considering the backscatter estimation Eq. (2) described
above, applied with a VM BB ADCP operating at a frequency
of 76.8 kHz, several parameters assume pivotal significance.
PDBW represents the power level, calculated as 10 log10 P
(with P being the transmitted power in watts), while C is
an empirical constant utilized in the calculations. The values
PDBW 23.8 and C −153.3 are specific to the instrument model used, as provided by [10]. These values serve
as crucial constants in the context of the VM BB ADCP
operating at a frequency of 76.8 kHz. The parameter L DBM
stands for 10 log10 L with L representing the transmit pulse
length. The instrument captures this length, which closely
aligns with the bin size. Specifically, for pre-upwelling and
late-upwelling phase, the bin sizes are approximately 16 m
and 8 m, respectively. Tx is the temperature of the water
beneath the ship’s hull at the transducer depth, measured in
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°C. K c is a system-specific parameter signifying the conversion factor (in dB/count) for each beam. For this ADCP, RDI
provided the values of 0.39, 0.38, 0.40, and 0.39 dB/count.
The recorded parameter E corresponds to the amplitude of
the RSSI as reported by the ADCP. The α is the sound absorption coefficient, expressed in dB/m. R is the slant range,
measured in metres, signifies the distance along the beam
to the scatterers. E r represents the observed RSSI amplitude,
reported in counts, by the ADCP in the absence of any signal
[10] commonly referred to as noise (counts). Computations
for α, R and E were discussed in subsequent section.
and [82], where:
R
c
B + |(L − D)/2|+(N × D) + (D/4)
×
cosθ
c
(3)
here: B is fixed parameter, set to 8 m, represents the blanking
distance near the transducer where echoes are not considered.
N is the bin number. D is the size of each bin. θ is the angle
between the transducer beams and the vertical, held consistently at 30°. c is the sound speed between transducer and
depth cells and was derived from temperature and salinity for
each distinct profile (m/s). c is the speed of sound, assumed
to be a constant 1475.1 m/s.
2.7 Calculation of Sound Absorption Coefficient
2.9 Background Noise Estimation and Er Calibration
In the process of acoustic estimation of zooplankton biomass,
addressing excess attenuation due to volume scattering is
crucial. The seawater absorption coefficient (α) at a specific
frequency f (kHz) was calculated following the framework
of [13], considering contributions from chemical relaxation
processes and absorption by pure water. Salinity, temperature, depth, sound frequency, and pH were recognized as
influential determinants. To consider α variations in the
EAS, temperature and salinity data from CTD measurements were utilized, with pH assumed constant at 8 based
on negligible variability [2]. In pursuit of transparency
and reproducibility, a Jupyter notebook with a Python programme for precise sound absorption coefficient calculation
was developed. This implementation adheres rigorously to
the well-established formula presented by [13]. The notebook, including detailed code, is openly accessible on our
GitHub repository [https://github.com/shirin9/Sound-Abso
rption-Coefficeient-Calculation].
To address variations in the α attributable to hydrographic
conditions, we conducted an analysis of 129 CTD profiles
collected across SEAS during two distinct phases: preupwelling and late-upwelling. Substantial depth-dependent α
variations, particularly within the depth range up to 1000 m,
were observed. Spatial variability in α ranged from 0.020 to
0.027 dB m−1 for both the pre-upwelling and late-upwelling
phases (Fig. 2). This variability was crucial for determining
the average α for sound propagation. Calculated α values
played a pivotal role in obtaining precise backscatter estimates as utilized in Eq. (2) presented in Sect. 2.6, contributing
to a broader understanding of acoustic estimation methodologies under diverse hydrographic conditions.
2.8 Slant Range Determination
The slant range (R) to a depth cell (m) represents the distance
to the relevant scattering layer along the acoustic beam. The
calculation of R follows the methodology outlined by [10]
Er represents the RSSI value in the absence of a signal, commonly referred to as noise according to [10]. The refinement
process involves adjusting the Er value provided by [54],
taking into account real-time electronics and transducer temperature [25]. Another approach, aligned with [10] and [23],
employs an in situ method by evaluating the lowest bin where
the signal-to-noise ratio approaches unity. Heywood et al.
[23] observed significant noise during ship transit, resulting
in a notable reduction in zooplankton estimation. Achieving
precise measurements becomes challenging when the vessel
is in motion due to the potential presence of flow noise. This
complicates the accuracy of the measurements, as the flow
noise is likely to contribute to the overall instrument noise
level [16]. To address this, calibrations were performed for
each of the four beams while the ship maintained a stationary
position over the CTD. The noise level for each profile was
estimated by considering the lowest bin RSSI values.
Er values for pre-upwelling and late-upwelling phases
were determined separately by calculating the average noise
values across all profiles. In the data processing for preupwelling and late-upwelling phases, profiles were excluded
if the noise exceeded 10 counts beyond the established mean
Er values, as recommended by [24]. Furthermore, in accordance with [24], the presence of backscatter in a given bin was
marked as absent if the signal strength lagged by less than 5
counts compared to the noise level. This systematic approach
ensured the reliability of Er values for each upwelling phase,
as detailed in Table 2.
