The PEACETIME cruise (May–June 2017) was a basin-scale
survey covering the Provencal, Algerian, Tyrrhenian, and Ionian basins during
the post-spring bloom period and was dedicated to tracking the impact of
Saharan dust deposition events on the Mediterranean Sea pelagic ecosystem.
Two such events occurred during this period, and the cruise strategy allowed for the study of the initial phase of the ecosystem response to one dust event in
the Algerian Basin (during 5 d at the so-called “FAST long-duration
station”) as well as the study of a latter response to another dust event in the Tyrrhenian
Basin (by sampling from 5 to 12 d after the deposition). This
paper documents the structural and functional patterns of the zooplankton
component during this survey, including their responses to these two dust
events. The mesozooplankton were sampled at 12 stations using nets with
two different mesh sizes (100 and 200 µm) that were mounted on a Bongo frame for vertical
hauls within the depth layer from 0 to 300 m.
The Algerian and Tyrrhenian basins were found to be quite similar in terms of
hydrological and biological variables, which clearly differentiated them
from the northern Provencal Basin and the eastern Ionian Basin. In general,
total mesozooplankton showed reduced variations in abundance and biomass
values over the whole area, with a noticeable contribution from the small size
fraction (<500µm) of up to 50 % with respect to abundance and 25 % with respect to biomass. This small size fraction makes a significant contribution (15 %–21 %) to the mesozooplankton fluxes (carbon demand, grazing pressure,
respiration, and excretion), which is estimated using allometric relationships to the
mesozooplankton size spectrum at all stations. The taxonomic structure was
dominated by copepods, mainly cyclopoid and calanoid copepods, and was completed by
appendicularians, ostracods, and chaetognaths. Zooplankton taxa assemblages,
analyzed using multivariate analysis and rank frequency diagrams, slightly
differed between basins, which is in agreement with recently proposed Mediterranean
regional patterns.
However, the strongest changes in the zooplankton community were linked to the abovementioned
dust deposition events. A synoptic analysis of the two dust events observed
in the Tyrrhenian and Algerian basins, based on the rank frequency
diagrams and a derived index proposed by Mouillot and Lepretre (2000),
delivered a conceptual model of a virtual time series of the zooplankton
community responses after a dust deposition event. The initial phase before
the deposition event (state 0) was dominated by small-sized cells consumed by
their typical zooplankton filter feeders (small copepods and
appendicularians). The disturbed phase during the first 5 d
following the deposition event (state 1) then induced a strong increase in
filter feeders and grazers of larger cells as well as the progressive attraction of
carnivorous species, leading to a sharp increase in the zooplankton
distribution index. Afterward, this index progressively decreased from day 5
to day 12 following the event, highlighting a diversification of the community (state 2). A 3-week delay was estimated for the index to return to its initial value,
potentially indicating the recovery time of a Mediterranean zooplankton
community after a dust event.
To our knowledge, PEACETIME is the first in situ study that has allowed for the observation
of mesozooplankton responses before and soon after natural Saharan dust
depositions. The change in the rank frequency diagrams of the zooplankton
taxonomic structure is an interesting tool to highlight short-term responses
of zooplankton to episodic dust deposition events. Obviously dust-stimulated
pelagic productivity impacts up to mesozooplankton in terms of strong but
short changes in taxa assemblages and trophic structure, with potential
implications for oligotrophic systems such as the Mediterranean Sea.
Introduction
The Mediterranean Sea is a semi-enclosed basin connected to the Atlantic
Ocean and the Black Sea. It is composed of two major subbasins, the eastern
and western Mediterranean, which are connected by the Sicilian Strait (Skliris, 2014). Due to its characteristics, such as its unique thermohaline circulation pattern and deep-water formation process, the Mediterranean Sea can be considered as a model of the world's oceans (Bethoux et al., 1999; Lejeusne et al., 2010). In addition, it is considered to be oligotrophic with an excess of carbon, a deficiency of phosphorus relative
to nitrogen (Durrieu de Madron et al., 2011), and a decreasing west–east chlorophyll-a (Chl-a) gradient (i.e., Siokou-Frangou et al., 2010).
For the last 200 years, numerous investigations have documented the
pelagic zooplankton community inhabiting the Mediterranean Sea (Saiz et al.,
2014), including long-term time series (i.e., Fernández de Puelles et
al., 2003; Mazzocchi et al., 2007; Molinero et al., 2008; García-Comas
et al., 2011; Berline et al., 2012) and a succession of oceanographic
surveys covering wide transects during different periods of the year (Kimor
and Wood, 1975; Nowaczyk et al., 2011; Donoso et al., 2017; Siokou et al.,
2019). The regular monitoring of the zooplankton community is essential when
considering the high sensitivity of the Mediterranean Sea to anthropogenic
and climate disturbance (Sazzini et al., 2014). Some of these disturbances
may alter the structure and function of the pelagic ecosystem, and this
is critical considering that marine ecosystems are being altered by
anthropogenic climate change at an unprecedented rate (Chust et al., 2017).
Dust deposition is a major source of micro- and macro-nutrients (Wagener et
al., 2010) that can stimulate primary production (Ridame et al., 2014),
accelerate carbon sedimentation, and possibly accelerate the aggregation of marine particles
(i.e., Neuer et al., 2004; Ternon et al., 2011; Bressac et al., 2014). Large
amounts of Saharan dust can be transported in the atmosphere throughout the
western and eastern Mediterranean Sea region and can then be deposited onto the sea surface
by wet or dry deposition. The PEACETIME oceanographic cruise, carried out
between 10 May and 11 June 2017, was designed to study the processes
occurring in the Mediterranean Sea after atmospheric dust deposition in situ, including their impact on marine nutrient budget and fluxes and their affect on the biogeochemical
function of the pelagic ecosystem. Thus, the survey strategy was designed
to be flexible in order to be able to change the sampling area depending on
atmospheric events (Guieu et al., 2020). Consequently, the survey
sampling program realized consisted of 14 oceanographic stations in the
central and western parts of the Mediterranean Sea.
The aims of the present contribution to the PEACETIME project are (1) to
document the zooplankton abundance, biomass, and size distribution along the
survey transect, paying special attention to small-sized zooplankton; (2) to
analyze the relationship between zooplankton structure and environmental
variability, including dust deposition; and (3) to estimate the bottom-up
(nutrient regeneration) and the top-down (grazing) impact of zooplankton on
phytoplankton stock and production by estimating its ingestion, respiration, and ammonium and phosphate excretions using allometric models.
These objectives will serve to test the following research questions:
Did the Saharan dust events impact the zooplankton community structure following
deposition?
If the Saharan dust events did impact the zooplankton community structure, would the effect be immediately
observable or would there be a lag time?
Would changes in zooplankton community
structure driven by dust deposition exceed regional differences under oligotrophic conditions?
Material and methodsStudy area and environmental variables
The PEACETIME cruise survey was conducted in May–June 2017 in the western
Mediterranean Sea (Fig. 1) onboard the R/V POURQUOI PAS?. Among the 12 stations studied,
10 were sampled once for zooplankton (the short-duration stations ST1–ST9 and the long-duration station TYR), whereas two long-duration stations
(ION and FAST, lasting 3 and 5 d, respectively) were sampled three times.
The station positions along the transect were planned before the cruise in
order to sample the principal ecoregions (see Fig. 4 in Guieu et al.,
2020) with the exception of FAST, which was an opportunistic station to monitor a
wet dust deposition event that occurred on 5 June – a few hours after the
first sampling date (Table 1). A dust event occurred over a large area,
including the southern Tyrrhenian Basin, starting on 10 May and could have
impacted the samples at ST5, TYR, and ST6 which were sampled on 16, 19,
and 22 May, respectively (Cécile Guieu, personal communication, 2020).
Stations sampled during the PEACETIME survey: geographical
information, date, and time of zooplankton net sampling. AB refers to the Algerian Basin,
PB refers to the Provencal Basin, TB refers to the Tyrrhenian Basin, and IB refers to the Ionian Basin.
Station IDAreaLat (N)Long (E)DateTime, GMT+01:00(dd/mm/yyyy)(hh:mm)ST1PB41∘53′516∘20′0012/05/201711:30ST2PB40∘30′376∘43′7913/05/201709:30ST3AB39∘8′007∘41′0114/05/201709:15ST4AB37∘58′997∘58′6115/05/201709:15ST5TB38∘57′1911∘1′4016/05/201707:05TYRTB39∘20′3912∘35′5719/05/201723:00ST6TB38∘48′4614∘29′9822/05/201710:15ST7IB36∘39′4918∘9′2924/05/201702:00ION1IB35∘29′3819∘46′5126/05/201721:59ION2IB35∘29′3819∘46′5127/05/201708:50ION3IB35∘29′3819∘46′5128/05/201708:45ST8IB36∘12′6216∘37′8630/05/201709:05ST9AB38∘8′085∘50′4501/06/200723:00FAST1AB37∘56′812∘54′9904/06/201722:15FAST2AB37∘56′812∘54′9906/06/201709:50FAST3AB37∘56′812∘54′9908/06/201723:45
A map showing the sampling points during the PEACETIME cruise 2017. The
colors of the points indicate the different areas considered over the course
of the study: green dots denote the Provencal Basin (PB), dark blue dots denote the Algerian
Basin (AB), light blue dots denote the Tyrrhenian Basin (TB), and red dots denote the Ionian Basin
(IB).
