Discussion
Phytoplankton communities respond quickly to environmental changes
across seasons largely by a dynamic relationship among species, however,
the evolutionary potential and adaptability of individual species are
still not well understood. Past experimental evolution studies have
shown that strong directional selection pressures can lead to adaptation
through de novo mutations, often within a few hundred generations
(Collins & Bell, 2004; Collins et al., 2014; Malerba et al., 2020;
Schaum et al., 2018). However, the role of selection on standing genetic
variation has to date not been directly assessed. In this study, we
utilized a newly developed strain-specific metabarcoding approach to
track selection among strains in two populations of a common pelagic
diatom, and we were able to assess how rapidly selection acts on
standing genetic variation. Our first hypothesis was that the diatomS. marinoi had evolved elevated tolerance to copper at a
mining-exposed inlet. We found some support for this hypothesis from the
observation that a small subset (10-15%) of the strains were much more
tolerant to copper stress than any of the strains from a reference
inlet. Secondly, we hypothesized that genetic variation would allow the
population exposed to copper close to a mining site to evolve copper
tolerance more rapidly, and with a larger amplitude, than from a single
strain genotype, or from an unexposed population. This hypothesis was
also supported by the outcome of the artificial evolution experiment
where the mining population rapidly selected for the most tolerant
strain in less than 50 generations and recovered more than three times
more fitness (growth rate) than the mono-clonal RO5AC strain and the
reference population.
Metabarcoding revealed that strain selection drove the evolutionary
responses of our artificial populations, providing empirical support to
the hypothesis that standing genetic variation within phytoplankton
populations can support rapid adaptation (Godhe & Rynearson, 2017). In
contrast, our results did not suggest that de-novo mutations were
involved. Mutations are, of course, the ultimate source of genetic
variation, and that we observed no tolerant strains at the reference
site suggests that the trait had evolved locally during centuries of
mining exposure, presumably through mutations and/or recombination.
Acquiring de-novo mutations is generally a slow process because
the vast majority of mutations are near neutral or deleterious (Kimura,
1983; Ohta, 1992) and restricted to affecting one locus at a time (Karve
& Wagner, 2022; Tupin et al., 2010), which is why such processes could
not rival selection from standing variation over relatively short
experimental periods, such as in our experiment (50-100 generations).
In one isolated replicate, sexual recombination and loss of
heterozygosity caused a strain to develop a hyper copper tolerant
phenotype. This observation suggests that much of the fitness
variability that de-novo mutations could rapidly generate may
already reside in natural populations and be available for selection to
act upon, especially if recombined into diverse combinations. This is a
reasonable expectation as phytoplankton populations harbor up to three
percent single nucleotide polymorphic diversity across the genome
(Flowers et al., 2015; Mock et al., 2017), contain large-scale
re-arrangements across species pan genomes (Blanc-Mathieu et al., 2017;
Kashtan et al., 2014; Osuna-Cruz et al., 2020; Read et al., 2013), and
can facilitate substantial rates of horizontal gene transfer (Vancaester
et al., 2020). Such high standing genetic variation, combined with
recombination during meiosis or horizontal gene transfer, should enable
phytoplankton populations to rapidly combine favorable alleles from
distant loci, strains, or even separate taxa. It is therefore reasonable
to expect that similar to the situation in macroorganisms (Barrett &
Schluter, 2008), outcrossing and selection from standing genetic
variation should provide the primary potential for evolutionary change
in phytoplankton populations, and provide short-term adaptations to both
seasonal changes, spatial heterogeneity, and anthropogenic stressors
such as metal pollution.
Strong toxic selection pressures are expected to purge sensitive
genotypes from a population (Blanck, 2002). In contrast, our results
showed that sensitive strains persisted in the mining exposed population
but that a small subset of strains (three out of 30) had evolved, and
retained, permanently high tolerance to copper. This suggests that the
mining site population is currently not experiencing a constant strong
selection pressure from copper, or that a trade-off between toxic copper
tolerance and other components of fitness exists, such as nutritional
copper uptake (Sunda, 2012). The lack of tolerant strains at the
reference site, and inhibition of adapted cultures by growth media with
low/regular copper concentrations, support the notion of a fitness cost
associated with high copper tolerance. There is unfortunately no
available timeseries of monitoring data of water concentrations of
metals from the mining area around Västervik Gåsfjärden, and we can only
speculate on the selective processes that shaped copper tolerance of the
two S. marinoi populations.