2.10 Correlation Analysis and Zooplankton
Classification
We used linear regression (log(B) mS v + b, where m and
b are the slope and intercept, respectively) to estimate zooplankton biomass (B, in mg m−3 ) from ADCP backscatter
(S v , in dB), which is a well-established approach [12, 19,
23, 28]. To match the timing of net sampling, we averaged
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Fig. 2 Sound absorption coefficient profiles typically during the pre-upwelling and late-upwelling phases
Table 2 Different Er Values are given below
Phase/Beam
Pre-upwelling
Late-upwelling
Beam1
26
24
Beam2
23
21
Beam3
25
24
Beam4
23
21
ADCP backscatter over 10-min ensemble intervals corresponding to the net sampling periods at each station (Fig. 1).
This approach ensured that the backscatter data used for estimating biomass was from the same time frame as the net
sampling. The study also aimed to identify which zooplankton categories are most effectively detected by the ADCP
and the dominant scatterers at an operating frequency of
76.8 kHz. To test the hypothesis that specific components of
the zooplankton biomass have a more significant influence on
the measured S v than others, following [57], we evaluated the
correlations between zooplankton abundance across different
groups, collected at the same time and S v using the Spearman
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rank correlation coefficient. This approach was chosen as the
data distribution was non-normal, and randomization methods were used to assess the significance of these correlations.
In this approach, we randomly paired S v values with each
zooplankton group and calculated the correlations between
the abundance of each group and S v . This method helps determine whether observed correlations are significant or could
have arisen by chance. This study classified zooplankton
based on their acoustic scattering properties and anatomical
features [43, 68, 73]. We categorized them into three classes:
fluid-like scatterers (including small crustaceans such as
Decapoda, Copepoda, Ostracoda, and Amphipoda, as well
as other organisms like Chaetognatha, Doliolids, Copelata,
and Salps), Elastic-shelled scatterers (such as Bivalve and
Heteropoda), and Gas-bearing scatterers (primarily fish larvae).
2.11 Estimation of Zooplankton Migration Velocity
ADCP data are also explored to investigate the vertical
movement of scattering particles, primarily zooplankton, in
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the water column. The ADCP measures the speed of particles, and under the assumption of minimal vertical water
movements such as upwelling or downwelling, the detected
vertical velocity is attributed to the swimming activity of
zooplankton [24]. The ADCP data thus provide qualitative
insights into zooplankton biomass and vertical movement
[17]. Zooplankton typically migrate upward at dusk and
downward at dawn due to DVM, and these vertical velocity
patterns help confirm their presence and rate of movement. We averaged vertical velocities over 30-min ensemble
intervals [24] to reduce measurement errors, mainly minimizing uncertainty in single-ping ADCP data. This approach
enhanced the reliability of vertical velocity measurements
by comparing backscatter, indicating zooplankton presence,
with vertical velocity in water columns, as discussed in the
DVM section, noting that data could be erratic and less reliable when the ship was in motion.
3 Results
3.1 Physical Forcing and Biotic-Abiotic Interactions:
Pre-Upwelling versus Late-Upwelling Dynamics
3.1.1 Pre-Upwelling Phase
During the pre-upwelling phase in the study area, indications
of upwelling characteristics are discernible from offshore
Ekman mass transport (EMT) data (Fig. 3a), showing negative indices predominate along the coast except for the
12–14°N region where the values were near zero or positive.
Despite a relatively weak transport rate (up to −200 kg/m/s),
upwelling intensification was noted in the southern region,
with values reaching −600 kg/m/s, correlating with lower
SST dropping to 28.75 °C around 8°N south. Despite the
warmer SSTs prevailing along the entire coast (> 28 °C),
slight indications of upwelling are inferred from surface temperature contrasts recorded by CTD profiles, with notable
thermal contrasts between coastal and offshore waters up to
11.5°N. This contrast aligns with higher densities observed
near the coast than the offshore EMT, favouring upwelling
conditions up to 11.5°N. Interestingly, observations near
13°N along the coast present a nuanced contrast. While densities are higher near the coast than in surrounding offshore
waters, coastal SSTs near 13°N are relatively warmer than
offshore waters, and higher MLDs of up to 46 m suggest
weak/negligible upwelling conditions in this region.
North of 13°N, coastal SSTs rise above 30.5 °C, while to
the south of 13°N, SSTs remain below this threshold, aligning
with the earlier records on upwelling in the early phase and
that the upwelling progress in its pattern and records along the
entire coast as the monsoon establishes by mid-July [55, 62,
72, 77]. The SEAS upwelling is triggered by the combined
role of local Ekman forcing (along shore wind stress) and
remote forcing, namely coastal Kelvin waves and the west
propagating Rossby waves [20, 64, 65] and the upwelling
along 9–13°N lat is more influenced due to coastal Kelvin
and Rossby waves [72]. In brief, the response in the SST,
Chl-a, MLD, and zooplankton biomass (Fig. 3) indicating
the presence of upwelling is defensible.
Surface Chl-a concentrations near the coast range from 0.1
to 0.35 mg/m3 , with distinct peaks in zooplankton biomass (~
0.06 g/m3 ) observed in pockets at coordinates 8°N 76°E and
10°N near the coast. Shallow MLDs of 20 and 22 m are linked
to these peaks. An analysis of total geostrophic and winddriven circulation patterns revealed a prominent cyclonic
circulation in the southern region with a distinct cyclonic
eddy core precisely located at 8°N 76°E (see Fig. 4a). This
feature correlates with a low MLD and a peak in zooplankton
biomass, observed on 28 May. Notably, the cyclonic eddy is
associated with a low regional SLA.
