Freshwater phytoplankton diversity: models, drivers and implications for ecosystem properties

  • Review Paper
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  • Published: 04 July 2020
  • Volume 848 , pages 53–75, ( 2021 )

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  • Gábor Borics 1 , 2 ,
  • András Abonyi 3 , 4 ,
  • Nico Salmaso 5 &
  • Robert Ptacnik 4  

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Our understanding on phytoplankton diversity has largely been progressing since the publication of Hutchinson on the paradox of the plankton. In this paper, we summarise some major steps in phytoplankton ecology in the context of mechanisms underlying phytoplankton diversity. Here, we provide a framework for phytoplankton community assembly and an overview of measures on taxonomic and functional diversity. We show how ecological theories on species competition together with modelling approaches and laboratory experiments helped understand species coexistence and maintenance of diversity in phytoplankton. The non-equilibrium nature of phytoplankton and the role of disturbances in shaping diversity are also discussed. Furthermore, we discuss the role of water body size, productivity of habitats and temperature on phytoplankton species richness, and how diversity may affect the functioning of lake ecosystems. At last, we give an insight into molecular tools that have emerged in the last decades and argue how it has broadened our perspective on microbial diversity. Besides historical backgrounds, some critical comments have also been made.

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Phytoplankton is a polyphyletic group with utmost variation in size, shape, colour, type of metabolism, and life history traits. Due to the emerging knowledge in nutritional capabilities of microorganisms, our view of phytoplankton has drastically changed (Flynn et al., 2013 ). Phagotrophy is now known from all clades except diatoms and cyanobacteria. At the same time, ciliates, which have not been considered as part of ‘phytoplankton’, span a gradient in trophic modes that render the distinction between phototrophic phytoplankton and heterotrophic protozoa meaningless. This complexity has been expressed in the high diversity of natural phytoplankton assemblages. Diversity can be defined in many different ways and levels. Although the first diversity measure that encompassed the two basic components of diversity (i.e., the number of items and their relative frequencies) appeared in the early forties of the last century (Fisher et al., 1943 ), in phytoplankton ecology, taxonomic richness has been used the most often as diversity estimates. Until the widespread use of the inverted microscopes, phytoplankton ecologists did not have accurate abundance estimation methods and the net plankton served as a basis for the analyses. Richness of taxonomic groups of net samples, and their ratios were used for quality assessment (Thunmark, 1945 , Nygaard, 1949 ).

The study of phytoplankton diversity received a great impetus after Hutchinson’s ( 1961 ) seminal paper on the paradox of the plankton. The author not only contrasted Hardin’s competitive exclusion theory (Hardin, 1960 ) with the high number of co-occurring species in a seemingly homogeneous environment, but outlined possible explanations. He argued for the non-equilibrium nature of the plankton, the roles of disturbances and biotic interactions, moreover the importance of benthic habitats in the recruitment of phytoplankton. The ‘paradox of the plankton’ largely influenced the study of diversity in particular and the development of community ecology in general (Naselli-Flores & Rossetti, 2010 ). Several equilibrium and non-equilibrium mechanisms have been developed to address the question of species coexistence in pelagic waters (reviewed by Roy & Chattopadhyay, 2007 ). The paradox and the models that aimed to explain the species coexistence in the aquatic environment have been extended to terrestrial ecosystems (Wilson, 1990 ). Wilson reviewed evidences for twelve possible mechanisms that potentially could explain the paradox for indigenous New Zealand vegetation, and found that four of them, such as gradual climate change, cyclic successional processes, spatial mass effect and niche diversification, were the most important explanations. By now, the paradox has been considered as an apparent violation of the competitive exclusion principle in the entire field of ecology (Hening & Nguyen, 2020 ).

Although Hutchinson’s contribution (Hutchinson, 1961 ) has given a great impetus to research on species coexistence, the number of studies on phytoplankton diversity that time did not increase considerably (Fig.  1 ), partly because in this period, eutrophication studies dominated the hydrobiological literature.

figure 1

Annual number of hits on Google Scholar for the keywords “phytoplankton diversity”

Understanding the drivers of diversity has been substantially improved from the 70 s when laboratory experiments and mathematical modelling proved that competition theory or intermediate disturbance hypothesis (IDH) provided explanations for species coexistence. Many field studies also demonstrated the role of disturbances in maintaining phytoplankton diversity, and these results were concluded by Reynolds and his co-workers (Reynolds et al., 1993 ).

From the 2000 s a rapid increase in phytoplankton research appeared (Fig.  1 ), which might be explained by theoretical and methodological improvements in ecology. The functional approaches—partly due to Colin Reynolds’s prominent contribution to this field (Reynolds et al., 2002 )—opened new perspectives in phytoplankton diversity research. Functional trait and functional ‘group’-based approaches have gained considerable popularity in recent years (Weithoff, 2003 ; Litchman & Klausmeier, 2008 ; Borics et al., 2012 ; Vallina, et al., 2017 ; Ye et al., 2019 ).

Analysis of large databases enabled to study diversity changes on larger scales in lake area, productivity or temperature (Stomp et al., 2011 ). Recent studies on phytoplankton also revealed that phytoplankton diversity was more than a single metric by which species or functional richness could be described, instead, it was an essential characteristic, which affects functioning of the ecosystems, such as resilience (Gunderson 2000 ) or resource use efficiency (Ptacnik et al., 2008 ; Abonyi et al., 2018a , b ).

The widespread use of molecular tools that reorganise phytoplankton taxonomy and reveal the presence of cryptic diversity, has changed our view of phytoplankton diversity. In this study, we aim to give an overview of the above-mentioned advancements in phytoplankton diversity. Here we focus on the following issues:

measures of diversity,

mechanisms affecting diversity,

changes of diversity along environmental gradients (area, productivity, temperature),

the functional diversity–ecosystem functioning relationship, and

phytoplankton diversity using molecular tools.

More than eight thousand studies have been published on “phytoplankton diversity” since the term first appeared in the literature in the middle of the last century (Fig.  1 ), therefore, in this review we cannot completely cover all the important developments made in recent years. Instead, we focus on the most relevant studies considered as milestones in the field, and on the latest relevant contributions. This study is a part of a Hydrobiologia special issue dedicated to the memory of Colin S. Reynolds, who was one of the most prominent and influential figures of phytoplankton ecology in the last four decades, therefore, we have placed larger emphasis on his concepts that helped our understanding of assembly and diversity of phytoplankton.

Measures of diversity

In biology, the term “diversity” encompasses two basic compositional properties of assemblages: species richness and inequalities in species abundances. Verbal definitions of diversity cannot be specific enough to describe both aspects, but these can be clearly defined by the mathematical formulas that we use as diversity measures.

Richness metrics

The simplest measure of diversity is species richness, that is, the number of species observed per sampling unit. However, this metric can only be used safely when the applied counting approach ensures high species detectability.

In case of phytoplankton, species detectability depends strongly on counting effort, therefore, measures that are standardised by the number of individuals observed, e.g. Margalef and Mehinick indices (Clifford & Stephenson, 1975 ) safeguard against biased interpretations. Ideally, standardization should take place in the process of identification. Pomati et al. ( 2015 ) gave an example how a general detection limits could be applied in retrospect to data stemming from variable counting efforts.

