Discussion
The data presented here supports the presence of a significant
seasonality in the photic community in the Subtropical North Atlantic,
whose signal and strength gradually decreases with depth, becoming
undetectable in the mesopelagic. The structure of epipelagic and
mesopelagic protist communities in the Sargasso Sea is strongly shaped
by local ocean dynamics (e.g., mixing, stratification, nutrient
distributions and light penetration) and expressed in the vertical and
seasonal partitioning of community composition, diversity, and function.
Mixotrophic groups dominate the sunlit epipelagic layers, with
autotrophic taxa gaining relevance where the base of the photic layer
meets the top of the nutricline (Arenovski et al. 1995). The
mesopelagic layers below are inhabited by heterotrophic communities
where Rhizaria becomes the most abundant group, outside of parasitic
lineages (i.e. Syndiniales). The deep communities are likely fueled by
complex food webs, feeding on prokaryotes, particles and each other
(Byung Cheol et al. 2000; Rocke et al. 2015).
Nutrient limitation in the epipelagic zone favors mixo- and
heterotrophic protists closer to the surface, with autotrophs balancing
nutrients and light at its base. This is largely a consequence of ocean
stratification, which enhances or inhibits vertical exchanges. The
degree of stratification at the base of the photic zone affects the rate
of nutrient supply fueling the autotrophic lineages at the local DCM.
Over the annual cycle, hydrographic Layers 0, 1 and 2 exhibit dramatic
changes in stratification driven by local air-sea fluxes, with Layers 1
and 2 being subsumed into Layer 0 beginning in the Fall and ending at
the Spring transition. This process creates the vertical partitioning of
clusters 1, 2 and 3 and their seasonality observed in K=9: i.e. clusters
1 and 2 dominate Layers 0 and 1 above the DCM from Spring through the
Stratified season, with cluster 1 disappearing in the Fall and Mixed
seasons, while cluster 3 in Layer 2 waxes in Spring then gradually wanes
through the Stratified and Fall seasons. Such seasonality is absent at
ALOHA in the North Pacific where winter mixing is shallower, the base of
the photic layer remains stratified throughout the year, and protist
communities align with depth (Ollison et al. 2021). This
difference is not unexpected, due to the more constant hydrographic
conditions, primary production and particle flux measured throughout the
year by the Hawaii Ocean Time-series compared to those at BATS (Churchet al. 2013). The more homogeneous conditions would then favor
the presence of a dominant community all year long at ALOHA, showing
only depth-controlled layering.
The pronounced seasonality observed in the Sargasso Sea implies shifts
in trophic ecology and energy fluxes which likely influence
biogeochemical cycles over the annual cycle. Elevated abundances of
mixotrophs and heterotrophs relative to autotrophs during the stratified
and fall seasons indicate a complex recycling food web where small
protists such as Warnowia , Telonemia , Karlodiniumor Minorisa minuta and MAST and MOCH lineages prey on
picophytoplankton, responsible of most of the primary production in the
Sargasso Sea (Caron et al. 1999; Cotti-Rausch et al. 2020;
del Campo et al. 2013; Klaveness et al. 2005; Massanaet al. 2014; Orsi et al. 2018; Place et al. 2012;
Riemann et al. 2011; Sanders et al. 2000). Both autotrophs
and small mixo- and heterotrophs would also be preyed upon by larger
mixotrophs or heterotrophs protists (Andersen et al. 2011; Evelyn
& Michael 1998; Quevedo & Anadón 2001). Autotrophic protists
(picophytoplankton and small nanophytoplankton, such asOstreococcus , Bathycoccus or Pelagomonas
calceolata ) had a numerical relevance only in the mixed and spring
seasons, but mixotrophs still represent a significant portion of the
community. Those mixotrophs would benefit from their ability to carry
out photosynthesis, while still preying on the small autotrophs (Sanderset al. 2000), likely due to the concurring C fixation by
photosynthesis with the heterotrophic acquisition of N and P (Edwards
2019). Recently, Choi et al. (2020) reported elevated summer
abundances of uncultured dictyophytes at BATS, and evidence that these
taxa are more prevalent in the upper water common when nutrients are
most depleted. These and other observations, and the findings we report,
suggest that mixotrophic strategies become relatively more advantageous
when production is limited by macronutrients, likely due to the
repartitioning of N and P rather than the energetic benefits of
mixotrophy. As such, in the oligotrophic Sargasso Sea they would be at a
significant advantage compared to non-mixotrophs (Choi et al.2020; Duhamel et al. 2019; Edwards 2019), only attenuated during
the strong mixing period.
The observed month-to-month depth expansion and contraction of the
different protist communities influences community functionality.
Cluster 1, which occupies the near surface layers throughout the spring
and stratified season, disappears in fall when the surface MLD begins to
erode into the DCM, and only emerges again when the ML abruptly shoals
the following spring. The temporary nature of this group contrasts with
the other communities, which are likely present throughout the year,
albeit with periods of expansion and contraction. As such, heterotrophy,
and its trophic and biogeochemical consequences, would increase in
summer and fall, with the expansion of the stratified surface community.
The fall is also when the Chl-a -max corresponds to a community
rich in heterotrophs, although many are potential mixotrophs via
endosymbionts (Bjorbækmo et al. 2020). The effect of these
non-constitutive mixotrophs on the biogeochemical cycles is one major
question to be addressed in the future. Although in some cases a few
clusters are missing in some months, it might be an artifact of the
punctuated depth sampling, which would miss their depths at that
particular month. As a comparison, the bottle sampling also missed some
of the density-derived layers, but they were all present in the
continuous profiles. In those months, some of the deeper clusters were
then missed.