Hydrological variables (temperature, density, and salinity) were measured on
vertical profiles using a CTD (conductivity, temperature, and depth profiler). Dissolved oxygen was measured using a SBE43
sensor, and the Chl-a concentration was determined from Niskin bottle
samples by high-performance liquid chromatography (HPLC), following the protocol of Ras et al. (2008), and with a
fluorescence sensor coupled to the CTD. Primary production was measured
using the 14C-uptake technique, following the methods detailed in
Marañón et al. (2000). The mixing layer depth (MLD) was
computed using the density difference criterion Δσθ=0.03kgm-3 defined in de Boyer Montégut et al. (2004).
Overview of the main characteristics of the wet dust events
occurring during PEACETIME. Zooplankton sampling was carried out very close
to a CTD cast at all stations except FAST2; at FAST2, sampling was undertaken between two casts: 9 h after the first cast a and 16 h before the second cast b. “*” denotes that the value was measured on 17 May 2017. DCM refers to deep chlorophyll maximum.
Stationsimpacted bydust and the cruisevisit durationCruise strategy withrespect to the dust eventsDates, geographical characteristics, andintensity of the dust events predicted by the model and by observationsZooplanktonsampling date(dd-mm-yyyy)Iron in aerosol(ng m-3)Nutrientsbelow thenutricline: NO3(nmol L-1); PO4 (nmol L-1)Surfaceprimaryproduction (mg C m-3 d-1)Water column(0–250)average Chl-aconcentration (mg m-3)Depth range ofthe DCM strata (m)Meanconcentration of Chl-a onthe DCMstrata (mg m-3)Fluorescence ratios forphytoplankton classes Fmicro:Fnano:Fpicowithin theDCM strataWet dust event;Tyrrhenian,16–22 MayTB stations were scheduledbefore the cruise.The model predicted a dustevent 6 d beforearrival.Dust event took pleace from 10 to 12 May. The whole southern Tyrrhenian Basin area was impacted. The predicted flux from models was >1 g m2 (Desboeufs et al., 2020). The dust event was confirmed by aluminum, iron, and lithogenic Si measured in sediment traps at TYR over 4 d. The cumulative lithogenic fluxes in sediment traps were 153 mg m-2 at 200 m and 207 mg m-2 at 1000 m (Bressac et al., 2020).ST5: 16-05-201757.3841; 1481.680.1270–800.55*21:48:30TYR: 19-05-2017162.354*; 1271.770.1170–800.61*33:40:27ST6: 22-05-2017189.8488; 1361.660.0770–800.36*7:44:49Wet dust event,2–8 JuneThe FAST stationschedule and positionwere determined onboardaccording tothe meteorological event.Dust event took place from 3 to 5 June. The area between the Balearic Islands and the Algerian coast was impacted. The predicted flux from models was 0.5 g m-2 (supplementary information figure SI5 in Guieu et al., 2020). Onboard atmospheric dust deposition observations confirmed a weak wet dust deposition of 0.012 g m-2 (Guieu et al., 2020). The cumulative lithogenic fluxes in sediment traps over 5 d were 50 mg m-2 at 200 m and 70 mg m-2 at 1000 m (Bressac et al., 2020). Water column observations, such as nutrients and trace metals (Van Wambeke et al., 2020, Tovar-Sánchez et al., 2020; Bressac et al., 2020), show a clear imprint of the atmospheric deposition.FAST1: 04-06-2017245.3224; 2462.440.1260–900.42*27:45:28FAST2: 06-06-2017266.0808; 2392.850.14a; 0.18 b60–100 a; 70–90 b0.38a; 0.86 b25:43:32a; 50:30:20bFAST3: 08-06-201744.9135; 1132.040.1070–900.42*20:49:31Data referencesFrançois Dulac(personalcommunication, 2020);Desboeufs et al. (2020);Guieu et al. (2020)Desboeufs et al. (2020);Guieu et al. (2020);Bressac et al. (2020);Tovar-Sánchez et al. (2020);van Wambeke et al. (2020)Tovar-Sánchez et al. (2020)France Van Wambeke(personal communication, 2020)EmilioMaranonand MaríaPerez-Lorenzo,personalcommunication, 2020Julia Uitz andCéline Dimier,personalcommunication, 2020Julia Uitz andCéline Dimier,personalcommunication, 2020Julia Uitz andCéline Dimier,personalcommunication, 2020Julia Uitz andCéline Dimier,personalcommunication, 2020Ancillary data on dust deposition events occurring during the
PEACETIME survey
Guieu et al. (2020) detailed how they used three regional dust
transport models to identify major dust events during the PEACETIME cruise.
Two major wet dust events occurred during the study period (Table 2). The first event
concerned the whole southern Tyrrhenian Basin, had a predicted flux of
>1 g m-2 (Desboeufs et al., 2020), and started on May 10,
several days before the arrival of the vessel in this area. The dust event
was confirmed by aluminum, iron, and lithogenic Si measured in sediment
traps at TYR station 6 to 9 d after the event and had a cumulative (4 d)
lithogenic flux of 153 mg m-2 at 200 m and 207 mg m-2 at 1000 m
(Bressac et al., 2020). The second event was located in the area between the
Balearic Islands and the Algerian coast, occurred from 3 to 5 June, and had a
predicted flux of 0.5 g m-2 (Cécile Guieu, personal communication, 2020) after the arrival
of the vessel in this area (FAST station). The dust event was confirmed by
onboard atmospheric dust deposition samples (Desboeufs et al., 2020); water column observations, such as nutrients and trace metals,
(Tovar-Sánchez et al., 2020); and tracers of dust deposition in sediment
traps that had a cumulative (5 d) lithogenic flux of 50 mg m-2 at 200 m
and 70 mg m-2 at 1000 m (Bressac et al., 2020). The lithogenic flux
values at TYR and FAST are likely underestimated: considering that traps were
placed with a time delay of 6 and 1 d following the dust event, the reported values could represent only a fraction of the total
fluxes. The highest aerosol mass concentrations (around 25 µg m-3) with the highest iron content (245 ng m-3) were measured at
FAST between 1 and 5 June, and the highest trace metal
concentrations in the surface micro-layer were measured on 4 June (Co concentration of 773.6 pM, Cu concentration of 20.1 nM, Fe concentration of 1433.3 nM, and Pb concentration of 1294.7 pM; Tovar-Sánchez et al.,
2020). The chemical composition of rain samples at FAST confirmed the wet
deposition of dust that reached a total particulate flux of 0.012 g m-2 (Fu et al., 2020). The Ionian Basin was the only southern area not
impacted by dust deposition during the PEACETIME cruise; therefore, the results
obtained at the long-duration ION station are considered (for
comparison) to be results from an area that was not recently impacted.
Zooplankton sampling and sample processing
A total of 16 zooplankton samples were collected at 12 stations (Table 1)
using a Bongo frame (double net ring of 60 cm mouth diameter) equipped with nets with a respective 100 and 200 µm mesh size (referred to as N100 and
N200 in the following, respectively) mounted with filtering cod ends. At all sampling stations,
the Bongo frame was vertically towed from a depth of 300 m to the surface at a
constant speed of 1 ms-1. The sample volume was estimated based on the ring
diameter and the towed cable length. The sampling was mostly performed
during the morning, except at ST7, ST9, and TYR, and night tows were also
performed for the long-duration stations FAST and ION. The samples were
preserved in 4 % borax-buffered formalin immediately after the net was
hauled back onto the deck.
The samples were processed using a FlowCAM (Yokogawa Fluid Imaging Technologies Inc. Series VS-IV, benchtop model) (Sieracki et al., 1998) and a ZOOSCAN (Gorsky et al., 2010). One of the
goals of this study was to achieve the determination of the complete size
structure of the zooplankton community by combining plankton nets with different mesh
sizes as well as different analysis techniques (FlowCAM and ZOOSCAN) in order to optimize
the observed size spectrum. The formalin-preserved samples were rinsed with
tap water to remove the formalin. For net N100, the sample was then
split into three size fractions: <200µm (referred to as
N100F<200 in the following), 200–1000 µm (referred to as
N100F200/1000 in the following), and >1000µm (referred to as
N100F>1000 in the following). For net N200, the sample was split into two size
fractions: <1000µm (referred to as N200F<1000 in the following) and
>1000µm (referred to as N200F>1000 in the following).
To determine the complete size spectrum, different combinations of size
fractions from the two nets and the two analytical techniques were tested using a
two-way ANOVA. Taking the two mesh sizes (N100 and
N200) into account, the limits of the size spectrum were defined from the
N100F<200 fraction for the lower limit and from the
N200F>1000 fraction for the upper limit. Considering that our FlowCAM
does not detect particles larger than 1200 µm equivalent spherical diameter (ESD) and our ZOOSCAN
does not detect particles smaller than 300 µm ESD,
N100F<200 was analyzed by FlowCAM and N200F>1000 was analyzed by
ZOOSCAN. The intermediate size fractions N100F200/1000 and
N200F<1000 were analyzed with both ZOOSCAN and FlowCAM. These
analyses delivered abundance and biomass values for successive ESD size
classes: <200µm (referred to as C<200), 200–300 µm
(referred to as C200-300), 300–500 µm (referred to as C300-500), 500–1000 µm
(referred to as C500-1000), 1000–2000 µm (referred to as C1000-2000), and >2000µm (referred to as C200-300). The challenge was to choose the best net–analysis
technique combination for the intermediate size fractions (C200-300,
C300-500, and C500-1000). The abundance of each class for the two
nets and the two treatments was statistically compared. Parts of the
spectrum corresponding to the C200-300 and C300-500 fractions from
N100 measured with FlowCAM and to the C500-1000 fraction from N200 measured with the ZOOSCAN have significantly higher
abundances than other net–analysis technique combinations (P<0.000). Consequently, we combined data from N100F<200 and
N100F200-1000 measured with FlowCAM to compute ESD size classes
<500µm (Fig. 2a), and we combined data from N200F<1000 and
N200F>1000 measured with ZOOSCAN to compute ESD size classes
>500µm (see Fig. 2b). The combination of these data enabled
us to compute the final size spectrum (Fig. 2c) that was used to estimate
abundance, biomass, and metabolic rates for each ESD size class as well as for
the whole sample (sum of all of the size classes) and for the total
mesozooplankton (sum of the size classes C200-300, C300-500,
C500-1000, and C1000-2000).