A strong density gradient was observed near the coast
at 10°N, accompanied by a weak offshore transport ranging from −200 to −300 kg/m/s. The SST surpassing 30 °C
indicated weak upwelling, marked by the retention of warm
surface waters, undisturbed by deeper, cooler waters. Indication of the weak upwelling was reflected in vertical Chl-a
profiles, notably at a depth of 20m, where Chl-a concentration
exhibited a distinct increase of approximately 0.55 mg/m3
(see Fig. 5, Station 4). This observed increase in Chl-a concentration at 20 m and the weak physical dynamics prevailing
over the region (restricting the horizontal exchange) suggests the presence of the subsurface chlorophyll-a maximum
(SCM) likely playing a pivotal role in sustaining zooplankton
biomass within the MLD.
Observations on 6 June identified a shallow MLD (22 m)
area at 11.5°N, 71°E, with high zooplankton concentration.
This low MLD found with the presence of anticlockwise
circulation patterns and low SLA showed the presence of
cyclonic eddy during this period (see Fig. 4b). Relative zooplankton biomass was higher than that of surrounding waters
for this offshore location. Also, SST was found to be low
compared to surrounding waters. The convergence of shallow MLD, cyclonic circulation, and reduced SST for 8°N
76°E and the offshore region at 11.5°N 71°N highlights the
regulatory roles played by the regional physical mechanism,
divergence caused due by the presence of cold-core eddies,
resulting in the occurrence of high zooplankton concentration.
In the NEAS, the highest recorded zooplankton biomass
(0.048 g/m3 ) was observed at approximately 19°N, 69E, at
a single station in the offshore region. The nearby waters
were also associated with the moderately high surface Chl-a
concentration (0.42 mg/m3 ), with the highest concentration
observed in the study area. Despite SST exceeding 31 °C
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Fig. 3 Spatial distribution of environmental parameters during pre-upwelling phase. Each subplot depicts a distinct parameter, with bubble size and
colour bar collectively signifying concentration levels
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Fig. 4 Weekly snapshot of current direction and velocity overlaid with SLA for the periods 25–31 May, 3–9 June, and 7–13 September 2005,
corresponding to environmental variables on their respective dates. The yellow box highlights the cyclonic eddy region
and MLD measuring around 38 m for this region, zooplankton and Chl-a concentrations persist at relatively high levels
compared to other offshore areas. Vertical Chl-a profiles further underscore significant variations, particularly notable at
a depth of 20 m, where a discernible increase in Chl-a concentration, approximately 0.67 mg/m3 , was observed (refer
to Fig. 5, Station 13). This elevation indicates the presence
of SCM, which is responsible for sustaining localized zooplankton biomass, similar to the observation near the coast
at 10°N. Low biomass in circles (Fig. 3f) outside the eddy
and SCM reflects the generally less productivity of the EAS
during the initial phase of the SM and coastal upwelling, particularly in offshore regions, except for the more productive
southern sector of the EAS.
3.1.2 Late-Upwelling Phase
As displayed in the offshore EMT data (Fig. 6), observations
during the late-upwelling phase show significant upwelling
intensity characterized by negative indices, notably exceeding < −1200 kg/m/s, extending from the southern regions to
10°N. The highest upwelling intensity was concentrated in
this area, gradually decreasing north of 10°N but remaining
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Fig. 5 Left panel shows station locations for Chl-a. Right panel indicates vertical Chl-a profiles during pre-upwelling period
significant with values below −600 kg/m/s up to approximately 15°N. Beyond this latitude, negative indices persist,
indicating the prevalence of upwelling along the entire coast.
Coastal SSTs were notably cool during the period due to the
upwelling, particularly around 8°N and 10°N, where SSTs
dropped below 27 °C, in contrast to the warmer conditions
observed during the pre-upwelling period. The high density
of around 22.5 kg/m3 was observed during this period in
these regions, highlighting significant upwelling activity.
The convergence of MLD observed near the coast extends
up to 17°N, indicative of the upwelling effect. The shallowest
MLD was observed near the coast of Kochi (10°N), attributed
to strong upwelling, where surface Chl-a concentration was
highest (1.98 mg/m3 ) and SST was lowest (24.72 °C).
Zooplankton biomass was also highest (0.81 g/m3 ) at this
location. Additionally, a secondary surface Chl-a peak
(1.45 mg/m3 ) was observed at 11.5°N 74°E, surpassing the
southern latitude (8°N). However, despite the higher Chl-a
concentration, the zooplankton biomass was relatively lower
in this area compared to the southern latitude. This discrepancy may be attributed to grazing or to the regional mixing
123
that happened just a few days before the observation, which
triggered nutrient enrichment and enhanced phytoplankton
production, and the observation was done before the secondary production was established. In another instance of
shallow MLD, but deprived of mixing or upwelling and the
resultant Chl-a, the zooplankton concentration also showed
lower values (< 0.1 g/m3 ) as observed at 17°N near the coast,
indicating a stratified less productive region just north of
the active upwelling region. These highlight the intricate
interplay of various environmental and biological factors
influencing zooplankton biomass dynamics.
The second peak in zooplankton biomass was observed
on 10 September at the offshore location of 8°N, 75°E.