It should be noted that in some cases the functionality of taxa is
relatively easy to assign (e.g. Telonemia orOstreococcus ), while in many other cases it is difficult to
assess with certainty. Close relatives, with different functional
profiles (i.e. mixotrophy vs. autotrophy) often share a single V4
sequence. Other sources of uncertainty come from the non-constitutive
mixotrophs. In our case, the main group is Rhizaria. These are known to
potentially carry photosynthetic endosymbionts (e.g., Phaeocystissp.) (Bjorbækmo et al. 2020). Addressing these uncertainties is
required to understand the full role of mixotrophy in the oceans, and
its influence in the biogeochemical cycles (Edwards 2019; Flynn et
al. 2019; Gonçalves Leles et al. 2021; Gonçalves Leles et
al. 2018; Mitra et al. 2014).
In the mesopelagic zone, protist are vertically partitioned into
distinct communities that occupy specific density strata. This may
reflect a response to resource availability and/or particular
environmental conditions (Biard & Ohman 2020), such as the OMZ, where
there is an increase in particles. Within the main clades, specific
lineages also occupied specific vertical layers, suggesting a preferred
niche for each. An alternate explanation is that vertical biological
gradients may reflect “where” and “when” the density layers were at
the sea surface – i.e. the ventilated thermocline model of gyre
circulation (Luyten et al. 1983). The “age” of a water parcel
at BATS typically increases with depth/density reflecting longer
distances traveled since ventilation. Geographic gradients in surface
community composition could thus translate to vertical gradients at
mesopelagic depths, and/or, if the community composition evolves as a
function of time, the vertical gradients may reflect “age”.
Diversity patterns
Our findings, showing higher diversity in the epipelagic, peaking below
the Chl-a max., and then decreasing with depth, are opposite in
pattern to the study by Ollison et al. (2021). These differences can be
attributed to many causes. There is great disparity in the environmental
conditions, with strong seasonality and fully oxic OMZ at BATS compared
to the quasi-permanent conditions and the thick and suboxic OMZ at ALOHA
(Church et al. 2013) . Additionally there are pure methodological
differences: the present manuscript is based in DNA metabarcoding, while
Ollison et al. (Ollison et al. 2021) used an RNA-based approach.
Our data, however, coincides with the findings of Canals et al. (2020),
where a similar pattern of decreasing diversity with depth and a peak
around the Chl-a maximum, was found for ciliates in the Atlantic Ocean,
suggesting that the environmental landscape (the hydrography) is driving
the differences between ocean basins.
The clear signal of increasing phylogenetic diversity towards the
Chla-max, with the layers below the 1% (but above the mesopelagic)
showing the highest diversity, is notable. This maxima might be related
to the presence of more, and more diverse, ecological niches; here light
and nutrients are able to sustain a rich autotroph community (below it
is too dark; nutrients are scarce above). This pattern contrasts with
the taxonomic diversity, either H’, or SVs richness, which did not show
a noticeable change in the epipelagic. This mismatch is significant
since PD is often directly correlated to species richness (Barker 2008;
Voskamp et al. 2020). The pattern here likely reflects the abrupt
transition from an Alveolata-dominate surface community towards one more
phylogenetically diverse, despite a relatively constant species
richness, owing to the availability of nutrients. The overlapping
presence of light and nutrient gradients would favor the presence of
more, and more diverse, ecological niches, facilitating the coexistence
of more phylogenetically apart lineages.
The mesopelagic communities exhibited lower diversity and tighter
grouping in the PCoA, indicative of less month-to-month variability
compared to epipelagic communities. These patterns may be related to
seasonality in the epipelagic layers, where a more dynamic environment
would favor community succession processes month to month (represented
as a higher dispersal between samples) and inhibits efficient
competitive exclusion processes (causing higher diversity within
sample). The deeper layers, however, characterized by less environmental
variability, would favor the same lineages with competitive advantages
month after month, limiting the number of taxa, and resulting in more
stable communities.
Methodological caveats
Metabarcoding approaches, as any other method, are subject to many
methodological limitations and bias (Bucklin et al. 2016;
Santoferrara 2019). The initial selection of which region and primers to
use, the marker copy number variability between taxa, etc., are a source
of biases that would affect the description of the community. Other
source of uncertainty is the taxonomic assignment of the different
lineages (SV or OTUs), due to both incomplete databases (where there is
a lot of work to do by the community), and to the lack of resolution at
the species level by most ribosomal markers. To mitigate these biases,
the taxonomy for the discussed lineages was analyzed against GenBank, to
understand the ambiguity of the taxonomic assignment, and corrected if
needed (compared to the mothur PR2 based assignment).
Regarding the copy number bias, we are confident that the core of this
manuscript, the seasonal and depth community transitions, are not
affected by this fact, and would very likely hold true independently of
the marker used. Finally, diversity indices (the raw value itself)
should be considered only in the context of this study, since
methodological questions as the indicated or the rarefaction (or
extrapolation) level chosen can drastically affect diversity estimates
(Blanco-Bercial 2020; Santoferrara 2019). Again, despite any possible
bias, it is likely that the observed patterns of variability (where
diversity is higher/lower, etc.) are robust against those bias. Any
comparison with other existing data would require a reanalysis with
shared bioinformatics approaches.