Size spectrum of ION1 (as an example) obtained by (a) FlowCAM
(N100), (b) ZOOSCAN (N200), and (c) a combination of FlowCAM (N100
counting only zooplankton smaller than 500 µm ESD) and ZOOSCAN
(N200 counting only zooplankton bigger than 500 µm ESD).
For the FlowCAM analyses, the sample was concentrated in a given water
volume. An aliquot of each sample was then analyzed using FlowCAM in
auto-image mode. For the N100F<200 fraction, a 4× magnification
and a 300 µm field of view (FOV) flow cell were used, and the analysis was carried out
up to 3000 counted particles. For the N100F200-1000 fraction a
2× magnification and 800 µm FOV flow cell were used, and the analysis
was carried out up to 1500 counted particles.
The digitalized images were analyzed using the
VisualSpeadsheet® software and were manually classified into
taxonomic categories. The living organism groups considered for the FlowCAM data were
copepods, nauplii, crustaceans, appendicularians, gelatinous, chaetognaths,
and other diverse zooplankton groups (such as Polychaeta and Ostracods).
Non-organism particles were classified as detritus, and duplicates and bubbles
were deleted.
To calculate the number of particles in the sample, the following equation
was used:
A=pa×VcVa×Vs,
where A is the abundance (individuals m-3), Pa is the number of particles
in the analyzed aliquot, Vc is the given volume in the concentrated
sample, Va is the volume of the analyzed aliquot (m3), and Vs is the
volume of sea water sampled by the zooplankton net (m3).
For the ZOOSCAN analyses, the sample was homogenized and split using a
Motoda box until a minimum of 1000 particles were obtained. For the
digitalization, the subsample was then placed onto the glass slide of the ZOOSCAN,
and the organisms were manually separated using a wooden spike to avoid
overlapping. After scanning, the images were processed with ZOOPROCESS
(version 7.32) using the Image J image analysis software (Grosjean et
al.,2004; Gorsky et al., 2010). Particles were automatically classified into
taxonomic categories using the Plankton Identifier software
(http://www.obs-vlfr.fr/~gaspari/Plankton_Identifier/index.php, last access: 27 October 2020). The classification
was then manually verified to ensure that every vignette was in the correct
category. The living groups of organisms considered for the ZOOSCAN were
copepods, nauplii, crustaceans, appendicularians, gelatinous, chaetognaths,
and diverse zooplankton (such as Polychaeta and Ostracods). Non-organism particles
were classified as detritus, and blurs and bubbles were deleted.
Normalized biomass size spectrum
The size spectra were computed for each station using combined FlowCAM and
ZOOSCAN data, following Suthers et al. (2006). The data were first
classified into 0.1 mm ESD size categories from 0.2 to 2.0 mm.
The zooplankton biovolume (mm3) was estimated for each category using the following equation:
biovolume=16×π×(ESD)3,
with ESD expressed in millimeters. The x axis of the normalized biomass size spectrum
(NBSS) was calculated by dividing the biovolume by the abundance of each
category and was transformed into Log10. For the y axis, the biovolume of each
category was divided by the difference in biovolume between two consecutive
categories and was transformed into Log10. The NBSS slope and intercept were
determined using a linear regression model. The slope of the NBSS reflects the
balance between small and large individuals, with a steeper slope corresponding
to a higher proportion of small individuals (bottom-up control) and a
flatter slope corresponding to a higher proportion of large individuals (top-down control) (Donoso et al., 2017; Naito et al., 2019).
Zooplankton carbon demand, respiration, and excretion rates
The zooplankton carbon demand (ZCD, in mg C m-3 d-1) was computed
based on estimates of biomass from ZOOSCAN and FlowCAM samples and on
estimates of the growth rate:
ZCD=ration×Bzoo,
where Bzoo is the biomass of zooplankton (in mg C m-3), which is calculated
using the area–weight relationships from Lehette and Hernández-León (2009) and converted to carbon assuming that carbon represents 40 % of the
total body dry weight (Omori and Ikeda, 1984). The ration (d-1) is defined
as the amount of food consumed per unit of biomass per day, and it is calculated as follows:
ration=gz+rA,
where gz is the growth rate, r is the weight-specific respiration, and A
is the assimilation efficiency. gz was calculated following Zhou et al. (2010):
gz(w,T,Ca)=0.033CaCa+205e-0.125Te0.09Tw-0.06
and is a function of sea water temperature (T, ∘C); food availability
(Ca, mg C m-3), which is estimated from Chl a; and the weight of individuals (w, mg C). Here, we consider that the food is phytoplankton, following Calbet et al.
(1996). Following Alcaraz et al. (2007) and Nival et al. (1975), values of r
and A were 0.16 d-1 and 0.7, respectively. ZCD was compared to the
phytoplankton stock (converted to carbon assuming a C / Chl-a ratio of 50 / 1)
and to primary production in order to estimate the potential clearance of
phytoplankton by zooplankton.
Ammonium and phosphorus excretion and oxygen consumption rates were
estimated using the multiple regression model by Ikeda et al. (1985) with
carbon body weight and temperature as independent variables:
lny=a0+a1lnx1+a2x2,
where lny represent the ammonium excretion, phosphorus excretion, or oxygen
consumption; α0, α1, and α2 are constants (see Ikeda et al.,
1985); x1 is the body mass (dry weight, carbon, nitrogen, or phosphorus
weight); and x2 is the habitat temperature (∘C).
The contribution from zooplankton to nutrient regeneration was estimated using the
values of primary production and was converted to nitrogen and phosphorus
requirement using the Redfield ratio. Respiration was converted to respiratory
carbon loss assuming a respiratory quotient for zooplankton of 0.97,
following Ikeda et al. (2000), and was used as the carbon requirement for zooplankton
metabolism.
Data analysis
Spatial patterns of the environmental variables were explored using a
principal component analysis (PCA). We considered temperature, salinity,
dissolved oxygen, and Chl-a values from a fluorescence sensor coupled to a CTD,
using the mean values of the layer from 0 to 300 m as well as the estimated MLD. The
data were normalized prior to the analyses, which were performed using PRIMER v7 software
(Anderson et al., 2008).
Differences in zooplankton abundance and biomass between size classes and
areas were tested using a two-way ANOVA. A one-way ANOVA with a Scheffé
post hoc analysis was applied to compare mean values between areas for total
zooplankton and within each size class. Data from prior analyses were
log transformed and tested for homogeneity. Dunnett's test was used in case
of inhomogeneity. Potential associations between univariate zooplankton and
environmental data were tested using Spearman's rank correlations. These
analyses were performed with Statistica 7 Software. The 100 µm sample
from station TYR was discarded from these analyses due to the poor preservation state of the sample.
In order to study the spatial patterns of zooplankton communities, a taxonomic
group–station matrix was created using the abundance values and was
square root transformed to estimate station similarity using Bray–Curtis
similarity. The similarity matrix was then ordinated using nonmetric
multidimensional scaling (NMDS). The contributions of significant taxa to
the similarity or dissimilarity between stations and areas were tested using
SIMPER. The BIOENV algorithm was then used to select the environmental
variables that best explained the spatial pattern observed for the zooplankton
communities. A PERMANOVA was utilized to test the differences between areas based
on environmental or zooplankton multivariate data. All of these analyses were
performed using PRIMER v7 software (Anderson et al., 2008).
The relationships between the biological and the environmental variables
were also studied by coupling multivariate analyses of two datasets. The
first dataset featured the abundances of all the zooplankton taxa identified
from the 200 µm net samples and the second dataset recorded environmental
variables (the same as for the PCA analysis). A factorial correspondence
analysis (FCA) and a principal component analysis (PCA) were performed on
these two datasets, respectively. The results of the two analyses were then
associated using a co-inertia analysis (Doledec and Chessel, 1994) that was
performed using ADE-4 software (Thioulouse et al., 1997). Prior to the
analyses, the data were log transformed to tend towards the normality of the
distributions.
Rank frequency diagrams (RFD) were created using the data from N200 in order to visualize differences in taxonomic composition between the samples.