Analysis of total geostrophic and wind-driven current circulation patterns revealed a prominent cyclonic circulation
in the southern region, accompanied by low SLAs (see
Fig. 4c). The MLD was observed to be low, approximately
22 m, with surface water temperatures measured by CTD
below 27 °C. Both these parameters exhibited lower values
than the surrounding waters, further confirming the presence of a cyclonic eddy in this location [21, 42, 49, 61].
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Fig. 6 Spatial distribution of environmental parameters during late-upwelling phase. Each subplot depicts a distinct parameter, with bubble size
and colour bar collectively signifying concentration levels
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This cyclonic eddy also causes an increase in zooplankton
biomass in the region, similar to observations at 8°N 76°E
during the pre-upwelling phase. The observation supports
[49], that the high variance in spatial distribution of zooplankton in the upper layer (100–150 m) will be associated
with the region of maximum potential energy of the eddy
field. This increase was attributed to favourable conditions
for zooplankton growth and propagation, including vertical
mixing, and the anticipated enhanced nutrient availability
and biological production.
3.2 Zooplankton Biomass and ADCP Backscatter
Relationship
The scatter plot delineates specific sampling points of each
station within the MLD, with the x-axis representing ADCP
backscatter (computed S v , in dB re 4πm-1 based on Eq. 2)
values and the y-axis corresponding to log-transformed zooplankton biomass values (log(B), in mg/m3 ) from the MPN
collected during the pre and late-upwelling period. The
ADCP backscatter data were vertically averaged to the MLD,
where the samples were taken at each station.
As indicated in Fig. 7, a positive correlation between
ADCP backscatter and zooplankton biomass was observed
during the pre-upwelling period. The regression analysis for
pre-upwelling period (r 0.58) yielded the equation log(B)
5.39 + 0.05 Sv, indicating a strong relationship between
S v and log(B). In contrast, late-upwelling period exhibited a
weaker correlation (r 0.25), with the regression equation
log(B) 3.10 + 0.02 Sv, suggesting that while there is a
positive trend in both periods, the correlation is more robust
during the earlier pre-upwelling phase.
In detail, in the pre-upwelling daytime period, S v showed
a mean of −90.35 dB with a standard deviation (SD) of
3.48 dB, indicating moderate variability. The log(B) had
a mean of 1.25 (SD 0.30). Night-time data showed
slightly higher S v (mean −87.75 dB, SD 2.88 dB) and
log(B) (mean 1.35, SD 0.21). The t-test comparison of
slopes indicated no significant difference between daytime
(r 0.55) and night-time (r 0.61) conditions, suggesting
that the backscatter-biomass relationship remains consistent
throughout the day.
During the late-upwelling daytime period, the S v variance
increased significantly, with an SD of 4.28 dB (mean −
94.55 dB). Despite the increase in mean log(B) (mean 1.61, SD 0.33), the mean S v decreased compared to the
pre-upwelling period. Night-time data in the late-upwelling
phase showed a similar trend, with a mean S v of -90.45 dB
(SD 4.16 dB) and a mean log(B) of 1.72 (SD 0.22). The
ADCP provided a robust estimate of zooplankton biomass,
with a strong correlation during the pre-upwelling period
(r 0.58). Despite a weakened correlation late-upwelling
(r 0.25), the increased variability in backscatter during
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this phase highlights the instrument’s sensitivity to biological
changes, affirming its effectiveness in detecting spatial and
temporal shifts in zooplankton biomass in the study period.
3.3 Zooplankton Abundance and Backscatter
During both pre-upwelling and late-upwelling periods in the
EAS, copepods consistently dominated the net zooplankton
abundance (Fig. 8), reinforcing the widespread pattern of
copepod prevalence in marine pelagic systems [36]. In the
pre-upwelling phase, copepods and fish eggs (8.7%) were
prominent, but their relative abundance decreased in the lateupwelling phase. Conversely, ostracods notably increased
from 7.1% in the pre-upwelling period to 12.5% in the
late-upwelling period. The pre-upwelling phase also showed
significant abundance from chaetognaths, pteropods, and
copepods. In contrast, the late-upwelling phase increased the
abundance of siphonophores, chaetognaths, euphausiids, and
decapod zooplankton groups.
The backscatter data depicted in Fig. 9, with the x-axis
representing categorized ranges of backscatter values and the
y-axis indicating the cumulative sum of zooplankton density
values within each range, is stratified into three equidistant
categories (of 4 dB and 6 dB). These categories delineate backscatter values observed during two distinct periods: the pre-upwelling phase and the late-upwelling phase.
The graphical representation suggests a positive correlation between zooplankton abundance and higher backscatter
values across both temporal phases. This correlation indicates that higher zooplankton populations correspond with
increased backscatter readings. Volume backscattering is
affected by factors such as the abundance and biomass of
scatterers, their taxonomic composition, and variations in
acoustic properties [74]. Moreover, [26] established a direct
correlation between the logarithm of zooplankton density
and acoustic volume backscattering strength. The pattern
observed in the EAS aligns with these findings, suggesting
that zooplankton dynamics can significantly impact underwater acoustic phenomena.