Since the mining activity ceased ca. 1920, and because metal
concentrations in the sediment have declined since the 1980s (Ning et
al., 2018), toxic exposure could be a historical event dating back to
the active mining period. Alternatively, the mining inlet population may
still experience fluctuating selection pressures between toxic and
non-toxic copper conditions, as the mining tailings are still exposed to
varying degrees of weathering. The fact that we chose to assay copper
tolerance in the resting stage population rather than the actively
growing planktonic population may also have influenced the copper
tolerance trait distribution. Resting stages can remain viable for at
least a decade (Lewis et al., 1999), potentially even centuries
(Härnström et al., 2011), providing an evolutionary buffer against loss
of diversity during periods of strong directional selection on the
planktonic population (Sundqvist et al., 2018). Therefore, our
populations likely contain resting stages from multiple bloom seasons,
and tolerant strains may have been deposited during phases of high
copper concentration, and sensitive ones during ambient conditions.
However, we also cannot exclude the possibility that the larger
variation in the mining population stems from spurious effect from
having only two sampling locations, or other sampling artifacts. Because
of the 10-fold slower sedimentation rate at the mining inlet
(Supplemental Method and Results), we germinated resting stages from
sediment deposited between 1995-2010, compared with 2012-2015 at the
reference inlet. The potentially older and more extended deposition
range at the mining inlet may have captured a more diverse set of
strains than at the reference inlet. Irresectable of the driver of the
larger variation in copper tolerance in the mining exposed population,
our finding highlights the important of incorporate large amounts of
strain diversity in adaptive studies of phytoplankton.
The variation in copper tolerance among distinct experimental strains
raises the question of how much variation there is in a natural
population. With a few notable exceptions (Ajani et al., 2020; Gross et
al., 2017; Schaum et al., 2016), artificial evolution experiments and
monoculture phenotyping studies incorporate less than ten strains to
represent an entire population or species (Lohbeck et al., 2012; Ribeiro
et al., 2011; Sassenhagen et al., 2015; Wolf et al., 2019). This is
likely insufficient to represent the actual diversity of most
phytoplankton populations, not least during blooms when they are
predicted to contain thousands to millions of unique clones (Sassenhagen
et al., 2021). Furthermore, pan-genome studies show that even if
hundreds of strains are sequenced, they generally do not saturate
(Kashtan et al., 2014). Mesocosm experiments using natural communities
can incorporate sufficiently large population sizes to represent clonal
population diversity, yet such experiments are challenging to analyze
and maintain, and still rare [but see (Schaum et al., 2017; Scheinin
et al., 2015)].
Using a strain-specific metabarcoding approach (Pinder et al., 2023)
improve our experimental analysis in several ways. First,
strain-specific metabarcoding estimates of fitness (growth rate and
copper tolerance) provided, on average, three times higher precision
than mono-clonal experiments. Second, the reduced number of experimental
bottle replicates of mixed-culture experiments enabled longer and more
complex experiments to be performed (Gresham & Dunham, 2014). Running
selection experiments for a long time added the benefit that it also
resolved if plastic responses develop over time, and if the plastic
potential differed between strains. In our experiments, metabarcoding
revealed that certain strains could develop a high degree of plasticity
relatively slowly (over 2-10 generations), which the mono-clonal
dose-response curves failed to capture. This was somewhat surprising
since the 72-hrs dose-response assay proceeded over multiple generations
(3-7 at <EC50), which is often sufficient to capture the
complete acclimation response in phytoplankton (Falkowski & LaRoche,
1991).
Third, with the strain-specific metabarcoding we could incorporate more
strains without added experimental effort. Since we have developed three
additional barcode loci for S. marinoi (Pinder et al., 2023),
multiplexing of several barcodes should enable separation between even
more strains, or strains that are not clones, but homologous in the
locus Sm_C12W1 . However, such approaches will only be possible
if the allelic genotypes of all strains in the selection experiment are
known, something that in diploid taxa requires strain isolation and
genotyping with molecule resolution (through molecular resolution
sequencing) to parse out the alleles. Yet with this added sequencing
effort, strain-specific metabarcoding experiments should be able to
incorporate much higher amounts of diversity than the 58 strains used in
our study.
Finally, a strain-specific metabarcoding approach is arguably more
accurate in determining the relative fitness of strains, compared with
mono-clonal fitness estimations. In part, this is because growth in
mixed population removes bottle effects and other experimental artifacts
associated with mono-clonal phenotyping (Robinson et al., 2014). More
importantly, mixed culture experiments can incorporate interactions
between strains and their shared aqueous environment. Strain-specific
metabarcoding should therefore be compatible with mesocosm experiments
on natural plankton communities (Scheinin et al., 2015; Tatters et al.,
2013), or experiments investigating the evolutionary effects of
predation (Sjöqvist et al., 2014), nutrient competition within (Collins,
2011) and between species (Descamps-Julien & Gonzalez, 2005), or other
fitness traits that are challenging to determine using mono-clonal
assays.