Discussion
Comparing to the traditional phytoplankton functional groups development
(Reynolds et al. 2002), affinity analysis is a method for rapidly
finding phytoplankton associations from a large dataset. It has the
advantage of time-saving and easy use, especially for new algae
researchers in a region with limited ecological studies on local
phytoplankton assemblages. So affinity analysis can be used as a first
step to identify candidate phytoplankton associations.
The identified phytoplankton associations reflect the ecological
preferences of phytoplankton including the resources acquisition (e.g.,
light and nutrients) and competitive abilities (e.g., r/K selection or
C-S-R model) (Salmaso et al. 2015). Cryptomonas erosa andCryptomons ovata or Chroomonas acuta from
the same family were often concurrent in HRB (Table 2). These species
can benefit from both mixotrophy and phagotrophy, and also can tolerate
high dissolved nutrients and limiting light conditions (Graham &
Wilcox, 2000; Kruk et al. 2012), and can avoid grazing by zooplankton.
Some taxa from different divisions can form the associations such as
diatom-cryptophytes. Diatom-cryptophytes associations are consistent
with what Sommer et al (1986) suggested that Cryptophyceae and small
centric diatoms developed together when nutrients were available and
light increased in spring because of their small volume and high growth
rate. Some of the identified phytoplankton associations can reflect the
grazing pressure. The observed associations are consistent with the
notion that both Scenedesmus and Selenastrum are more
resistant to grazing than either Chlamydomonas orAnkistrodesmus , while the latter two taxa are better competitors
in the absence of grazing (Drake 1993). Motile benthic diatoms such asNitzschia palea are concurrent with some planktonic algae.
Benthic diatom motility offers a selective advantage on silty substrata,
but it is also correlated with some ecological traits((Passy, 2007).
Kawamura et al (2004) demonstrated that grazing pressure of gastropods
had an influence on the Nitzschia species.
We performed a RDA for assessing the applicability of the identified
phytoplankton associations in environmental assessment. In HRB, the
light and TN were the best predictors of phytoplankton associations
(Fig. 3). Our results are consistent with Mackay et al (2012) that the
diatom-association was strongly with the TN nutrient. In Dishui Lake,
the light and salinity were the best predictors for phytoplankton
associations (Fig. 4). Chrysophytes are restricted to cold, oligotrophic
conditions. Small Chromulina groups showed a different response
to pH and
water
clarity, compared to the
medium size Chlamydomonas groups (Fig. 4). The importance of pH
as a primary factor affecting chrysophytes has been reported in studies
from widely separated geographic regions. Chromulina andChlamydomonas are bothr -selected taxa, their small-medium body size and motility
conferred by flagella are advantages and allow them to reduce sinking
rate (Kruk et al. 2010). Compare the Chlamydomonas , theChromulina prefer the oligotrophic environments with an abundance
of macrophytes (Thiago Rodrigues dos Santos1 · Carla Ferragut, 2019).
Compared to the river, more variance (33%) in phytoplankton
associations can be explained by different combinations of environmental
factors in the man-made shallow lake. A lake is perceived to be
relatively stable, that of a river, is characteristically
graded from the origin to the river-mouth (Reynolds et al. 1994).
Therefore, candidate phytoplankton associations are reasonable proxies
for explaining environmental variables.
Binary data were used to construct the phytoplankton associations in
this work, which ignores the abundance of phytoplankton species.
Although binary data are commonly observed and analyzed in many
application fields (Yamamoto & Hayashi, 2015), some species which were
not abundant potentially contributed much more to the analysis than
those common taxa but we minimized the effects. The phytoplankton
associations identified by affinity analysis should be viewed as
candidate associations and each association should be carefully
evaluated using ecological theories and concepts.
In essence, affinity analysis can be a useful method for finding the
phytoplankton associations from the complex and informative dataset. It
can explain some fraction of the variance from the both spatial and
temporal algal assemblages distribution patterns, although their
effectiveness varies differently in river and lake, depending on the
gradients of environmental factors. Our results do not mean that the
proposed method should replace the conventional ecological
classifications of phytoplankton. The proposed method provides an
alternative, especially for the regions where researches on
phytoplankton assemblages are still limited. Affinity analysis remains
to be tested in future research whether it is predicted better for
phytoplankton associations than other classification systems.