Species richness can also be given using richness estimators. These can be parametric curve-fitting approaches, non-parametric estimators, and extrapolation techniques using species accumulation or species-area curves (Gotelli & Colwell, 2011 ; Magurran, 2004 ). These approaches have been increasingly applied in phytoplankton ecology (Naselli-Flores et al., 2016 ; Görgényi et al., 2019 ).

Abundance-based metrics

Classical diversity metrics such as Shannon and Simpson indices combine richness and evenness into univariate vectors. Though used commonly in the literature, they are prone to misinform about the actual changes in a community, as they may reflect changes in evenness and/or richness to an unknown extent (a change in Shannon H' 1948 ) may solely be driven by a change in evenness or richness). Dominance metrics emphasise the role of the most important species (McNaughton, 1967 ). Rarity metrics, in contrast, focus on the rare elements of the assemblages (Gotelli & Colwell, 2011 ).

Species abundance distributions (SAD) and rank abundance distributions (RAD: ranking the species’ abundances from the most abundant to the least abundant) provide an alternative to diversity indices (Fisher et al., 1943 ; Magurran & Henderson, 2003 ). These parametric approaches give accurate information on community structure and are especially useful when site level data are compared. Most RADs follow lognormal distributions and allow to estimate species richness in samples (Ulrich & Ollik, 2005 ).

Mechanisms affecting diversity

  • Community assembly

Understanding the processes that shape the community structure of phytoplankton requires some knowledge on the general rules of community assembly. Models and mechanisms, which have been proposed to explain the compositional patterns of biotic communities, can be linked together under one conceptual framework developed by Vellend ( 2010 , 2016 ). Vellend proposed four distinct processes that determine species composition and diversity: speciation (creation of new species, or within-species genetic modifications), selection (environmental filtering, and biotic interactions), drift (demographic stochasticity) and dispersal (movement of individuals). The four processes interact to determine community dynamics across spatial scales from global, through regional to local. The importance of the processes strongly depends on the type of community, and the studied spatial and temporal scales (Reynolds, 1993 ).

Importance of evolutionary processes in the community assembly have been demonstrated by several phylogenetic ecological studies (Cavender-Bares et al., 2009 ) and also indicated by the emergence of a new field of science called ecophylogenetics (Mouquet et al., 2012 ). As far as the phytoplankton is concerned, the role of speciation can be important when the composition and diversity of algal assemblages are studied at large (global) spatial scales. However, we may note that although microscopic analyses cannot grasp it, short-term evolutionary processes do occur locally in planktic assemblages (Balzano et al., 2011 ; Padfield et al., 2016 ; Bach et al., 2018 ).

Demographic stochasticity influences growth and extinction risk of small populations largely (Parvinen et al., 2003 ; Méndez et al., 2019 ). Similarly, it might also act on large lake phytoplankton since population size in previous years affects the success of species in the subsequent year. Small changes in initial abundances may have strong effects on seasonal development. Demographic stochasticity, however, is crucial in small isolated waters (especially in newly created ones) where the sequence of new arrivals and small differences in initial abundances likely have a strong effect on the outcome of community assembly.

Theoretical models, laboratory experiments and field studies demonstrated that the other two processes, selection and dispersal, have a pivotal role in shaping community assembly and diversity. Although this statement corresponds well with the Baas-Becking ( 1934 ) hypothesis (everything can be everywhere but environment selects), importance of selection and dispersal depends on the characteristics of the aquatic systems. Selection and dispersal can be considered as filters (Knopf, 1986 , Pearson et al., 2018 ), and using them as gradients, a two-dimensional plane can be constructed, where the positions of the relevant types of pelagic aquatic habitats can be displayed (Fig.  2 ). At high dispersal rate, the mass effect (or so-called source-sink dynamics) is the most decisive process affecting community assembly (Leibold & Chase, 2017 ). Phytoplankton of rhithral rivers is a typical example of the sink populations because its composition and diversity are strongly affected by the propagule pressure coming partly from the source populations of the benthic zone and from the limnetic habitats of the watershed (Bolgovics et al., 2017 ). The relative importance of the mass effect decreases with time and with the increasing size of the river, while the role of selection (species sorting) increases. Due to their larger size, the impact of the source-sink dynamics in potamal rivers must be smaller, and selection becomes more important in shaping community assembly. Although the role of spatial processes in lake phytoplankton assembly cannot be ignored, their importance is considerably less than that of the locally acting selection. Relevance of the spatial processes have been demonstrated for river floodplain complexes (Vanormelingen et al., 2008 ; Devercelli et al., 2016 ; Bortolini et al., 2017 ), or for the lakes of Fennoscandia (Ptacnik et al., 2010a , b ), where the large lake density facilitates the manifestation of spatially acting processes. High selection and low dispersal represent the position of phytoplankton inhabiting isolated lakes. Reviewing the literature of algal dispersal Reynolds concluded ( 2006 ) that cosmopolitan and pandemic distribution of algae is due to the fact that most of the planktic species effectively exploit the dispersal channels. However, he also noted that several species are not good dispersers, therefore, endemism might occur among algae.

figure 2

Positions of the relevant types of pelagial aquatic habitats in the selection/dispersal plane

Composition and diversity of these assemblages are controlled by the locally acting environmental filtering and by biotic interactions, frequently, by competition. The environmental filtering metaphor appears in Reynolds’ habitat template approach (Reynolds, 1998 ), where the template is scaled against quantified gradients of energy and resource availability. The template represents the filter, while the habitats mean the porosity (Reynolds, 2003 ). Species that manage to pass the filter are the candidate components of the assemblages. Finally, low-level biotic interactions (Vellend, 2016 ) determine the composition and diversity of the communities.

The four mechanisms, proposed by Vellend, act differently on the various metric values of diversity. Using the special cases of Rényi’s entropy (α: → 0, 1, 2, ∞) (ESM Box 1) we can show how mechanisms influence species richness and species inequalities, and how they act on the metrics between these extremes (ESM Table 1). Drivers of functional diversity are identical with that of species diversity, but their impacts are attenuated by the functional redundancy of the assemblages.

The role of competition in the maintenance of diversity

The concept of competition and coexistence has been first proved experimentally both for artificial two-species systems (Tilman & Kilham, 1976 ; Tilman, 1977 ) and for natural phytoplankton assemblages (Sommer, 1983 ). However, limitations by different nutrients are responsible only for a small portion of diversity, even if the micronutrients are also included. Therefore, it was an important step when Sommer ( 1984 ) applying a pulsed input of one key nutrient in a flow-through culture managed to maintain the coexistence of several species; although they were competing for the same resource. Several competition experiments have been carried out in recent years demonstrating the role of inter- (Ji et al., 2017 ) and intra-specific competition (Sildever et al., 2016 ) in the coexistence of planktic algae.

The fact that one single resource added in pulses can maintain the coexistence of multiple species has been also proved by mathematical modelling (Ebenhöh, 1988 ). Using deterministic models, Huisman & Weissing ( 1999 ) showed that competition for three or more resources result in sustained species oscillations or chaotic dynamics even under constant resource supply. These oscillations in species abundance make possible the coexistence of several species on a few limiting resources (Wang et al., 2019 ).

