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.