To improve the interpretation of the RFDs, we first used a method derived
from Saeedghalati et al. (2017) based on the ordination of the normalized rank
abundance distribution. A rank abundance matrix was created in which the data
were standardized by the total abundance. Resemblance was measured with
Bray–Curtis similarity, and a cluster was created using the complete linkage
criterion. Second, a rank abundance distribution index was estimated
following Mouillot and Lepretre (2000). The RFD for each station was
separated into two portions: first the ranks with relative abundance
<0.5 % were discarded (rare taxa, between 0 % and 30 % of the
taxa according to all stations; by taking <1 % we would discard
between 18 % and 49 % of the taxa) and then the two parts were fitted with a
linear regressions. One part comprised the four highest ranks (see Mouillot and Lepretre, 2000
for the justification), and the remaining portion comprised the following ranks
(between 15 and 23 taxa, depending on the station). The slope for both the upper
and lower RFD portions was calculated (p1 and p2, respectively), and the
p1/p2 ratios were then estimated to quantify the differences between the RFDs of
all of the stations.
ResultsSpatial patterns of environmental variables
The principle component analysis (PCA) on environmental data explains 90.3 % of the total variance on the first two axes and delivers three clusters
of oceanographic areas plus two distinct stations (Fig. 3). The first axis
(62 % of the variance) is mostly influenced by temperature and dissolved
oxygen, as shown by their high correlations with the scores of the sampling
points on this axis (r=0.95 with p=0.000 and r=0.92 with p=0.000,
respectively), whereas the second axis (28.3 %) is mostly influenced by
MLD (r=-0.75, p=0.01), salinity (r=-0.75, p=0.001), and Chl-a
(r=-0.57, p=0.022; Table S1 in the Supplement).
Principal component analysis (PCA) ordination of five
environmental indicators: mixing layer depth (MLD), integrated values of the
Chl-a concentration, and mean values in the upper 0--300 m for temperature,
salinity, and dissolved oxygen. AB refers to the Algerian Basin, PB refers to the Provencal Basin, TB refers to the
Tyrrhenian Basin, and IB refers to the Ionian Basin.
The cluster of western stations in the Algerian Basin (AB) includes ST3,
ST4, ST9, and FAST, which are characterized by low temperature, salinity, and
MLD values. The cluster located in the Tyrrhenian Basin (TB) comprises
ST5, ST6, and TYR and is very close to the first group but with
lower Chl-a concentrations and higher temperature and
salinity values. Eastern stations (ST7, ST8, and ION) located in the
Ionian Basin (IB) are characterized by the highest temperature and salinity
values and the lowest dissolved oxygen concentrations found during the
survey. ST1 and ST2 in the Provencal Basin (PB) do not cluster with any
of the other stations due to the deeper MLD and higher Chl-a
concentrations.
Values of zooplankton abundance (a) and biomass (b) cumulated by
ESD size classes across different stations of the PEACETIME cruise.
The green line shows the integrated Chl-a concentrations, “*” denotes stations sampled during the
night, and “**” denotes that only the abundance and biomass values above 300 µm are presented for TYR.
Spatial patterns of zooplankton structure
Zooplankton abundance (Fig. 4a) during the PEACETIME cruise ranges between
265×103 and 583×103 individuals m-2, with an average of 372×103±84×103 individuals m-2, and zooplankton biomass (Fig. 4b) ranges from 1160 to
2170 mg DW m-2, with an average of 1707±333 mg DW m-2. The
highest abundances are found in PB and AB, and the highest biomass is found in AB.
The averaged total biomass in PB is lower than in AB due to the very low
contribution of the C1000-2000 and C>2000 size classes, but the size
classes from C<200 to C500-1000 present higher biomass values than
in AB. In TB, the total biomass values decrease between ST4 and ST6, with the latter
presenting the lowest biomass value of the whole survey. Note that the
biomass values at TYR are only obtained for the size classes above 500 µm
ESD, and the corresponding abundance value is comparable to those obtained at ST5
and ST6 for these larger size classes. In IB, total biomass and abundance
are lower than at AB and have low variability between stations. The detritus
estimated by FlowCAM and ZOOSCAN for all analyzed classes represents between
14.6 % and 39.1 % of the total biomass. The C200-300 ESD size class
has the highest average contribution (42.9 %) to the total zooplankton
abundance, followed by C300-500 (28.5 %), C<200 (17.8 %),
C500-1000 (8,9 %), C1000-2000 (1.7 %), and
C>2000 (0.22 %). In terms of biomass, C500-1000 has the
highest average contribution (25.3 %), followed by the C1000-2000 (23.8 %), C300-500 (21.3 %), C>2000 (15.5 %), C200-300
(11 %, 9 %), and C<200µm (2.1 %) fractions. There is
no correlation between the total zooplankton abundance or biomass and the integrated
Chl-a, but the C300-500 biomass is negatively correlated with Chl-a
(r=-0.52, p=0.044). The total abundance is negatively correlated with
temperature (r=-0.67, p=0.006; Table 3).
Table summarizing the Spearman's rank correlations. T∘ represents temperature, Sal represents salinity, Chl-a represents chlorophyll, MLD represents the mixing layer depth, and PP represents primary
production. Bold characters indicate a significant Rs value (p<0.05).
Copepods are the most abundant taxonomic group at all stations (Fig. 5),
representing 40 % to 79 % of the abundance and 32 % to 85 % of the total
biomass. The abundance of zooplankton smaller than 300 µm is dominated by
cyclopoid and calanoid copepodites. In N200, 51 taxonomic groups are
found, 34 of which are copepod genera. The adult stages of the copepod
community are dominated by the following genera: Para/Clausocalanus spp. (28.7 %), Oithona spp. (13.7 %),
Corycaeus spp. (6.2 %), Oncaea spp. (4.1 %), and undefined calanoid copepods (7.0 %). The most abundant non-copepod groups are appendicularians (5.1 %),
ostracods (4.8 %), and chaetognaths (3.6 %). The highest contributions
of copepods to abundance and biomass are found in PB; this
proportion then tends to decrease southwards as the abundance and biomass of
the other groups such as chaetognaths and gelatinous zooplankton increase.
The ratio between copepods with a length smaller than 1 mm and those larger than 1 mm
(Fig. 5) ranges from 2.8 to 8.3 (5.1 on average), with the maximum mean values
found in TB and the minimum values found in IB.
Spatial variation in taxonomic groups (bars) and the small
(length <1 mm)/large (length >1 mm) copepod ratio (dashed
line). “*” denotes stations sampled during the night.
The two-way ANOVA shows that the PB is characterized by a significantly
lower abundance and biomass in the upper size classes (1000–2000
and >2000µm) compared with the other areas (p<0.05). One-way ANOVA results show that both total zooplankton and
mesozooplankton present a significantly higher abundance in PB than in IB,
whereas their total biomass was not significantly different between the
areas (p>0.05). Significant differences in abundance and biomass
between areas were found in the C300-500, C1000-2000, and
C>2000 size classes and in the biomass for the C<200 class (P<0.05; Table 4 and Supplement Fig. S1).
Results of the one-way ANOVA tests performed to test differences
between areas (PB, AB, IB, and TB) with respect to the abundance and biomass data for the
different zooplankton size classes, for total zooplankton (all
size classes), and for mesozooplankton (ESD between 200 and 2000 µm)
between the areas. Significant differences (p value <0.05) are marked in bold.
“ns” denotes no significant difference. Italic F and p values mark where a Dunnett's test was used. In the post hoc
analysis, the homogeneous groups with the lowest and highest values are noted using
“a” and “b”, respectively. PB refers to Provencal Basin, AB refers to Algerian Basin, TB refers to Tyrrhenian Basin, and IB refers to Ionian
Basin.
Abundance Biomass Sheffé post hoc Sheffé post hoc Size classFpPBABTBIBFpPBABTBIBC2003.190.067nsnsnsns3.640.048nsnsnsnsC200-3003.460.055nsnsnsns2.550.109nsnsnsnsC300-5004.40.029bababa5.030.020baabaC500-10003.010.076nsnsnsns1.750.214nsnsnsnsC1000-200014.770.000ababb17.870.000ababbC>20009.250.002ababab11.630.001abaaTotal5.510.015bababa3.20.066nsnsnsnsTotal mesozooplankton (200–2000 µm)5.030.020bababa1.060.405nsnsnsns
The NBSS is calculated for each station, as shown in Fig. 6, using ION1 as an
example. During the PEACETIME survey, the NBSS slopes (Fig. 7) range
from -0.60 to 1.27, with an average value of -0.80. The most negative
slopes are found in PB, whereas the IB area has the fewest negative slopes.
At the long-duration stations FAST and ION, strong variations in slope
values appear depending on the sampling time, with the steeper slopes in the
samples collected during the daytime indicating the higher contributions of
small zooplankton compared with large zooplankton, which is potentially linked to the daily
migration of larger forms to depths below 300 m.
Normalized biomass size spectrum (NBSS) of mesozooplankton at
ION1. Black dots show the normalized biomass values in the successive size classes, and the straight line represents the linear regression, giving the slope value.
NBSS slope values of mesozooplankton obtained for all stations
during the PEACETIME survey. Black dots represent night samples, and gray dots represent day samples.
The NMDS analysis (Fig. 8) on the mesozooplanktonic taxa abundances based
on N200 delivers a distribution pattern for the stations that is rather
similar to that of the PCA on environmental variables. ST1 and ST2 in PB are
the most dissimilar stations due to the higher abundance of copepods –
especially the abundance of Para/Clausocalanus spp. at ST1, which is twice as high as at ST2 and between 5 and
13 times higher than the rest of the transect (Figs. 5, 8a). Similarly,
the Centropages spp. abundance is 10 times higher at ST1 and ST2 than at other stations in
the survey. In contrast, the abundances of Oithona spp. and Corycaeus spp. are 6 and
10 times lower at ST1 and ST2, respectively, than at other stations. The zooplankton
community in AB is slightly different from those in TB and IB due to
appendicularians and unidentified calanoid copepods being more abundant in
AB and due to Haloptilus spp. being more abundant in TB and IB. Within TB and IB, the
three sampling dates (ION1, ION2, and ION3) at ION form a unique
cluster, whereas ST7 and ST8 are grouped with station TB in another cluster.