The non-equilibrium nature of phytoplankton and the role of disturbances

One of the underlying assumptions of the classical competition theories is that species coexistence requires a stable equilibrium point (Chesson & Case, 1986 ). However, the stable equilibrium state is not a fundamental property of ecosystems (DeAngelis & Waterhouse, 1987 ; Hastings et al., 2018 ). Hutchinson put forward the idea that phytoplankton diversity could be explained by “permanent failure to achieve equilibrium” (Hutchinson, 1941 ). On a sufficiently large timescale, ecosystems seem to show transient dynamics, and do not necessarily converge to an equilibrium state (Hastings et al., 2018 ). However, the virtually static equilibrium-centred view of ecological processes cannot explain the transient behaviour of ecosystems (Holling, 1973 ; Morozov et al., 2019 ). Today, there is a broad consensus in phytoplankton ecology that composition and diversity of phytoplankton can be best explicable by non-equilibrium approaches (Naselli-Flores et al., 2003 ). The non-equilibrium theories do not reject the role of competition in community assembly but place a larger emphasis on historical effects, chance factors, spatial inequalities, environmental perturbations (Chesson & Case, 1986 ), and transient dynamics of the ecosystems (Hastings, 2004 ). The interactions among the internally driven processes and the externally imposed stochasticity of environmental variability as an explanation of community assembly have been conceptualized in the Intermediate Disturbance Hypothesis (IDH) (Connell, 1978 ). This hypothesis predicts a unimodal relationship between the intensities and frequencies of disturbances and species richness. Although this hypothesis has been developed for macroscopic sessile communities, it has become widely accepted in phytoplankton ecology (Sommer, 1999 ). It has been proposed that the frequency of disturbances has to be measured on the scale of generation times of organisms (Reynolds, 1993 ; Padisák, 1994 ). Field observation suggested that diversity peaked at disturbance frequency of 3–5 generation times (Padisák et al., 1988 ), which was also corroborated by laboratory experiments (Gaedeke & Sommer, 1986 ; Flöder & Sommer, 1999 ). The IDH, however, is not without weaknesses (Fox, 2013 ). Recognition and measurement of disturbance are among the main concerns (Sommer et al., 1993 ). Diversity changes are measured purely as responses to unmeasured events (disturbances) (Juhasz-Nagy, 1993 ), which readily leads to circular reasoning. Repeated disturbances might change the resilience of the system, which modifies the response of communities and makes the impact of disturbances on diversity unpredictable (Hughes, 2012 ).

Amalgamation of the equilibrium and non-equilibrium concepts

The existence of the equilibrium and non-equilibrium explanations of species coexistence represents a real dilemma in ecology. Being sufficiently different, and thus avoid strong competition, or sufficiently similar with ecologically irrelevant exclusion rates (as it is suggested by Hubbell’s neutral theory ( 2006 )) are both feasible strategies for species (Scheffer & van Nes, 2006 ). Coexistence of species with these different strategies is also feasible if the many sufficiently similar species create clusters along the niche axes (in accordance with Hubbel’s ( 2006 ) neutral theory), and the competitive abilities within the clusters are sufficiently large. It has been demonstrated that the so-called “lumpy coexistence” is characteristic for phytoplankton assemblages (Graco-Roza et al., 2019 ). Lumpy coexistence arises in fluctuating resource environments (Sakavara et al., 2018 ; Roelke et al., 2019 ), and show higher resilience to species invasions (Roelke & Eldridge, 2008 ) and higher resistance to allelopathy (Muhl et al., 2018 ).

The model of lumpy coexistence has its roots in mechanistic modelling of species coexistence (Scheffer & van Nes, 2006 ). Analysing lake phytoplankton data Reynolds ( 1980 , 1984 , 1988 ) demonstrated that species with similar preferences and tolerances to environmental constraints like nutrients or changes in water column stratification frequently coexist. These empirical observations were formalised later in the functional group (FG) concept (Reynolds et al., 2002 ). Despite their different theoretical backgrounds, the two approaches came to identical conclusions: species having similar positions on the niche axes form species clusters (or FGs), and in natural assemblages clusters or FGs coexist. Thus, the concept of lumpy coexistence can also be considered as a mechanistic explanation of the Reynolds’s FG concept.

The mechanisms and forces detailed above can explain how diversity is maintained at the local scale. Recent metacommunity studies, however, indicate that spatial processes have a crucial role in shaping phytoplankton diversity (Devercelli et al., 2016 ; Bortolini et al., 2017 ; Guelzow et al., 2017 ; Benito et al., 2018 ). Despite the increasing research activity in this field, spatial processes are far less studied than local ones. More in-depth knowledge on the role of connectivity of aquatic habitats and dispersal mechanisms of the phytoplankters will contribute to better understand phytoplankton diversity at regional or global scales.

Changes of diversity along environmental scales

Species–area relationships across systems.

The area dependence of species richness deserved special attention in ecology both from theoretical and practical points of view. The increase of species number with the area sampled is an empirical fact (Brown & Lomolino, 1998 ). The first model that described the so-called species–area relationship (SAR) appeared first by Arrhenius ( 1921 ) who proposed to apply power law for predicting species richness from the surveyed area. Because of the differences in the studied size scale and the studied organism groups, several other models have also been proposed such as the exponential (Gleason, 1922 ), the logistic (Archibald, 1949 ) and the linear (Connor & McCoy, 1979 ) models. However, the power-law ( S  =  c  ×  A z , where S : number of species; A : area sampled; c : the intercept, z : the exponent) is still the most widely used formula in SAR studies. The rate of change of the slope with an increasing area ( z value) depends on the studied organisms, and also on the localities. High values ( z : 0.1–0.5) were reported for macroscopic organisms (Durrett & Levin, 1996 ), while low z values characterised ( z : 0.02–0.08) the microbial systems (Azovsky, 2002 ; Green et al. 2004 ; Horner-Devine et al. 2004 ).

The phytoplankton SAR appeared first in Hutchinson’s ( 1961 ) paper, where he analysed Ruttner’s dataset on Indonesian (Ruttner, 1952 ), and Järnefelt’s ( 1956 ) data on Finnish lakes. He concluded that there was no significant relationship between the area and species richness. Hutchinson reckoned that contribution of the littoral algae to the phytoplankton might be relevant, and because the littoral/pelagic ratio decreases with lake size, this contribution also decreases. Therefore, species richness cannot increase with lake area. In a laboratory experiment, Dickerson & Robinson ( 1985 ) found that large microcosms had significantly smaller species richness values than small ones. Based on laboratory studies, published species counts from ponds lakes and oceans, Smith et al. ( 2005 ) studied phytoplankton SAR in the possible largest size scale (10 −9 to 10 7  km 2 ). They demonstrated a significant positive relationship between area and species richness. The calculated z value ( z  = 0.134) was higher than those reported in other microbial SAR studies. However, we note that this study suffers from a methodological shortcoming, because of differences in compilation of species inventories. Therefore, the results are only suggestive of possible trends that should be investigated more thoroughly.

Analysing phytoplankton monitoring data of 540 lakes in the USA Stomp et al. ( 2011 ) found only a slight increase in richness values with a considerable amount of scatter in the data. The covered size range was small in this study, and the applied counting techniques could lead to bias in richness estimation. Phytoplankton species richness showed a similar weak relationship with lake size for Scandinavian lakes (Ptacnik et al., 2010a , b ), although the counting effort was much better standardised. All the above studies suggested that species richness was not independent of water body size. However, because of the methodological differences, and differences in the covered water body size, in richness estimation or the type of the water bodies, any conclusions based on these results should be handled with caution.