The present study shows a significant correlation between
various zooplankton groups and acoustic backscatter during
pre-upwelling and late-upwelling periods (Table 3). Fish larvae, a gas-bearing category, consistently exhibited a strong
correlation with backscatter across both periods, highlighting their importance as scatterers regardless of upwelling
conditions (r 0.51, p 0.001 pre-upwelling; r 0.55, p <
0.001 late-upwelling). In the pre-upwelling period, fluid-like
scatterers, including crustaceans such as decapoda (r 0.54,
p < 0.001), ostracoda (r 0.43, p 0.007), and copepoda
(r 0.43, p 0.007), along with copelata (r 0.39, p 0.015) and chaetognatha (r 0.35, p 0.027), were significant contributors to backscatter. Additionally, bivalves, an
elastic-shelled category (r 0.40, p 0.012), also played
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Fig. 7 Correlation between the zooplankton biomass (dry weight mg/ m3 ) and the vertically averaged ADCP backscatter (dB) in MLD for (a) preupwelling and (b) late-upwelling phases
Fig. 8 Percentage contribution of Zooplankton abundance during (a) pre-upwelling and (b) late-upwelling phase
a notable role. During the late-upwelling period, the importance of fluid-like scatterers shifted slightly, with copepoda
(r 0.36, p 0.008), fish eggs (r 0.31, p 0.024), and
doliolids (r 0.29, p 0.036) were the primary contributors
to backscatter. Heteropoda, an elastic-shelled scatterer (r 0.35, p 0.011), also emerged as a significant source during
this period. These results demonstrate the varying contributions of different zooplankton groups to acoustic backscatter
in response to changes in upwelling conditions.
3.4 DVM
The vertical variation in ADCP backscatter across two transects at 8°N and 10°N and one stationary location was
analysed to understand zooplankton’s DVM (Fig. 10a, b).
Elevated backscatter values (> −90 dB) were consistently
observed from the surface to approximately 80 m depth during night-time (18:00–06:00 IST), indicating zooplankton
aggregation near the surface. This pattern was evident during the ship’s continuous movement and the stationary period
at 8°N (Fig. 10b).
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Fig. 9 Stratified Zooplankton Density (in situ) Relative to Backscatter Intensity Ranges (from ADCP S v )
Table 3 Spearman Correlation
(S_Corr) between individual
zooplankton groups and their
abundance with backscatter
during the pre-upwelling and
late-upwelling phases
Zoo_gps
pre-upwelling
S_Corr P-value
Zoo_gps
S_Corr P-value
late-upwelling
Fish larvae
0.55
0.000
Decapoda
0.54
0.000
Fish larvae
0.51
0.001
Copepoda
0.36
0.008
Ostracoda
0.43
0.007
Heteropoda
0.35
0.011
Other organisms
0.43
0.007
Fish eggs
0.31
0.024
Copepoda
0.43
0.007
Doliolids
0.29
0.036
Bivalve
0.40
0.012
Amphipoda
0.26
0.064
Copelata
0.39
0.015
Copelata
0.22
0.118
Chaetognatha
0.35
0.027
Salps
0.22
0.122
Fish eggs
0.29
0.072
Other organisms
0.20
0.146
Significant values are shaded in blue.
Hourly vertical backscatter data averaged up to 80 m depth
(B80 in this section) revealed distinct spatiotemporal variability in zooplankton populations. At the 8°N transect on
May 28, 2005, B80 values decreased from −92 dB at 10
AM to a minimum of −95 dB at 1 PM, suggesting a downward migration or reduced population of scatterers during
midday. A subsequent increase in B80 from −94 dB at 3
PM to −87 dB at 8 PM indicates an upward migration or
increased population of organisms in shallower waters, with
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the most significant increase (4 dB) occurring between 6 PM
(−92 dB) and 7 PM (−88 dB) reflects the upward migration
pattern (Fig. 10b).
For the 10°N transects on June 1, 2005, B80 initially
decreased from −93 dB at 6:00 AM to −95 dB by 8:00
AM. Following this, the values fluctuated and significantly
increased to −85 dB by 9:00 PM, mirroring the pattern
observed at 8°N. The highest increase in B80 (4 dB) was
again observed between 6:00 PM (−92 dB) and 7:00 PM
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Fig. 10 (a) Ship transects along 8°N and 10°N (green: daytime, blue: night-time) with a stationary point at ~ 8°N, 73°E (red dot), (b) ADCP
backscatter along 8°N and 10°N transects (ship in motion) and at ~ 8°N, 73°E (ship stationary) and (c)Vertical velocity at ~ 8°N, 73°E (ship
stationary)
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(−88 dB), reflecting the upward migration pattern (Fig. 10b).
A notable reduction in B80 (3 dB) from −86 dB at 5:00 AM
to −89 dB at 6:00 PM reflects the downward migration pattern (Fig. 10b).
The 8°N stationary data on May 29, 2005, displayed a
similar pattern, with B80 values ranging from −93 dB in
the early morning to −88 dB in the evening. The highest
increase in B80 (3 dB) occurred between 6:00 PM (−92 dB)
and 7:00 PM (−89 dB), coinciding with the highest vertical
velocity (7.2 cm/s) during this period, indicating an upward
migration (Fig. 10c). Throughout the night, B80 remained
elevated around −88 dB, suggesting that zooplankton continued migrating upward or near the surface. Similarly to
the 10°N transect, B80 decreased by 3 dB from 5 to 6 AM,
with a minimum vertical velocity of −7.7 cm/s, indicating significant downward migration. Hourly vertical velocity
averaged up to 150 m depth also showed a clear diel pattern.