This differentiation of ST7 and ST8 from the ION sampling dates in the NMDS
analysis is mainly due to differences in the relative abundance of Mesocalanus spp. (more
abundant), ostracods (less abundant), Clytemnestra spp. (absent in ION), and Pontellidae. (absent at ST7 and ST8).
NMDS analysis of the zooplankton taxa for all stations (a) excluding ST 1 and ST2. (b) A plot of the stations and the taxa correlated at
>0.65 with the axes. The color of the stations represents the areas
identified by the PCA in the environmental analysis (see Fig. 2). This
analysis was performed on the zooplankton collected with the data from
N200. PB refers to Provencal Basin, AB refers to Algerian Basin, TB refers to Tyrrhenian
Basin, and IB refers to Ionian Basin.
The SIMPER analysis shows that the lower average similarity between the
stations (64.79 %) is mainly due to Para/Clausocalanus spp. in PB. The rest of the basins
share a higher internal similarity: 78.43 %, 79.79 %, and 78.03 % for
AB, TB, and IB, respectively. Another interesting point highlighted in the
SIMPER analysis is the lower average dissimilarity between TB and stations ST7 and
ST8 (20.25 %). This dissimilarity increases when the comparison is
made between TB and the rest of the stations included in IB (29.04 %);
this finding is in agreement with the NMDS analysis (Fig. 8) that related ST7 and
ST8 to TB rather than to the stations in their basin.
Relationship between the environmental variables and the zooplankton community
Results of the PERMANOVA analysis on the environmental variables and on the
diversity of taxa are summarized in Table 5. Interestingly,
based on the zooplankton diversity of TB and IB, their difference is more
significant when ST7 and ST8 are removed from IB and placed in TB (based on
the NMDS cluster, Fig. 8), whereas this is not the case when considering
environmental variables (see Table 5). This suggests that the similarity
between ST7 and ST8 and the TB stations is not linked to the environmental
context.
PERMANOVA analysis on the environmental variables and on
zooplankton taxa abundances: pair-wise tests with unrestricted permutation
of raw data (number of permutations: 999) were used for the comparison between the
zones. Resemblance worksheets are based on Euclidean distance. Significant
p values (p<0.05) are marked in bold.
The BIOENV results show that salinity and chlorophyll were the environmental
variables that best explained the overall spatial distribution of the zooplankton
community (BIOENV; Rs=0.657).
The first factorial plane of the co-inertia analysis (Fig. 9) explained
96 % of the total variance, with 79 % due to the first axis. For both
spaces (“Environment” and “Zooplankton”), the IB stations on the first axis are associated with high temperature and salinity values and several zooplankton taxa (namely echinoderm larvae and some copepod taxa, e.g., Pontellidae, Rhincalanus spp., Haloptilus spp., and Phaena spp.) and are separated from the PB and AB stations correlated with higher chlorophyll concentrations and with some copepod taxa (mainly Pseudodiaptomus spp., Tortanus spp., and Pleuromamma spp.). On this axis, TB stations have an intermediate
position, close to the coordinate zero. The second axis opposes northern
(ST1 and ST2 in PB) and southern (AB) stations sampled in the western
Mediterranean Basin. On this axis, PB stations are characterized by higher
chlorophyll and salinity values and a deeper MLD compared with AB and by the
association with Pseudodiaptomus spp., whereas southern AB stations are associated with the
copepods Heterorhabdus spp., Labidocera spp., and Euterpina spp. As in the preceding multivariate analyses,
we note that ST8 and ST9 from the IB tend to be closer to the TB stations
than to the ION station on the first factorial plane, particularly in the
“Zooplankton system”. The association between the environmental context and
the zooplankton community is high with good correlation between the
normalized scores of the stations (R2=0.844 and R2=0.820 for the x1 and x2
axes, respectively) and with the positions of the plots of these stations
close to the equality lines (i.e., x1 Zooplankton =x1 Environment or x2
Zooplankton =x2 Environment).
Co-inertia analysis. Ordination on the plans (1, 2) of the
environmental variables (a) and the abundance of the zooplankton taxa (b). Ordination on the plans (1, 2) of the stations in the “Environment system” (c) and in the “Zooplankton
system” as well as plots of the stations on the first (c) and second (d) axes of
the two systems. Lines represent the equality between the coordinates on
the two systems. Colored squares identify the different regions: green denotes PB, black denotes AB, yellow denotes TB, and red denotes IB.
Rank frequency diagram at TYR (a), ST5 and ST6 (b), ION (c), FAST (d), and the Log-standardized frequency for all stations (e) and stations
influenced by dust deposition (f). Ac denotes Acartia spp., Cal denotes calanoid copepods, Cala denotes
Calanus spp., Cent denotes Centropages spp., Cor denotes Corycaeus spp., Euc denotes Eucalanus spp., Halop denotes Haloptilus spp., Luci denotes Lucicutia spp., Mecy denotes
Mecynocera spp., On denotes Oncaea spp., Ot denotes Oithona spp., P/Cla denotes Para/Clausocalanus spp., Pleu denotes Pleuromamma spp., Pont denotes Pontellidae,
Tem denotes Temora spp., App denotes Appendicularia, Cha denotes Chaetognatha, Dec denotes decapods, Hydro denotes
hydrozoans, Ich denotes ichtyoplankton, Ost denotes ostracods, Poly denotes Polychaeta, Pte denotes
pteropods, Siph denotes siphonophores, and Thal denotes thaliaceans.
Zooplankton community changes linked to dust deposition events during
the PEACETIME survey
The zooplankton community changes were analyzed using the variations in the RFDs
between samplings. The RFDs for TYR, ST5, ST6, ION, and FAST are
presented separately in Fig. 10a–d and are grouped in Fig. 10e and
f. As only one sample was carried out at TYR, 9 d after a large
dust deposition event in the southern Tyrrhenian Basin, the RFDs of ST5 and ST6
also sampled in TB (6 and 12 d after the dust event, respectively)
are added for comparison (Fig. 10a, b). At all three TB stations,
the RFDs are characterized by the high dominance of the filter-feeding zooplankton
Para/Clausocalanus spp. and Oithona spp. in the first and second positions and a strong drop in abundance for
the following ranked taxa (undefined calanoid copepods or Corycaeus spp.).
Appendicularians drop from the fourth position at ST5 and TYR to the
tenth position at ST6. The shapes of the RFDs change more between ST5 and
TYR than between TYR and ST6. At ION, which was not impacted by dust
deposition, the RFD shapes are similar for both sampling dates (ION1 and ION3),
and the community is dominated by Para/Clausocalanus spp. (Fig. 10c). Corycaeus spp. changes from the
second position to the fourth, calanoid copepods change from the third position to the
sixth, and Oithona spp. changes from the fourth position to the second. Appendicularians occupy a very
similar position in both RFDs (sixth and seventh rank at ION1 and ION3,
respectively). At FAST, the taxonomic composition is dominated by
copepods (Fig. 10d), but the rank order of the most dominant species
changes between the two sampling dates (FAST1 and FAST3). Oithona spp. and
Para/Clausocalanus spp. have the first and second ranks during FAST1, but this order is
reversed during FAST 3. The third place on both days is occupied by calanoid
copepods. Appendicularians present one of the most significant changes, with
their rank dropping from fourth to fourteenth between the two dates. It is
remarkable that the RFDs change from a convex shape at FAST1 to a more
concave one at FAST2, which is influenced by the high dominance of
Para/Clausocalanus spp. at the first rank (Fig. 10d). The comparison of the standardized
RFDs for all of the stations (Fig. 10e) highlights that the greatest change
in shape is visible at FAST, whereas it remains moderate at ION and negligible
at TB. Figure 10f is similar to Fig. 10e, except without ION, in order to visualize
changes in the zooplankton community composition at different time lags after a
dust event. This will be commented on in more detail in Sect. 4. The RFDs for all stations are shown in Fig. S2 in the Supplement.
Estimated grazing, respiration, and excretion rates of zooplankton
based on allometric models (see Sect. 2) and their impact on the
phytoplankton stock and production along the PEACETIME survey transect.