Nutrients, latitudinal and altitudinal differences (Stomp et al., 2011 ) or the size of the regional species pool (Fox et al., 2000 , Ptacnik et al., 2010a , b ) also influence phytoplankton diversity. To reduce the impact of these factors, Várbíró et al. ( 2017 ) investigated phytoplankton SAR in a series of standing waters within the same ecoregion and with similar nutrient status. The water bodies covered ten orders of magnitude size range (10 −2 to 10 8  m 2 ). In this study, both the sampling effort and the sample preparation was standardised. The authors demonstrated that species richness did not scale monotonously with water body size. They managed to show the presence of the so-called Small Island Effect (SIE, Lomolino & Weiser, 2001 ), the phenomenon, when below a certain threshold area (here 10 −2 to 10 2  m 2 size range) species richness varies independently of island size. A right-skewed hump-shaped relationship was found between the area and phytoplankton species richness with a peak at 10 5 –10 6  m 2 area. This phenomenon has been called as Large Lake Effect (LLE) by the authors, and they explained it by the strong wind-induced mixing, which acts against habitat diversity in the pelagic zones of large lakes. The significance of this study is that its results help explain the controversial results of earlier phytoplankton SAR studies. The LLE explains why the species richness had not grown in the case of the Ruttner’s and Jarnefelt’s dataset. The SIE, however, explains why Dickerson & Robinson ( 1985 ) found opposite tendencies to SAR in microcosm experiments. Detailed analysis of the phytoplankton in those water bodies that produced the peak in the SAR curve in the study of Várbíró et al. ( 2017 ) demonstrated that high diversity has been caused by the intrusion of metaphytic elements to the pelagic zone (Görgényi et al., 2019 ), which can be considered as a within-lake metacommunity process.

Productivity–diversity relationships

Despite the more than half a century-long history of investigations on the productivity/diversity relationship (PDR), the shape of the relationship and the underlying mechanisms still remain a subject of debate. The models describing the PDR vary from the monotonic increasing, through the hump shaped and u-shaped to the monotonic decreasing types in the literature (Waide et al., 1999 ). In the PDR studies, there are great differences in the applied scale (local/regional/global), in the metric used to define productivity (e.g., nutrients, biomass, production rate, precipitation, evaporation), in the used diversity metrics, and also in the studied group of organisms (special phylogenetic groups, functional assemblages) (Mittelbach et al., 2001 ). PDR studies also have other methodological and statistical problems (Mittelbach et al., 2001 ). These differences in approaches may generate different patterns, which lead to confusion and inconclusive results (Whittaker & Heegaard, 2003 ; Hillebrand & Cardinale, 2010 ). Despite these uncertainties, the most general PDR patterns are the hump-shaped and positive linear relationships; the first has been observed mostly in the case of local, while the second in the case of regional scale studies (Chase & Leibold, 2002 ; Ptacnik et al., 2010a ). These patterns are so robust that they have been shown for various organisms independently from the highly different proxies applied to substitute the real productivity.

The number of studies that explicitly focus on phytoplankton PDR is few. The view that phytoplankton diversity peaks at intermediate productivity level has been demonstrated by several authors (Ogawa & Ichimura, 1984 ; Agustí et al., 1991 ; Leibold, 1999 ). This is greatly due to the fact that phytoplankton studies fortunately do not suffer from scaling problem: most studies use sample-based local α – s as diversity metrics and nutrients or biomass (Chl-a) as a surrogate measure of productivity. Unimodal relationships were found for Czech (Skácelová & Lepš, 2014 ) and Hungarian water bodies (Borics et al., 2014 ). Diversity peaked in both cases at the 10 1 –10 2  mg L −1 biovolume range, characteristic for eutrophic lakes.

It has also been demonstrated that the unimodal relationship was also true for the functional richness/productivity relationship (Borics et al., 2014 ; Török et al., 2016 ). Differences were also found between the species richness and functional richness peaks; the latter peaked at smaller biovolume range (Török et al., 2016 ). We note here that all three studies were based on monitoring data, and because of the applied sample processing, species richness values might be slightly underestimated.

Several theories have been proposed to explain this unimodal pattern. Moss ( 1973 ) reckoned that the relationship could be accounted for by that the populations of oligotrophic and eutrophic lakes overlapping at the intermediate productivity range. Rosenzweig’s ( 1971 ) paradox of enrichment hypothesis explained the unimodal relationship by the destabilized predator–prey relationship at enhanced productivity level. Tilman’s resource heterogeneity model ( 1985 ) predicts that the coexistence of competing species is enhanced when the supply of alternative resources is heterogeneous both spatially and temporally. This heterogeneity increases with resource supply together with species richness up to the point when richness declines because the correlation between spatiotemporal heterogeneity and resource supply disappeares. The resource-ratio hypothesis can also provide an explanation of the hump shaped PDR (Tilman & Pacala, 1993 ; Leibold, 1997 ). This theory suggests that relative supply of resources generates variations in species composition. Identity of the most strongly limiting resource changes, and at very high resource supply (on the descending end of the curve) only a few K-strategist specialists will prevail. The species pools overlap at intermediate productivity level, resulting in high species richness. This explanation seems to be reasonable for phytoplankton PDR studies.

Investigating the PDR in fishless ponds, Leibold ( 1999 ) found that his results could be best explained by the keystone predation hypothesis (Paine, 1966 ). This theory asserts that at low productivity exploitative competition is the main assembly rule, while with increasing productivity range the role of predator avoidance becomes more important.

The number of various explanations illustrates the complexity of processes affecting the shape of the PDR. The shifting effects of bottom-up vs. top-down control on the trophic gradient, the size of the regional species pool, that is, the number of potential colonizers, or the history of the studied water bodies (naturally eutrophic lakes are studied, or eutrophicated formerly oligotrophic ones) can considerably modify the properties of the PDRs.

With a few exceptions (Irigoien et al., 2004 ), phytoplankton PDRs have been studied almost exclusively in standing waters.

Investigating the phytoplankton PDRs in rivers Borics et al. ( 2014 ) found monotonic increasing pattern in rhithral and monotonic decreasing PDR in potamal rivers. They explained the positive linear PDR with the newly arriving species from the various adjacent habitats of the watershed, which resulted in high phytoplankton diversity even at highly eutrophic conditions. This phytoplankton is a mixture of those elements that enter the river from the connected water bodies of various types. In contrast, potamal rivers are highly selective environments in which the phytoplankton succession frequently terminates in low diversity plankton dominated by K strategist centric diatoms ( Cyclotella and S tephanodiscus spp.).

We note here that study of the regional phytoplankton PDR should be an important and challenging area of future work, which is presently hindered by the disconnected databases and by difficulties in measuring regional productivity.

Linkage between diversity and the metabolic theory of ecology

Metabolism controls patterns, processes and dynamics at each level of biological organisation from single cells to ecosystems, summarised as the metabolic theory of ecology (Brown et al., 2004 ). Metabolic theory (MTE) provides alternative explanations for observations on various fields of ecology such as in individual performance, life history, population and community dynamics, as well as in ecosystem processes. According to MTE, dynamics of metabolic processes have implications for species diversity. Metabolic processes influence population growth and interspecific competition, might accelerate evolutionary dynamics and the rate of speciation (Brown et al., 2004 ). The direct linkage between temperature and metabolic rate raises the possibility of new explanations of the well-known latitudinal dependence of species richness. Allen et al. ( 2002 ) found that for both terrestrial and aquatic environments natural logarithm of species richness should be a linear function of the mean temperature of the environment. This model has been tested both for lake and oceanic phytoplankton. Investigating more than 600 European, North and South American lakes Segura et al. ( 2015 ) found a pronounced effect of temperature on species diversity between 11 and 17 °C. Righetti et al. ( 2019 ) analysed the results of more than 500,000 phytoplankton observations from the global ocean, and also showed the relationship between temperature and species richness, but similarly to freshwater lakes the relationship was not monotonic for the whole temperature gradient. These results suggest that the MTE can be a possible explanation for the temperature dependence of diversity. However, we note that other theories emphasising longer “effective” evolutionary time (Rohde, 1992 ) or higher resource availability (Brown & Lomolino, 1998 ) can also explain this general pattern.