The lowest hourly downward velocities (−3.31 cm/s) were
observed at 5:00 AM, and the highest hourly upward velocities (4.13 cm/s) occurred at 7:00 PM. The observed behaviour
aligns with the classic DVM pattern, characterized by zooplankton migrating downward at dawn (5–6 AM) and upward
at dusk (6–7 PM), reflecting the relationship between zooplankton movement and backscatter intensity.
4 Discussion
4.1 Physical Forcing and Biotic-Abiotic Interactions:
Pre-Upwelling versus Late-Upwelling Dynamics
The EAS, with two distinct ecosystems at north and south,
has several distinctive features in its plankton community
compared to the rest of the AS [30, 60]. Hydrography and
circulation patterns in marine ecosystems affect primary
and secondary production, including zooplankton biomass
and distribution. This study focused on how temperature,
density and biotic factors like Chl-a concentration influence zooplankton during pre-upwelling and late-upwelling
periods. Coastal upwelling [18, 70, 72, 77] and associated
processes are essential in determining the spatiotemporal
pattern in biological production, either at primary or secondary levels. The combination of a shallow MLD and high
zooplankton biomass underscores the presence of a productive hotspot. These are maintained by a regional mechanism
like mesoscale processes as observed in association with the
coastal upwelling [15, 27, 37, 61, 63, 72]. The cyclonic eddy
observed during the pre-upwelling phase at 8°N 76°E aligns
with low MLD and high zooplankton concentrations, underscoring the role of cyclonic features in promoting upwelling
and nutrient enrichment.
Similarly, the presence of a cyclonic eddy at an offshore
location (11.5°N 71°E) is an influential aspect in promoting
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high zooplankton biomass. The Okubo-Weiss parameterization and the interannual variation addressed in the study
explain high Chl-a supported by the upwelling jointly with
the cold-core eddies, which are more prominent during
the SM. Density stratification acts as a barrier to nutrient exchange, limiting nutrient availability in the surface
layer and thereby affecting surface biological production,
while dissolved organic substances in the subsurface support production at deeper layers, leading to an abundance of
shade-loving organisms and the formation of a SCM, a common phenomenon in stratified seas [8, 29, 53]. The highest
zooplankton biomass in offshore regions, particularly around
19°N 69°E and 10°N near the coast, supports the presence
of SCM, which likely contributes to the high zooplankton
biomass by enhancing primary production.
The late-upwelling phase demonstrates a more robust process, with substantial negative EMT indices extending to
15°N. The most increased zooplankton biomass at 10°N,
observed with the shallowest MLD and highest Chl-a concentration, underscores the critical role of upwelling in increasing primary productivity and zooplankton biomass. The
observed zooplankton biomass at different latitudes despite
high Chl-a concentrations suggests differential regional factors, such as grazing pressure or recent mixing events, that
can impact zooplankton dynamics. The cyclonic eddy at 8°N
75°E, identified in the late-upwelling phase, plays a vital role
in modulating local upwelling conditions and zooplankton
biomass.
In brief, the abundance distribution pattern and regulatory
mechanism that determine the spatial variation of zooplankton thus appear to be coastal upwelling, currents, eddies [79],
SCM, and the prevailing biological dynamics (such as grazing). Pre-upwelling shows localized zooplankton increases
due to SCM and cyclonic patterns, while late upwelling leads
to higher biomass due to intensified upwelling and cyclonic
features.
4.2 Zooplankton Biomass and ADCP Backscatter
Relationship
The analysis of ADCP backscatter and zooplankton biomass
reveals key insights into biological productivity during
upwelling phases. The statistically significant positive correlation between ADCP backscatter and log-transformed
zooplankton biomass during the pre-upwelling period (r 0.58) demonstrates the ADCP’s effectiveness as a tool for
estimating zooplankton biomass. During the pre-upwelling
period, the relationship between backscatter and biomass
remained consistent between day and night, indicating that
diel variations do not significantly impact the ADCP’s measurement of zooplankton biomass. The higher correlation
observed at night is likely due to the increase in zooplankton biomass as most of the migrating community consists
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of larger organisms (> 2 mm) detectable by the ADCP
[41]. This strong correlation indicates a reliable relationship between the acoustic signal and zooplankton biomass,
making ADCP backscatter a robust proxy for zooplankton
biomass under stable pre-upwelling conditions. The ADCP
effectively captured and measured zooplankton biomass,
providing consistent and reliable baseline data, which is
essential for understanding typical zooplankton distribution
and establishing reference conditions against which lateupwelling changes can be compared.
The weak correlation during the late-upwelling period
(r 0.25) suggests changes in the relationship between
ADCP backscatter and zooplankton biomass, likely due to
shifts in the biological and physical environment. While the
correlation remains positive, the decrease in mean backscatter despite higher zooplankton biomass suggests that the
ADCP’s sensitivity to certain zooplankton types or sizes may
be reduced under these conditions. The increased variability
in both backscatter and biomass highlights the significant
impact of zooplankton size and taxonomic composition on
backscatter readings [68]. Several factors may explain the
low correlation observed during the late-upwelling period.
Echo intensity may underestimate the biomass of organisms close to the detection threshold size, as noted by [52].