Provencal Basin Algerian Basin Tyrrhenian Basin Ionian Basin ST1ST2FAST1FAST2FAST3ST9ST3ST4ST5ST6ST8ST7ION1ION2ION3Grazing impact Phytoplankton stock (mg C m-2)17491632155416911412180511611458152693315821212137615871587Primary production (mg C m-2d-1)295155229184297303165225197190289187266279304ZCD (mg C m-2 d-1)280155274263249228224278202145195205204244177Grazing impact on phytoplankton stock (%)16.09.517.715.617.712.719.319.113.315.612.417.014.815.411.2Grazing impact on primary production (%)94.899.9119.7143.383.975.4135.6123.7102.576.767.6109.776.587.658.3Respiration Respiration (mg C m-2 d-1)112.264.395.390.186.281.383.8100.278.762.975.677.072.494.771.6Percentage of primary productionrespired by zooplankton38.041.441.549.029.026.850.644.539.833.126.141.027.133.923.5NH4 zooplankton contribution Excretion (mg N–NH4 m-2 d-1)17.79.213.612.912.316.212.014.311.39.110.911.010.413.610.3Phytoplankton needs (mg N m-2 d-1)50.226.439.031.350.651.628.238.333.632.449.231.945.347.451.8N demand (%)35.234.934.941.124.331.542.637.433.628.022.134.622.928.819.9PO4 zooplankton contribution Excretion (mg P–PO4 m-2 d-1)2.31.32.01.91.81.71.82.11.61.31.61.61.52.01.5Phytoplankton needs (mg P m-2d-1)8.64.56.75.38.68.84.86.55.75.58.45.47.78.18.8P demand (%)27.329.730.435.921.319.536.832.528.623.518.629.619.724.116.6Estimated zooplankton carbon demand, grazing pressure, respiration, and
excretion rates
Zooplankton carbon demand ZCD (Table 6) varies between 145.9 and 280.1 mg C m-2 d-1 at ST6 and FAST1, respectively. Assuming that phytoplankton is
the major food source, zooplankton consumption potentially represents 15 % of the phytoplankton stock on average per day and 97 % of the
primary production (see Table 6). ZCD follows the zooplankton biomass
pattern with higher values in AB and lower values in TB, and it does not
increase with primary production (r=-0.18, p>0.05). The
average respiration (mean of 83.1 mg C m-2 d-1 and range between 62.9
and 112.2 mg C m-2 d-1) corresponds to 36.4 % of the integrated
primary production. Almost half of this zooplankton respiration is due to
organisms smaller than 500 µm ESD. Mean ammonium excretion is 12.3 mg NH4 m-2 d-1 (range between 9.1 and 17.7 mg NH4 m-2 d-1), and mean phosphate excretion is 1.7 mg PO4 m-2 d-1 (range
between 1.3 and 2.3 PO4 m-2 d-1). The potential contributions of
excreted nitrogen and phosphorus to primary production are 31.5 % (range between 19.9 % and 42.6 %) and 26.3 % (range between 19.9 % and
42.6 %), respectively. Zooplankton size classes smaller than 500 µm ESD
contribute 45 % and 47 % of the total ammonium and phosphate excretion,
respectively. Estimated values for all zooplankton size classes for grazing,
respiration, and excretion rates and for their impact on the phytoplankton
stock and production along the PEACETIME survey transect are presented in
Table S2 in the Supplement.
DiscussionMethodological concerns and the importance of the small zooplankton
fraction
The methodology used in this study – combining two nets (N100 and N200) and two sample
treatments (FlowCAM and ZOOSCAN) – enables us to deliver a more accurate
mesozooplankton community size spectrum (200–2000 µm), although the C<200 and C>2000 size
classes at the edges of the spectrum range remain
undersampled and require different equipment for proper sampling (bottles and a larger mesh size net, respectively). The length / width ratio of mesozooplankton
organisms is quite variable and ranges from 1 for nearly spherical organisms,
such as nauplii or cladoceran, to more than 10 for long organisms, such as
chaetognaths (Pearre, 1982) or some copepods like Macrosetella gracilis (Böttger-Schnack,
1989), with an average value of between 3 and 4 for copepods (Mauchline, 1998).
If we consider that organisms with a length / width ratio of 6 caught by the
200 µm mesh size will present an ESD of at least 490 µm, it is
consistent that this net quite correctly samples organisms with an ESD
above 500 µm. For these organisms (>500µm
ESD), ZOOSCAN is the most appropriate tool to deliver the size spectrum.
Similarly, the 100 µm mesh size net allows small organisms with a width
just below 100 µm to pass through; however, most of them might have an ESD of
up to 200 µm because the length / width ratio
is mostly below 4 for these smaller sizes (Mauchline, 1998). Due to the threshold of ZOOSCAN at 300 µm ESD, FlowCAM is the best tool to process organisms in the fraction
below 500 µm.
Several authors have already highlighted the limitation of the 200 µm
mesh size with respect to catching small zooplankton individuals. Comparisons of different
zooplankton nets' mesh sizes between 60 and 330 µm have
systematically shown a decrease in plankton abundance with an increasing mesh size
(Turner, 2004; Pasternak et al., 2008; Riccardi, 2010; Makabe et al., 2012;
Altukhov et al., 2015). When the goal of the study is to achieve a full
understanding of the complete mesozooplankton community structure and
function, the size selectivity of the sampling nets is an important
issue: clearly, a large fraction of organisms between 200 and 500 µm ESD is undersampled using a single 200 µm mesh size.
Pasternak et al. (2008) reported that a 220 µm mesh can lose up to
98 % of the abundance of Oithona spp. and 80 % of copepodite stages of Calanus spp.
Riccardi (2010) found that a classical 200 µm net catches only 11 %
of the abundance and 54 % of the biomass compared with a 80 µm mesh
size, which, in that study, led to differences in the observed species composition in the
Venice Lagoon. During the PEACETIME survey, the small size classes
(C200-300 and C300-500) of mesozooplankton have been optimally
sampled using a 100 µm mesh size (N100). Consequently, these
size classes represent very large percentages of the total abundance
(52.3 % and 34.8 %, respectively) and a significant contribution to the
total biomass (14.5 % and 25.9 %, respectively). These reliable estimations
have direct consequences for the estimated fluxes (see below).
Comparison of zooplankton biomass and abundance in different areas
of the Mediterranean Sea. “*” denotes the wet weight. NWMS refers to the northwestern Mediterranean Sea, and SWMS refers to the southwestern Mediterranean Sea.
AreaSampling periodNet mesh size (µm)Layer (m)Biomass (mg m-3)Abundance (individuals m-3)ReferenceNWMS – Provencal and Ligurian basinsFeb 20131200–25012.3 (1.9–42.3)608 (21–2548)Donoso et al. (2017)NWMS – Provencal and Ligurian basinsApr 20131200–25064.5 (13.9–197.8)3668 (850–7205)Donoso et al. (2017)NWMS – Gulf of Lion shelfMar–Apr 199880–2000–2009.56±4.73Gaudy et al. (2003)NWMS – Gulf of Lion shelfJan 199980–2000–2004.73±2.53Gaudy et al. (2003)NWMS – Provencal BasinMar 19692000–2000.4–53Nival et al. (1975)NWMS – Provencal BasinApr 19692000–20010–210Nival et al. (1975)NWMS – Provencal BasinSpring 20082000–20013.15±2.51731Mazzocchi et al. (2014)NWMS – Provencal BasinJul 19992000–300383Siokou et al. (2019)NWMS – Provencal BasinMay–Jun 2017100–2000–3005.5±2.11638±433this studySWMS – Algerian BasinJul–Aug 19972000–2008.2 (2.1–34.5)370 (36–844)Riandey et al. (2005)SWMS – Algerian BasinJul 19992000–300197Siokou et al. (2019)SWMS – Algero–Provencal BasinJun–Jul 20082000–2005.41561±205Nowaczyk et al. (2011)SWMS – Algerian BasinMay–Jun 2017100–2000–3006.6±0.61254±191This studyTyrrhenian BasinAutumn 19862000–503.6–32Fonda Umani and de Olazábal (1988)Coastal Tyrrhenian Basin1984–20062000–501708Mazzocchi et al. (2011)Tyrrhenian BasinSep–Oct 196360–3000–7000.15–0.3Cited from Champalbert (1996)Tyrrhenian BasinJun–Jul 20082000–2003.21250Nowaczyk et al. (2011)Tyrrhenian BasinJun 1968Not specified0–2005.8*Cited from Kovalev et al. (2003)Tyrrhenian BasinMay–Jun 2017100–2000–3004.8±1.11398±108This studyIonian BasinApr–May 19992000–1006.0±0.8 (eastern)Mazzochi et al. (2003)Ionian BasinApr–May 19992000–1008.2 to 13.4 (western)Mazzochi et al. (2003)Ionian BasinSpring 19922000–300219Mazzochi et al. (2003)Ionian BasinSpring 19992000–300193Mazzochi et al. (2003)Ionian BasinSpring 20082000–2002.73213Mazzocchi et al. (2014)Ionian BasinAutumn 20082000–2003.25338Mazzocchi et al. (2014)Ionian BasinJun–Jul 20082000–20081181±630Nowaczyk et al. (2011)Ionian BasinJul 19992000–300146Siokou et al. (2019)Ionian BasinMay–Jun 2017100–2000–3005.1±0.51003±76This studyDifferences in abundance, biomass, and zooplankton community structure in
relation to regional environmental characteristics
A review of the most relevant information available on zooplankton biomass
and abundance in different regions of the central and western Mediterranean
Sea (Table 7) shows a wide range of variation that can be attributed to
location, sampling seasons, and/or sampling methods (e.g., net mesh size and depth of
tow). In general, the values from the PEACETIME survey are of the
same order of magnitude as values from previous studies, although most of the other studies were performed with
a 200 µm mesh size net and often over a shallower surface layer.