The functional diversity–ecosystem functioning relationship in phytoplankton

More diverse communities perform better in terms of resource use and ecosystem stability (Naeem & Li, 1997 ); known as the biodiversity-ecosystem functioning relationship (BEF). Similar to BEF relationships shown in terrestrial plant communities (Tilman et al., 1996 , 1997 ), positive BEF relationships have also been evidenced in both natural and synthetic phytoplankton communities (Ptacnik et al., 2008 ; Striebel et al., 2009 ; Stockenreiter et al., 2013 ). The BEF relationship itself, however, does not explain the mechanisms underlying the relationship. The most often recognised mechanisms are complementarity (Loreau & Hector, 2001 ) and sampling effect (Fridley, 2001 ). Complementarity means that more diverse communities complement each other in resource use in a more efficient way. Sampling effect, on the other hand, means that the chance increases for the presence of species with effective functional attributes in more diverse communities (Naeem & Wright, 2003 ).

In an attempt to get mechanistic understanding of diversity-functioning relationships, there is a growing interest in quantifying functional diversity of ecological communities (Hillebrand & Matthiessen, 2009 ). Functional diversity summarizes the values and ranges of traits that influence ecosystem functioning (Petchey & Gaston, 2006 ). By translating taxonomic into functional diversity, we may eventually also distinguish complementarity from sampling effect.

In phytoplankton ecology, two functional perspectives have been developing. First, the identification of morphological, physiological and behavioural traits (Weithoff, 2003 ; Litchman & Klausmeier, 2008 ) that affect fitness (Violle et al., 2007 ) and are, therefore, functional traits. Traits have been used in phytoplankton ecology at least since Margalef’s ‘life forms’ concept (Margalef, 1968 ; 1978 ), even if they were not referred to ‘traits’ explicitely (Weithoff & Beisner, 2019 ). Second, the recognition of characteristic functional units within phytoplankton assemblages led to the development of functional group (ecological groups) concepts (see Salmaso et al., 2015 ). These are the phytoplankton functional group concept sensu Reynolds (FG, Reynolds et al., 2002 ), the morpho-functional group concept (MFG, Salmaso & Padisák, 2007 ), and the morphological group concept (MBFG, Kruk et al., 2011 ).

The functional trait concept has been advocated in trait-based models (Litchman et al., 2007 ) and aimed at translating biotic into functional diversity, which eventually would allow quantify functional diversity at the community level. The functional trait concept has recently been reviewed in context of measures and approaches in marine and freshwater phytoplankton (Weithoff & Beisner, 2019 ). On the other hand, the ‘functional group’ concepts have rather been developed in the context of describing characteristic functional community compositions in specific set of environment conditions (that is, the functional community–environment relationship).

The simplest functional diversity measure of phytoplankton is the number of ‘functional units’ in assemblages. That is, either the number of unique combinations of functional traits or the number of ecological groups indentified. One way to use functional units is to convert them into univariate measures corresponding to those calculated from taxonomic information (e.g., richness, evenness). Or, trait data also allow the calculation of community-level means of trait values (CWM) as an index of functional community composition (Lavorel et al., 2008 ). Second, one may consider calculate the components of functional diversity (FD) such as functional richness, functional evenness, and functional divergence (Mason et al., 2005 ); all representing independent factes of functional community compositions. The same FD concept has been developed further accounting also for the abundance of taxa within a multidimensional trait space based on functional evenness, functional divergence and functional dispersion (Laliberté & Legendre, 2010 ). The recently developed ‘FD’ R package enables one to calculated easily all the aforementioned FD measures (Laliberté & Legendre, 2010 ; Laliberté et al., 2014 ). The use of FD components in the context of BEF in phytoplankton has only started very recently (Abonyi et al., 2018a , b ; Ye et al., 2019 ). Trait-based functional diversity measures in BEF have recently been reviewed by Venail ( 2017 ).

The functional community composition–environment relationship

Functional traits can be classified as those affecting fitness via growth and reproduction (i.e., functional effect traits) and those responding to alterations in the environment (i.e., functional response traits) (Hooper et al., 2002 , 2005 ; Violle et al., 2007 ). Since many ecophysiological traits, such as nutrient and light utilization and grazer resistance, correlate with phytoplankton cell size (Litchman & Klausmeier, 2008 ), size has been recognized as a master trait. Phytoplankton cell size responds to alterations in environmental conditions, like change in water temperature (Zohary et al., 2020 ), and also affects ecosystem functioning (Abonyi et al., 2020 ). The response of freshwater phytoplankton size to water temperature changes seems to be consequent based on both the cell and colony (filament) size (Zohary et al., 2020 ). However, one may consider that cell and colony (filament) sizes are affected by multiple underlying mechanisms, and the choose of cell or colony size as functional trait might be question specific.

The functional group (ecological group) composition of phytoplankton can be predicted well by the local environment (Salmaso et al., 2015 ). However, the different functional approaches have rarely been compared in terms of how they affect the community composition–environment relationship. Kruk et al. ( 2011 ) showed that the morphological group (MBFG) composition of phytoplankton could be predicted from the local environment in a more reliable way than Reynolds’s functional groups (FG), or taxonomic composition. In a broad-scale phytoplankton dataset from Fennoscandia, Abonyi et al. ( 2018a , b ) showed that phytoplankton functional trait categories, as a community matrix, corresponded with the local environment better than Reynolds’s functional groups or the taxonomic matrix. Along the entire length of the Atlantic River Loire, Abonyi et al. ( 2014 ) showed that phytoplankton composition based on Reynolds’s FG classification provided more detailed correspondence to natural- and human-induced changes in environmental conditions than based on the morpho-functional (MFG) and morphological (MBFG) systems.

The aggregation of taxonomic information into functional units reduces data complexity that could come along with reduced ecological information (Abonyi et al., 2018a , b ). Reduced data complexity can be useful as long as it does not imply serious loss of ecological information. Information lost can happen when functional traits are not quantified adequately, cannot be identified, or when ecologically diverse taxa, such as benthic diatoms are considered similar functionally (Wang et al., 2018 ). Otherwise, the aggregation of taxonomic to functional data highlights ecological similarities among taxa (Schippers et al., 2001 ) and should lead to better correspondence between community composition and the environment (Abonyi et al., 2018a , b ).