Consequently, the 76.8 kHz frequency of the ADCP, which
detects objects as small as 2 mm, may also underestimate the
contribution of smaller zooplankton (as observed during the
late phase of upwelling) to the overall biomass, potentially
masking their presence in the backscatter data. Although a
MPN with a 200 μm mesh size was used to sample smaller
zooplankton, the ADCP’s limitations could still mask these
organisms in the backscatter data. Study on contribution
of size-fractioned biomass measurements also revealed that
zooplankton smaller than 2 mm contributed more to the overall zooplankton biomass during the daytime compared to
zooplankton larger than 2 mm [41]. The patchy distribution of
zooplankton can introduce variability in backscatter that does
not always align with overall biomass, further complicating the correlation [47]. Additionally, the study suggests that
Particulate Organic Carbon (POC) may influence backscatter readings [6]. However, given the low frequency used in
our analysis, POC is unlikely to affect the biomass estimates significantly. Calibration issues between the ADCP
and traditional net sampling methods may also contribute to
discrepancies. Factors like net avoidance, volume discrepancies, towing depth uncertainties, and background noise can
distort backscatter measurements [10, 23, 51].
The ADCP’s high-resolution data are essential for studying zooplankton dynamics and ecosystem changes, and the
present study shows the approach is effective in upwelling
environments. Results show its usefulness in tracking zooplankton abundance, with a notable correlation during the
pre-upwelling phase. Future studies should carefully consider sampling techniques and assess how detection thresholds affect smaller zooplankton. Including size-specific data
will refine biomass estimates and enhance the reliability of
ADCP measurements for monitoring zooplankton biomass
and evaluating the impacts of upwelling on marine ecosystems.
4.3 Zooplankton Abundance and Backscatter
The predominance of copepods in zooplankton abundance
during both periods underscores their crucial role in the
EAS. The reduction in fish eggs and the increase in ostracod abundance from pre-upwelling to late upwelling indicate
significant shifts in zooplankton community composition.
These shifts and the rise in groups such as siphonophores
and euphausiids suggest that environmental changes associated with upwelling drive dynamic alterations in zooplankton
populations. The positive correlation between zooplankton
abundance and backscatter values highlights the effectiveness of acoustic methods for estimating zooplankton
populations, consistent with previous research [26]. Volume backscattering, influenced by scatterer abundance and
acoustic properties, supports the use of acoustic data for
monitoring zooplankton dynamics and their impact on underwater acoustic environments during both pre-upwelling and
late-upwelling phases.
In underwater acoustics and fisheries studies, the assumption that scattering from individual animals accumulates
incoherently leads to a linear increase in the volume backscattering coefficient with animal density, provided there are
no significant acoustic attenuation, multiple scattering, or
target density variations [34, 39]. This principle is evident
in our study, where fish larvae consistently contributed to
backscatter across both pre-upwelling and late-upwelling
periods. Their gas-bearing characteristics make them reliable scatterers at 76.8 kHz, serving as dependable proxies
for zooplankton abundance. Despite their lower abundance
compared to other zooplankton groups, fish larvae emerge
as dominant scatterers, highlighting that even less abundant
zooplankton can substantially impact acoustic backscatter in
this region. However, strong scatterers like fish larvae can
mask the detection of weaker scatterers such as crustaceans,
potentially complicating the interpretation of backscatter
data [81]. During the pre-upwelling period, fluid-like scatterers, such as decapods, ostracods, copepods, copelata,
and chaetognaths, along with elastic-shelled bivalves, significantly contributed to backscatter. In contrast, the lateupwelling period showed a shift in the dominant scatterers,
with copepods, fish eggs, doliolids, and elastic-shelled heteropods becoming the primary contributors, reflecting their
varying acoustic properties. These shifts suggest changes
in zooplankton community structure or distribution, directly
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impacting the backscatter signal. As observed in other studies, the lack of significant correlations for certain groups,
particularly fluid-like scatterers, may be due to their response
at higher frequencies (120 and 200 kHz) [81]. Additionally,
echograms revealed that many scatterers were organized in
layers, while patches, primarily consisting of gas-bearing
organisms, were especially prevalent during the daytime [5].
These findings highlight the dynamic nature of zooplankton
communities and their impact on acoustic backscatter, underscoring the importance of considering temporal changes in
zooplankton composition for accurate biomass and distribution assessments in upwelling-affected regions.
zooplankton layers with average speeds of around 2 cm/s and
maximum speeds of up to 8 cm/s. DVM surveys have traditionally been both demanding and time-consuming. Recent
advances in acoustic technology have mostly simplified these
processes by reducing the time and effort required. These
innovations allow additional precise capture of migration
patterns with detailed pursuit of zooplankton behaviour and
a clearer understanding of their spatial distribution. While
moored ADCP systems provide practical time-series observations, VM systems offer high-resolution and real-time data
that is substantial to understanding the zooplankton dynamics.
4.4 DVM
5 Summary and Conclusion
The DVM patterns in ADCP backscatter from the 8°N
and 10°N transects and the stationary site provide valuable
understandings into zooplankton behaviour in response to
environmental cues. Consistently elevated backscatter values
during night-time, extending from the surface to approximately 80 m depth, indicate zooplankton aggregation near the
surface. This observation aligns with the established concept
of DVM, a fundamental phenomenon in zooplankton ecology [38]. The drop in backscatter values observed during
daytime, particularly from late morning to early afternoon,
suggests either a downward migration of zooplankton or a
decrease in their density in the upper water column. The
subsequent increase in backscatter in the late afternoon and
evening, notably the sharp rise between 6:00 and 7:00 PM,
reflects the onset of upward migration as zooplankton return
to surface waters. This pattern keeps DVM as a widespread
and ecologically meaningful behaviour [23, 24]. The data
from the stationary site at 8°N show a similar diel pattern
with increased backscatter in the evening and sustained high
values throughout the night. This character with the transect
data indicates that the observed migrations are driven by diel
cycles rather than localized conditions.