However, during this post-bloom period, no clear regional patterns in
abundance and biomass were found, unlike other descriptions showing a
north–south and west–east decrease in zooplankton stocks (Dolan et al.,
2002; Siokou-Frangou, 2004). In PB, Donoso et al. (2017) and Nival et al. (1975) highlighted a strong variability that is consistent with the strong
gradient found between ST1 and ST2 during PEACETIME (see Fig. 4). In AB,
abundance and biomass values obtained during the survey are similar to those
recorded in late spring by Nowaczyk et al. (2011), whereas Riandey et al. (2005) found lower abundance and higher biomass values. However, the latter
study focused on the high resolution of a mesoscale eddy, highlighting an
important fine-scale variability in abundance and biomass values. For TB,
the data are difficult to compare due to the different sampling conditions (net
mesh size, depth of tow, and sampling season). In IB, all biomass values
presented in Table 7 are of the same order of magnitude, although abundances found by
Mazzocchi et al. (2003, 2014) are 3 times lower than those observed
during PEACETIME, which is probably due to the high contribution of C<200 and
C200-300 obtained with N100 (see Fig. 4). In general, the
better sampling of small size classes with N100 should lead to higher
abundance values. However, the comparison of data in Table 7 shows that the
regional and temporal variability of these values partially masks this
benefit.
In PEACETIME, clear regional differences are found both in terms of
environmental variables and zooplankton taxonomic composition. ST1 and ST2
are clearly differentiated from all of the other stations by a deeper MLD, higher
Chl-a concentrations, and a zooplankton community dominated by
herbivorous copepods typical of the PB (e.g., Centropages, Para/Clausocalanus, and Acartia,), as mentioned by Gaudy et al. (2003)
and Donoso et al. (2017), and characterized by a scarcity of thaliaceans
which normally occur in ephemeral and aperiodic patches (Deibel and
Paffenhöfer, 2009). AB and TB are very closely related to each other in
terms of hydrological features and Chl-a, but they are slightly differentiated
with respect to salinity and zooplankton taxonomy, probably because they are both
strongly influenced by the Modified Atlantic Water (MAW) and its associated
mesoscale features (Millot and Taupier-Letage, 2005). In AB, 17 d
separated the sampling of ST3 and ST4 from that of ST9 and FAST; however, despite
this time gap, they are very close in terms of hydrological features,
Chl-a level, and zooplankton community structure. IB is clearly
differentiated from these groups in terms of environmental parameters (see
Fig. 3) due to a higher salinity and a lower Chl-a concentration; however, in terms of
zooplankton community, the western Ionian stations (ST7 and ST8) present more
analogy with TB than with the ION (see Fig. 8). During PEACETIME,
ION appears to be clearly separated from ST7 and ST8 that are located further
westwards by a north–south jet (ADCP and MVP observations, Leo Berline, personal communication, 2020), which might correspond to the “Mid-Mediterranean Jet”
(Malanotte-Rizzoli et al., 2014, their Fig. 5). The location of ST7 and
ST8 within the anticyclonic structures of the portion of the Modified Atlantic
Water (MAW) flowing through the Strait of Sicily could explain their
similarity to the TB stations in terms of zooplankton assemblages – as TB is
directly influenced by the main part of the MAW flowing through the Sardinian
Channel. Ayata et al. (2018) also classified the Tyrrhenian Basin as
heterogeneous due to complex circulation patterns including transient
hydrodynamic structures in the south, which could also explain the
similarity of ST7 and ST8 to the TB stations in terms of zooplankton assemblages
during PEACETIME. This area of the IB visited during PEACETIME certainly
represents a transition area between the eastern and western Mediterranean
basins (Siokou-Frangou et al., 2010; Mazzocchi et al., 2003).
These regional differences, highlighted both in terms of environmental
characteristics and zooplankton taxa assemblages, are in agreement with the
regionalization of the Mediterranean Basin by Ayata et al. (2018) based on
historical biogeochemical, biological, and physical data of the epipelagic
zone. For example, ST1 of PEACETIME, which is characterized by high Chl-a, high
zooplankton abundance, and the dominance of small copepods, is clearly located in
the “consensual Ligurian Sea Region” sensu Ayata et al. (2018), which has been identified as the
most productive region of the Mediterranean due to intense deep convection events.
Among the AB stations, ST3, ST4, and ST9 are clearly within the “consensual
Algerian region” (Ayata et al., 2018), whereas FAST corresponds to
the “western Algerian heterogeneous region”. Among the IB stations,
ST7 and ST8 from the ION stations in terms of zooplankton
communities and, to a lesser extent, environmental variables, also
correspond to the distinction between the “consensual North Ionian” region
and the western part of the “Ionian Sea region”, which is considered to be a
heterogeneous region (Ayata et al., 2018).
Estimated zooplankton-mediated fluxes during the PEACETIME survey
By using allometric relationships relating zooplankton grazing and metabolic
rates to size structure, zooplankton impacts (top-down vs. bottom-up impacts) on
primary production have been investigated. We are aware that using constant
conversion factors may limit the analysis of the spatial variation, as
these factors may display temporal and geographical variations (Minutoli and
Guglielmo, 2009). However, our sampling strategy based on a limited number
of stations sampled did not enable us to consider temporal and spatial
variations accurately, and our main goal was to roughly estimate
the epipelagic zooplankton-mediated fluxes at the scale of the PEACETIME
cruise.
Cluster analysis on rank frequency diagrams (a) and
changing trends in the p1/p2 ratio (b) on the stations impacted by wet dust
deposition.
ZCD estimations show that zooplankton required 15 % of the daily
phytoplankton stock, with narrow variations over the whole area (between 9.5
and 19.3), which is twice as low as the values estimated by Donoso et al. (2017) during the spring bloom in the northwestern Mediterranean Sea.
However, estimated grazing rates are of the same order as the estimated primary
production, which corresponds to the highest range of the values summarized
by Siokou-Frangou et al. (2010) for the whole Mediterranean Sea (from 14 % to
100 %). Estimating ZCD on the basis of mesozooplankton alone
certainly leads to an overestimation of its top-down impact on phytoplankton.
In the Mediterranean Sea, primary production is consumed by a
“multivorous web” including microbial and zooplankton components
(Siokou-Frangou et al., 2010). Mesozooplankton simultaneously grazes on
phytoplankton and heterotrophic prey, such as heterotrophic dinoflagellates
(Sherr and Sherr, 2007) or ciliates (Dolan et al., 2002), and might be quite
flexible in its feeding strategy depending on the composition and size of
prey as well as the environmental variables such as turbulence (Kleppel,
1993; Yang en al., 2010). On the one hand, a large part of the primary
production can be consumed by ciliates (Dolan and Marrasé et al., 1995),
but, on the other hand, mesozooplankton can consume almost the entire ciliate
production (Pitta et al., 2001; Pérez et al., 1997; Zervoudaki et al.,
2007), which potentially explains the wide variations in the ciliate standing stock over the Mediterranean Sea (Dolan et al., 1999; Pitta et al., 2001;
Dolan et al., 2002). The extensively described east–west pattern of
decreasing grazing impact (Siokou-Frangou et al., 2010) could not be
observed during this study as only one station (ION) was typical of
the eastern Mediterranean Sea.
Estimated NH3 and PO4 excretion rates by mesozooplankton during
PEACETIME are consistent with the few observations collected in the
Mediterranean Sea (Alcaraz, 1988; Alcaraz et al., 1994; Gaudy et al., 2003)
and with those obtained at similar latitudes (see review in
Hernández-León at al., 2008). From our estimation, zooplankton
excretion would contribute to 21 %–44 % and 17 %–38 % of
the N and P requirements for phytoplankton production, respectively. In the NWMS, Alcaraz
et al. (1994) estimated a zooplankton nitrogen excretion contribution to
primary production of >40 %, whereas Gaudy et al. (2003) reported
31 %–32 % and 10 %–100 % N and P contributions, respectively. This impact on
phytoplankton production can be even greater in proximity to the DCM where
zooplankton tend to aggregate, fueling regenerated production (Saiz and
Alcaraz, 1990) and enhancing bacterial production (Christaki et al., 1998).
Zooplankton grazing impact and nutrient contribution to primary production
are higher in the western basin than in the Ionian Basing, which is mainly linked to
variations in zooplankton biomass.
Mean carbon released through zooplankton respiration represents 36 % of
the primary production during PEACETIME, which is higher than previous
measurements in the NWMS (by Alcaraz, 1988; Gaudy et al., 2003) from onboard
incubation experiments on zooplankton collected with a 200 µm mesh
size net.
Metabolic estimations clearly show that the size fractions <500µm (optimally captured with the 100 µm mesh size) make a
significant contribution to the whole mesozooplankton estimated fluxes: 14.9 % of the ZCD is due to organisms <300 µm, and this size
class contributes 21 % and 20 % of the total ammonium and phosphate
excretion, respectively.
Impact of dust deposition on the zooplankton community
In the past, responses to Saharan dust inputs in marine systems have
mostly been studied in microcosm and mesocosm experiments or, more rarely,
observed in situ. Most studied responses to dust are focused on the microbial biota
and are generally marked by an increase in metabolic rates rather than by
standing stock changes, which is probably due to trophic transfer along the food web (Ternon et al., 2011; Guieu et al., 2014; Ridame et al., 2014; Herut et al.,
2016). In mesocosms, changes in zooplankton stocks are strongly dependent on
the initial conditions, and cannot really reflect what could occur in
natural waters within the Mediterranean “multivorous planktonic food web”
(Siokou-Frangou et al., 2010). Pitta et al. (2017) found an increase in
mesozooplankton biomass 9 d after the beginning of a mesocosm experiment,
probably as a result of an earlier increase in prey (flagellates, ciliates,
and dinoflagellates). Tsagaraki et al. (2017) described an increase in
productivity after an artificial dust deposition event that was transferred to
higher trophic levels by the classical food web, resulting in an increase in
copepod egg production 5 d after the beginning of the experiment. Very
few in situ studies have documented mesozooplankton responses to Saharan dust.