The functional diversity–ecosystem functioning relationship

Based on taxonomic data, recent studies support a positive biodiversity–ecosystem functioning relationship in phytoplankton clearly (Naeem & Li, 1997 ; Ptacnik et al., 2008 ; Striebel et al., 2009 ). The well-known paradox of Hutchinson asking how so many species may coexist in phytoplankton (Hutchinson, 1961 ) has been reversed to how many species ensure ecosystem functioning (Ptacnik et al., 2010b ). Based on functional traits, however, almost half of the studies reported null or negative relationship between functional diversity and ecosystem functioning (Venail, 2017 ). Recently, Abonyi et al. ( 2018a , b ) argued that functional diversity based on trait categories (i.e., functional trait richness—FTR) and Reynolds’ ecological groups (i.e., functional group richness—FGR) represented different aspects of community organisation in phytoplankton. While both functional measures scaled with taxonomic richness largely, FTR suggested random or uniform occupation of niche space (Díaz & Cabido, 2001 ), while FGR more frequent niche overlaps (Ehrlich & Ehrlich, 1981 ), and therefore, enhanced functional redundancy (Díaz & Cabido, 2001 ). A key future direction will be to understand mechanisms responsible for the co-occurrence of functional units (‘functional groups’) within phytoplankton assemblages, and detail phytoplankton taxa within and among the ecological groups in a trait-based approach. This will enhance our ability to disentangle the ecological role of functional redundancy (within groups) and complementarity (among groups) in affecting ecosystem functioning in the future.

Phytoplankton diversity using molecular tools

The assessment of phytoplankton diversity in waterbodies is strongly dependent from the methods used in the taxonomic identification of species and the quantitative estimation of abundances. The adoption of different methods can strongly influence the number of taxa identified and the level of detail in the taxonomic classifications.

Premise: advantages and weaknesses of light microscopy

Traditionally, phytoplankton microorganisms have been identified using light microscopy (LM). The use of this technique was instrumental to lay the foundation of phytoplankton taxonomy. Many of the most important and well-known species of nano- (2–20 μm), micro- (20–200 μm) and macrophytoplankton (> 200 μm) have been identified by several influential papers and manuals published between the first half of the 1800 s and first half of 1900 s (e.g. (Ehrenberg, 1830 ; de Toni, 1907 ; Geitler & Pascher, 1925 ; Guiry & Guiry, 2019 ). LM is an inexpensive method providing plenty of information on the morphology and size of phytoplankton morphotypes, allowing also obtaining, if evaluated, data on abundances and community structure. Conversely, in addition to being time-consuming, the correct identification of specimens by LM requires a deep knowledge of algal taxonomy. Further, many taxa have overlapping morphological features so that the number of diacritical elements often is not enough to discriminate with certainty different species (Krienitz & Bock, 2012 ; Whitton & Potts, 2012 ; Wilmotte et al., 2017 ). The identification can be further complicated by the plasticity that characterise a number of phenotypic characteristics and their dependence from environmental conditions (Komárek & Komárková, 2003 ; Morabito et al., 2007 ; Hodoki et al., 2013 ; Soares et al., 2013 ). The adoption of electron microscopy for the study of ultra-structural details has represented an important step in the characterization of critical species (e.g. Komárek & Albertano, 1994 ) and phyla. For example, in the case of diatoms, scanning electron microscopy had a huge impact on diatom taxonomy, making traditional LM insufficient for the recognition of newly created taxa (Morales et al., 2001 ). Since aquatic samples usually contain many small, rare and cryptic species, a precise assessment of the current biodiversity is unbearable with the only use of classic LM (Lee et al., 2014 ) and electron microscopy. Nonetheless, despite its limitations, the analysis of phytoplankton by LM still continues to be the principal approach used in the monitoring of the ecological quality of waters (Hötzel & Croome, 1999 ; Lyche Solheim et al., 2014 ).

Culture-dependent approaches—classical genetic characterization of strains

Owing to the above limitations, the identification of phytoplankton species by LM has been complemented by the adoption of genetic methods. These methods are based on the isolation of single strains, their cultivation under controlled conditions, and their characterization by polymerase chain reaction (PCR) and sequencing of specific DNA markers able to discriminate among genera and species, and sometimes also between different genotypes of a same species (Wilson et al., 2000 ; D’Alelio et al., 2013 ; Capelli et al., 2017 ). After sequencing, the DNA amplicons obtained by PCR can be compared with the sequences deposited in molecular databases, e.g. those included in the International Nucleotide Sequence Database Collaboration (INSDC: DDBJ, ENA, GenBank) using dedicated tools, such as BLAST queries (Johnson et al., 2008 ). Further, the new sequences can be analysed, together with different homologous sequences, to better characterize the phylogenetic position and taxonomy of the analysed taxa in specific clades (Rajaniemi et al., 2005 ; Krienitz & Bock, 2012 ; Komárek et al., 2014 ). The phylogenetic analyses provide essential information also for evaluating the geographical distribution of species (Dyble et al., 2002 ; Capelli et al., 2017 ) and their colonization patterns (Gugger et al., 2005 ), to infer physiological traits (Bruggeman, 2011 ), and to evaluate relationships between phylogeny and sensitivity to anthropogenic stressors in freshwater phytoplankton (Larras et al., 2014 ). The selection of primers and markers, and their specificity to target precise algal groups is an essential step, which strictly depends on the objectives of investigations and availability of designated databases. For example, though 16S and 18S rRNA genes are the most represented in the INSDC databases, dedicated archives have been curated for the blast and/or phylogenetic analyses of cyanobacteria (e.g. Ribosomal Database Project; Quast et al., 2013 ; Cole et al., 2014 ) and eukaryotes (e.g. Quast et al., 2013 ; Rimet et al., 2019 ). Further, an increase in the sensitivity of the taxonomic identification based on DNA markers can be obtained through the concurrent analysis of multiple genes using Multilocus Sequence Typing (MLST) and Multilocus Sequence Analysis (MLSA) (see Wilmotte et al., 2017 , for details).

A potential issue with the single use of only microscopy or genetic methods is due to the existence of genetically almost identical different morphotypes and to the development of uncommon morphological characteristics in strains cultivated and maintained in controlled culture conditions. To solve these problems, a polyphasic approach has been proposed, which makes use of a set of complementary methods, based besides genetics, on the analysis of phenotypic traits, physiology, ecology, metabolomics and other characters relevant for the identification of species of different phyla (Vandamme et al., 1996 ; Komárek, 2016 ; Salmaso et al., 2017 ; Wilmotte et al., 2017 ).

Considering the existence of different genotypes within a single species (D’Alelio et al., 2011 ; Yarza et al., 2014 ), the genetic characterizations of phytoplankters have to be performed at the level of single strain. Excluding single cell sequencing analyses (see below), the methods have to be therefore applied to isolated and cultivated strains. This represents a huge limitation for the assessment of biodiversity, because the analyses are necessarily circumscribed only to the cultivable organisms. The rarest and the smaller ones are equally lost. Further, the genetic and/or the polyphasic approaches are time-consuming, allowing to process only one species at a time. To solve this limitation, a set of culture-independent approaches to assess biodiversity in environmental samples have been developed since the 1980s.