Furthermore, the relationship between vertical velocity
and backscatter intensity highlights the role of zooplankton
vertical movement in their migration patterns. The highest upward velocities (7.2 cm/s) observed in the evening
correspond with increased backscatter that indicates zooplankton ascent to the surface. Conversely, the downward
velocities (−7.7 cm/s) in the early morning align with
reduced backscatter that shows the descent of zooplankton
to deeper waters. The lowest hourly downward velocities of
−3.31 cm/s were recorded at 5:00 AM, while the highest
upward velocities of 4.13 cm/s occurred at 7:00 PM. These
measurements indicate that the onset of significant vertical
movement in the water column began early in the morning with significant downward velocity, followed by a peak
in upward velocities in the evening. These values are consistent with a previous study by [38] that shows migrating
123
The study explores the distribution and abundance pattern of zooplankton associated with the EAS’s initial and
late-upwelling season using underwater acoustic technology, specifically ADCP backscatter data, along with in situ
net sampling and measured oceanographic parameters. The
research explores the physical processes that affect the
abundance and distribution of zooplankton. It emphasizes
the importance of coastal upwelling, circulation patterns,
mesoscale eddies, and regional stratification. The study finds
localized increases in zooplankton during the pre-upwelling
phase due to the effects of SCM and cyclonic features. In
contrast, during late upwelling, intensified upwelling and
cyclonic patterns result in higher zooplankton biomass, indicating the significant role of these processes in shaping
spatial variations throughout the upwelling cycle. The volume backscattering strength from the ADCP is derived based
on the sound profiles considering the appropriate sound
absorption coefficient, slant range and the backscatter noise.
The findings demonstrate that ADCP backscatter is a reliable indicator of zooplankton biomass within the MLD of
the EAS, particularly during the pre-upwelling phase, where
a strong positive correlation was observed (r 0.58; log(B)
5.39 + 0.05 Sv) and weaker correlation during the lateupwelling period (r 0.25; log(B) 3.10 + 0.02 Sv).
While the correlation indicates reliability, the reduced r
value in the late-upwelling phase suggests variability in the
backscatter-biomass relationship, potentially due to changes
in zooplankton size, distribution, or environmental conditions. Future studies should refine sampling techniques and
account for detection thresholds for smaller zooplankton to
enhance the accuracy of ADCP measurements and better
assess the impacts of upwelling on marine ecosystems. This
correlation highlights the potential utility of ADCP backscatter as a tool for ecosystem assessment and management and
provides a deeper understanding of column secondary production within the marine environment. Study demonstrates
that fish larvae consistently act as dominant scatterers due
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to their gas-bearing properties and serve as reliable proxies
for zooplankton abundance across both upwelling phases.
The varying contributions of fluid-like and elastic-shelled
scatterers between phases reflect changes in zooplankton
composition and impact on acoustic backscatter. These variations underscore the need to consider temporal changes
in zooplankton community structure for accurate biomass
and distribution assessments. The DVM of zooplankton
based on the ADCP backscatter data shows a consistent
pattern of upward migration to approximately 80 m during
night-time hours and a downward migration at dawn. The
migration speeds were calculated from the vertical velocity data in the range of 7.2 cm/s for upward movement at
dusk and 7.7 cm/s for downward movement at dawn. Understanding zooplankton dynamics and their ecological impact
relies on the importance of continuous monitoring and timeseries observations, as emphasized by the study. In summary,
rather than deriving a quantification method, the backscatterzooplankton relation can effectively link the plankton group
distribution and abundance pattern to the different physical settings both horizontally and vertically to better define
their habitats. This can be further extended to investigate
prey-predator interactions by combining real-time acoustic
surveys and in situ sampling.
Acknowledgements The authors are grateful to the Secretary, Ministry of Earth Sciences (MoES), the Director, Centre for Marine Living
Resources and Ecology (CMLRE) and MRFP DESK IITM for supporting the study. The first author acknowledges the financial support from
the MoES Research Fellow Programme for carrying out this study. The
preliminary analysis attempted for the paper is published as a general
article in the bulletin ‘Ocean Digest’ published by the Ocean Society
of India in the July 2023 issue (https://www.oceansociety.in/docs/oc
ean/ocean_digest/2023/Ocean-Digest_2023(Vol_10)_Issue_3.pdf) , by
the same authors and few of the figures are repeated in the present
manuscript as well. The authors thank plankton biologists Asha Devi
and Anandavelu I for their insightful discussions. The authors are grateful to the anonymous reviewers for their constructive criticism that
greatly improved this work. This is CMLRE contribution number 193.
Author contribution Shirin J. Jadhav prepared concept, data processing, analysis, plots, and drafting. Smitha B. R. presented concept,
analysis, drafting, and editing.
Funding The work was taken up as part of the Marine Living Resources
Programme of the Ministry of Earth Sciences at CMLRE.
Data availability The data used in the present study are available in the
national repository at Indian National Centre for Ocean Information
Services (INCOIS), Hyderabad.
Declarations
Conflict of interests This study has no conflicts of interest to disclose.
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