An increase in plankton abundance was observed by Thingstad et al. (2005) in the eastern
Mediterranean Sea, and Hernández-León et al. (2004) also noted an increase in abundance in Atlantic
waters close to the Canary Islands 1 week after dust deposition. In the
latter area, Franchy et al. (2013) detected increases in zooplankton grazing
and zooplankton biomass after another event. Thus, the PEACETIME survey, which was
dedicated to the tracking of such events, was an opportunity to observe real
in situ zooplankton responses in the epipelagic layer (0–300 m).
At FAST (an opportunistic station after a Saharan dust deposition
event), an increase in nitrate (from 50 to 120 nM) and phosphate (from 8 to 16 nM) concentrations occurred in the mixed layer (Cécile Guieu, personal communication, 2020), which led to an increase in primary production from FAST1 to
FAST3, although with no visible changes in phytoplankton biomass (see Table 2).
For zooplankton, the total abundance slightly decreases, but the community
composition presents obvious changes, mainly a decrease in appendicularians
and an increase in Para/Clausocalanus spp. and carnivorous taxa (e.g., Candacia spp., chaetognaths, and
siphonophores; see Fig. 10d). The sharp decrease in appendicularian
abundance (4-fold decrease) and rank position (see Fig. 10d) could
potentially be linked to either food limitation or predation. The size and
species composition of the phytoplankton community in FAST suggest a change
toward larger cells (Table 2) that are poorly ingested by appendicularians and induce filter clogging. There were also potential increases in food competition
with Para/Clausocalanus spp. (Lombard et al., 2010) and/or in predation from chaetognaths and
siphonophores (Purcell et al., 2005). Although the total zooplankton biomass
remains relatively stable at FAST, the contribution of the
C500-1000 and C1000-2000 size classes increase relative to the smaller size
classes (see Fig. 4b), inducing variations on the NBSS slope from -0.76 to
-0.63 (see Fig. 6). This 15 % increase in biomass is mainly due to large
migrating taxa such as copepods (Eucalanus spp., Rhincalanus spp., and Candacia spp.), chaetognaths, and
siphonophores. The daily observation of sediment traps at 200 and 500 m
over 5 d between FAST1 and FAST3 (Cécile Guieu, personal communication, 2020) shows a
relative increase in swimmers collected at 500 m vs. those collected at
200 m, which also suggests an increasing number of migrants. An obvious planktonic
transition occurred during this period, but it is difficult to conclude which
of the bottom-up (changes in primary producers) or top-down (increase in
carnivorous migrants) effects was dominant. The change in the RFDs (Fig. 10d) from a convex shape at FAST1, indicating a more stable system with no
dominance from the first taxonomic groups, to a more concave shape at FAST3,
influenced by the high dominance of Para/Clausocalanus at the first rank, could reflect a
disturbance effect (sensu Pinca and Dallot, 1997) on the
zooplankton community due to dust deposition.
A synoptic analysis of the RFDs linked to the dust events observed in the
Tyrrhenian Basin and at FAST offers a basis for proposing a
conceptual model of a virtual time series of zooplankton community responses
after a dust deposition event (Fig. 10f): the first sampling is carried
out before the event (FAST1) and several other samplings are undertaken with a
time lag of 5 d (FAST3), 6 d (ST5), 9 d (TYR), and 12 d (ST6) after the event. FAST1 represents an initial steady state (state 0) with no dominance in the first taxa ranks, whereas FAST3 and ST5 represent
a disturbed state of the community (state 1) with strong dominance from the
first taxa and the collapse of the following taxa. TYR and ST6 represent the
beginning of recovery towards a stable system (state 2) as the
second rank moves up. State 0, before the dust event, is characterized by
oligotrophic conditions with low nutrients, a low phytoplankton concentration
dominated by small-size cells, and their typical zooplankton grazers (e.g.,
appendicularians and thaliaceans), leading to a convex RFD shape (like
FAST1; Fig. 10f) and reflecting a mature community (sensu Frontier, 1976). State 1
is characterized by a nutrient input linked to the dust event that stimulates
larger phytoplankton cells and their herbivorous grazers (copepods) and
attracts carnivorous migrants, leading to a more concave RFD shape (like
FAST3, ST5, and TYR; Fig. 10f) that is typical of a disturbed community (sensu Frontier,
1976). State 2 is characterized by the diversification of herbivorous taxa,
leading to changes in the RFD towards a convex shape (like ST6; Fig. 10f).
The cluster analysis on the RFDs (Fig. 11a) is in agreement with this
succession of the time series (Fig. 10f) by grouping the stations
according to the impact level of the wet dust deposition. It separates the
initial condition (FAST1) from the most disturbed state (FAST3 and
ST6) and identifies a transition phase before (FAST2) and after (TYR and
ST6) the peak disturbance. The changing trends in the p1/p2 ratios (Fig. 11b)
show an interesting development, with a sharp increase until day 5 after the
dust deposition and a progressive decrease towards the end of the virtual
time series. The linear regression suggests that the community structure
will deliver a p1/p2 ratio value similar to the initial value of the time
series after 22 d. Is interesting to note that this delay corresponds to
an average generation time of zooplankton organisms for this region. Cluster
analysis on the RFDs and the p1/p2 ratio for all stations are shown in
Figs. S3 and S4, respectively. Interestingly, in the co-inertia
analysis (see Fig. 9), the stations impacted by dust (FAST and TB) are grouped on the left side of the relationship between the x2 axis
of environment and zooplankton. In addition, their succession in this graph
is consistent with the sequence observed in the virtual time series of the RFD
(with FAST1 as the initial station before dust deposition and TYR and
ST6 corresponding to 9 and 12 d after the dust event, respectively) and shows the coupled
impact of dust on both environment and zooplankton.
Conclusions
To our knowledge, PEACETIME was the first study in the Mediterranean Sea
that managed to collect zooplankton samples before and soon after natural
Saharan dust deposition events and to highlight in situ zooplankton responses in
terms of community composition and size structure. Our study suggests that a
complete understanding of the mesozooplankton community response to a single
massive dust event would require continuous observation over 2 to 3
weeks – from an initial state just before the event to a complete process of
zooplankton community succession after the event. To identify such a
succession, the RFDs of the zooplankton taxonomic
structure appear to be a more practical and sensitive index than observable
changes in stock (abundance and biomass) or in metabolic rates, and they should
be tested further. In particular, the changes in the p1/p2 ratio might
characterize the response of the zooplankton community to a pulse of dust
(or any massive disturbance) and its resilience capacity after the forcing
event.
This approach requires a complete overview of the mesozooplankton size spectrum
and community composition which was achieved in our study by combining data
from two net mesh sizes (100 and 200 µm) and two analytical
techniques (FlowCAM and ZOOSCAN). In our study, this strategy also enabled
us to show the importance of small forms (<500µm ESD),
both in terms of stocks and fluxes.
Data availability
All data from the PEACETIME cruise (10.17600/17000300; Guieu and Desboeufs, 2017) are stored at the LEFE CYBER database (http://www.obs-vlfr.fr/proof/php/PEACETIME/peacetime.php, last access: 27 October 2020) and will be made freely available once all of the papers have been submitted to the PEACETIME special issue. In the meantime, data can be obtained upon request from François Carlotti.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-5417-2020-supplement.
Author contributions
GF, MP, and FC wrote the paper with contributions from PH. GF was responsible for
the sample treatment. GF, FC, MP, and PH carried out the data analysis.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Atmospheric deposition in the low-nutrient–low-chlorophyll (LNLC) ocean: effects on marine life today and in the future (ACP/BG inter-journal SI)”. It is not associated with a conference.
Acknowledgements
This study is a contribution to the PEACETIME project
(http://peacetime-project.org, last access: 27 October 2020), which is a joint initiative of the MERMEX and ChArMEx
components supported by CNRS-INSU, IFREMER, CEA, and Météo-France as
part of the MISTRALS program coordinated by INSU. The PEACETIME cruise
(10.17600/17000300) was managed by Cécile Guieu (LOV) and Karine Desboeufs (LISA). We thank the PEACETIME project coordinators and scientists, especially Nagib Bhairy, who carried out the zooplankton sampling.
Zooplankton analyses were realized on the Microscopy and Imaging platform of
MIO, which is partly funded by the European FEDER Fund (project no. 1166-39417). We thank Loïc Guilloux and Lucas Lhomond for initiation sessions to ZOOSCAN and FlowCAM, respectively.
The authors are grateful to Emilio Maranon and María Perez-Lorenzo for the primary
productivity data and to Julia Uitz,
Céline Dimier and the SAPIGH analytical service at the Institut de la
Mer de Villefranche (IMEV) for onboard sampling and HPLC analysis. The authors also wish to thank
Cécile Guieu, Elvira Pullido, France Van Wambeke, and Julia Uitz for
critical reading and advice on the paper and Michael Paul for correcting
the English. We would also like to acknowledge the two reviewers for their constructive
comments and suggestions that stimulated a substantial revision.
Guillermo Feliú was supported by a Becas-Chile PhD scholarship by the National
Agency for Research and Development (ANID), Government of Chile.
Financial support
This research has been supported by the INSU–CNRS (France; France MISTRALS program) and the ANID (Chile; Becas-Chile PhD scholarship).
Review statement
This paper was edited by Cecile Guieu and reviewed by Tamar Guy-Haim and one anonymous referee.
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