Culture independent approaches—traditional methods

A consistent number of molecular typing methods based on gel electrophoresis and a variety of other approaches (e.g. quantitative PCR-qPCR) have been applied since the 1980 s and 1990 s in the analysis of microbial DNA, including “phytoplankton” (for a review, see Wilmotte et al., 2017 ). These approaches are tuned to target common regions of the whole genomic DNA extracted from water samples or other substrata, providing information on the existence of specific taxonomic and toxins encoding genes (Campo et al., 2013 ; Capelli et al., 2018 ), and the taxonomic composition of the algal community without the need to isolate and cultivate individual strains. In this latter group of methods, probably one of the most used in phytoplankton ecology is the denaturing gradient gel electrophoresis (DGGE; (Strathdee & Free, 2013 ). Taking advantage of the differences in melting behaviours of double-stranded DNA in a polyacrylamide gel with a linear gradient of denaturants, DGGE allows the differential separation of DNA fragments of the same length and different nucleotide sequences (Jasser et al., 2017 ). This technique is able to discriminate differences in single-nucleotide polymorphisms without the need for DNA sequencing, providing information at level of species and genotypes. For example, analysing samples from eight lakes of different trophic status, Li et al. ( 2009 ) identified complex community fingerprints in both planktic eukaryotes (up to 52 18S rDNA bands) and prokaryotes (up to 59 16S rDNA bands). If coupled with the analyses of excised DNA bands (Callieri et al., 2007 ), or with markers composed of cyanobacterial clone libraries (Tijdens et al., 2008 ), DGGE can provide powerful indications on the diversity and taxonomic composition of phytoplankton. More recent examples of the application of this technique to phytoplankton and eukaryotic plankton are given in Dong et al. ( 2016 ), Batista & Giani ( 2019 ). A recent comparison of DGGE with other fingerprint methods (Terminal restriction fragment length polymorphism, TRFLP) was contributed by Zhang et al. ( 2018 ).

A second method that has been used in the characterization of phytoplankton from microbial DNA is fluorescence in situ hybridization (FISH), and catalysed reporter deposition (CARD)-FISH (Kubota, 2013 ). In freshwater investigations, this technique has been used especially in the evaluation of prokaryotic communities (Ramm et al., 2012 ). A third method deserving mention is cloning and sequencing (Kong et al., 2017 ).

In principle, compared to LM and traditional genetic methods, these techniques can provide an extended view of freshwater biodiversity. Nevertheless, they suffer from several limitations, due to the time, costs and expertise required for the analysis, and the incomplete characterization of biodiversity due to manifest restrictions in the methods (e.g. finite resolution of gel bands in DGGE and number and sensitivity of markers to be used in CARD-FISH). Part of these limits have been solved with the adoption of new generation methods based on the analysis of environmental and microbial DNA.

Culture independent approaches—metagenomics

The more modern methods boost the sequencing approach over the traditional constraints, allowing obtaining, without gel-based methods or cloning, hundreds of thousands of DNA sequences from environmental samples using high throughput sequencing (HTS). Under the umbrella of metagenomics, we can include a broad number of specialized techniques focused on the study of uncultured microorganisms (microbes, protists) as well as plants and animals via the tools of modern genomic analysis (Chen & Pachter, 2005 ; Fujii et al., 2019 ). The methods based on HTS analysis of microbial DNA can be classified under two broad categories, i.e. studies performing massive PCR amplification of certain genes of taxonomic or functional interest, e.g. 16S and 18S rRNA (marker gene amplification metagenomics), and the sequence-based analysis of the whole microbial genomes extracted from environmental samples (full shotgun metagenomics) (Handelsman, 2009 ; Xia et al., 2011 ). While full shotgun metagenomics techniques were used in the first global investigations of marine biodiversity (Venter et al., 2004 ; Rusch et al., 2007 ; Bork et al., 2015 ), the use of marker gene amplification metagenomics in the study of freshwater phytoplankton has shown an impressive increase in the last decade. The reasons are still due to the minor costs (a few tens of euros per sample) and the simpler bioinformatic tractability of sequences of specific genes compared to full shotgun metagenomics.

The large progress and knowledge obtained in the study of microbial communities (Bacteria and Archaea) based on the analysis of the 16S rDNA marker in the more disparate terrestrial, aquatic and host-organisms’ habitats (e.g. gut microbial communities) had a strong influence in directing the type of investigations undertaken in freshwater environments. At present, the majority of the investigations in freshwater habitats are focused on the identification of microbial (including cyanobacteria) communities, with a minority of studies focused on the photosynthetic and mixotrophic protists (phytoplankton) evaluated through deep sequencing of the 18S rDNA marker (e.g. (Mäki et al., 2017 ; Li & Morgan-Kiss, 2019 ; Salmaso et al., 2020 ).

The results obtained from the applications of HTS to freshwater samples are impressive and are unveiling a degree of diversity in biological communities previously unimaginable, including a significant presence of the new group of non-photosynthetic cyanobacteria (Shih et al., 2013 , 2017 ; Salmaso et al., 2018 ; Monchamp et al., 2019 ; Salmaso, 2019 ). Nonetheless, the application of these techniques is not free from difficulties, due to (among the others) the semiquantitative nature of data, the short DNA reads obtained by the most common HTS techniques, the variability in the copy number per cell of the most common taxonomic markers used (i.e. 16S and 18S rDNA), the incompleteness of genetic databases, which are still fed by information obtained by the isolation and cultivation approaches (Gołębiewski & Tretyn, 2020 ; Salmaso et al., 2020 ). Despite these constraints, the use of HTS techniques in the study of phytoplankton, which is just at the beginning, is contributing to revolutionize the approach we are using in the assessment of aquatic biodiversity in freshwater environments, opening the way to a next generation of investigations in phytoplankton ecology and a new improved understanding of plankton ecology.


In this study, we reviewed various aspects of phytoplankton diversity, including definitions and measures, mechanisms maintaining diversity, its dependence on productivity, habitat size and temperature, functional diversity in the context of ecosystem functioning, and molecular diversity.

Phytoplankton diversity cannot be explained without the understanding of mechanisms that shape assemblages. We highlighted how Vellend’s framework on community assembly (speciation, selection, drift, dispersal) could be applied to phytoplankton assemblages. Competition theories and non-equilibrium approaches fitted also well into this framework.

The available literature on phytoplankton species–area relationship contains information on isolated habitats. These studies argue that richness depends on habitat size. However, findings on eutrophic shallow water bodies suggest that habitat diversity can modify the monotonous increasing tendencies and hump-shaped relationship might occur. The literature on lake’s phytoplankton productivity–diversity relationship supports trends reported for terrestrial ecosystems, i.e. a humped shape relationship at local scale if a sufficiently large productivity range is considered. However, the shapes of the curves depend also on the types of the water bodies. In rivers, both monotonic increasing (rhithral rivers) and decreasing (potamal rivers) trends could be observed.

The aggregation of phytoplankton taxonomic data based on functional information reduces data complexity largely. The reduced biological information could come along with ecological information loss, e.g. when traits cannot be quantified adequately, or, when ecologically diverse taxa are considered similar functionally. Since pelagic phytoplankton is relatively similar functionally, the aggregation of taxonomic into functional data can highlight ecological similarities among taxa in a meaningful way. Accordingly, functional composition and diversity may help better relate phytoplankton communities to their environment and predict the effects of community changes on ecosystem functioning.

The adoption of a new generation of techniques based on the massive sequencing of selected DNA markers and planktonic genomes is beginning to change our present perception of phytoplankton diversity. Moreover, being “all-inclusive” techniques, HTS are contributing to change also the traditional concept of “phytoplankton”, providing a whole picture not only of the traditional phytoplankton groups, but of the whole microbial (including cyanobacteria) and protist (including phytoplankton) communities. The new molecular tools not only help species identification and unravel cryptic diversity, but provide information on the genetic variability of species that determine their metabolic range and unique physiological properties. These, basically influence speciation and species performances in terms of biotic interactions or colonisation success, and thus affect species assembly.

Overexploitation of ecosystems and habitat destructions coupled with global warming resulted in huge species loss on Earth. The rate of diversity loss is so high that scientists agree that the Earth’s biota entered the sixth mass extinction (Ceballos et al., 2015 ). While population shrinkage or extinction of a macroscopic animal receive large media interest (writing this sentence we have the news that the Chinese paddlefish/ Psephurus gladius/ declared extinct), extinction rate of poorly known taxa can be much higher (Régnier et al., 2015 ). Phytoplankton, invertebrates and microscopic organisms belongs to groups where extinctions do occur, but the rate of extinctions cannot be assessed. Worldwide, thousands of phytoplankton samples are investigated every day, mostly for water quality monitoring purposes. However, assessment methods focus on the identification of the dominant and subdominant taxa, because these determine mostly the values of quality metrics. Since species richness or abundance-based diversity metrics are not considered as good quality indicators (Carvallho et al., 2013 ), investigators are not forced to reveal the overall species richness of the samples. To give an accurate prediction for the species richness of a water body, an extensive sampling strategy and the use of species estimators would be required. Nevertheless, high local species richness does not necessarily mean good ecosystem health and high nature conservation value; e.g. if weak selection couples with high number of new invaders. Small water bodies with low local alpha diversity but with unique microflora can have high conservation value (Bolgovics et al., 2019 ). Preservation of large phytoplankton species diversity at the landscape or higher geographic level needs to maintain high beta diversity by the protection of unique habitats (Noss, 1983 ). Because of the multiple human impacts and global warming, small water bodies belong to the most endangered habitats whose protection is of paramount importance.

Our understanding about phytoplankton diversity has progressed in the recent decades. These were mainly motivated by elucidating mechanisms that drive diversity, and by the emergence of new approaches for analysing relationships between diversity and ecosystem functioning.

Increasing human pressure and global warming-induced latitudinal shifts in climate zones, resulting in hydrological regime shifts with serious implications for aquatic ecosystems including phytoplankton. These timely challenges will also affect near future trends in phytoplankton studies. The sound theoretical principles, together with the new molecular and statistical tools open new perspectives in diversity research, which, may let us hope that the Golden Age of studying phytoplankton diversity lies before us and not behind.

Each study in this special issue of Hydrobiologia is dedicated to the memory of the late Colin S. Reynolds, who made an outstanding contribution to aquatic science, and considered one of the most prominent phytoplankton ecologists of the last three decades. His encyclopedic work, The ecology of phytoplankton (2006) considered by many as the Bible for lake phytoplankton ecology, and serves still as a reference for many recent works. His oeuvre covers a wide range of topics within aquatic ecology, including community assembly, functional approaches, modelling of biomass production, resilience and health of aquatic ecosystems. Reynolds’s contribution to our understanding of diversity maintenance mechanisms is still relevant and served as a basis for shaping our manuscript.

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Open access funding provided by ELKH Centre for Ecological Research. BG was supported by the GINOP-2.3.2-15-2016-00019 project and by the NKFIH OTKA K-132150 Grant. NS was supported by the co-financing of the European Regional Development Fund through the Interreg Alpine Space programme, project Eco-AlpsWater (Innovative Ecological Assessment and Water Management Strategy for the Protection of Ecosystem Services in Alpine Lakes and Rivers - ). AA was supported by the National Research, Development and Innovation Office, Hungary (NKFIH, PD 124681).

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BG wrote ‘Introduction’, ‘Mechanisms affecting diversity’, ‘Diversity measures’, Changes of diversity along environmental scales , ‘Conclusions’ and ‘Outlook’ with substantial contribution from RP. AA, RP wrote ‘The functional diversity–ecosystem functioning relationship in phytoplankton’, NS wrote ‘Phytoplankton diversity using molecular tools’ chapters.

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Borics, G., Abonyi, A., Salmaso, N. et al. Freshwater phytoplankton diversity: models, drivers and implications for ecosystem properties. Hydrobiologia 848 , 53–75 (2021).

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Research Article

Productivity-Diversity Relationships in Lake Plankton Communities

* E-mail: [email protected]

Affiliation Department of Environmental Sciences, University of Helsinki, Helsinki, Finland

Affiliation State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (CAS), Nanjing, China

  • Jenni J. Korhonen, 
  • Jianjun Wang, 
  • Janne Soininen


  • Published: August 5, 2011
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Figure 1

One of the most intriguing environmental gradients connected with variation in diversity is ecosystem productivity. The role of diversity in ecosystems is pivotal, because species richness can be both a cause and a consequence of primary production. However, the mechanisms behind the varying productivity-diversity relationships (PDR) remain poorly understood. Moreover, large-scale studies on PDR across taxa are urgently needed. Here, we examined the relationships between resource supply and phyto-, bacterio-, and zooplankton richness in 100 small boreal lakes. We studied the PDR locally within the drainage systems and regionally across the systems. Second, we studied the relationships between resource availability, species richness, biomass and resource ratio (N∶P) in phytoplankton communities using Structural Equation Modeling (SEM) for testing the multivariate hypothesis of PDR. At the local scale, the PDR showed variable patterns ranging from positive linear and unimodal to negative linear relationships for all planktonic groups. At the regional scale, PDRs were significantly linear and positive for phyto- and zooplankton. Phytoplankton richness and the amount of chlorophyll a showed a positive linear relationship indicating that communities consisting of higher number of species were able to produce higher levels of biomass. According to the SEM, phytoplankton biomass was largely related to resource availability, yet there was a pathway via community richness. Finally, we found that species richness at all trophic levels was correlated with several environmental factors, and was also related to richness at the other trophic levels. This study showed that the PDRs in freshwaters show scale-dependency. We also documented that the PDR complies with the multivariate model showing that plant biomass is not mirroring merely the resource availability, but is also influenced by richness. This highlights the need for conserving diversity in order to maintain ecosystem processes in freshwaters.

Citation: Korhonen JJ, Wang J, Soininen J (2011) Productivity-Diversity Relationships in Lake Plankton Communities. PLoS ONE 6(8): e22041.

Editor: Martin Solan, University of Aberdeen, United Kingdom

Received: April 1, 2011; Accepted: June 14, 2011; Published: August 5, 2011

Copyright: © 2011 Korhonen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This study was supported by grants from University of Helsinki ( , grant no. 2157010) and Academy of Finland ( , grant no. 126718). No additional external funding sources for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


In recent decades, the number of studies examining the factors affecting species richness in ecosystems has greatly increased. This increase results partly from the ongoing global decline in biodiversity caused by humans. In recent years, studies have especially addressed the causes of diversity patterns along specific gradients such as altitude [1] and latitude [2] . Moreover, the current recognition of the pivotal role of diversity in ecosystem functioning and services has enhanced the interest in studies on biodiversity [3] .

One of the most interesting gradients associated with the variation in species diversity is ecosystem productivity. Given the predominant role of productivity for species coexistence, the relationship between productivity and diversity (PDR) has become a fundamental research area in modern ecology (e.g. [4] ). The relationship has direct applications for many central environmental issues, such as biodiversity conservation and ecosystem functions and services. The role of diversity in ecosystems is remarkable, because species richness can be both a cause and a consequence of primary production, i.e. the rate of carbon fixed through photosynthesis [5] , [6] . This dual role of biodiversity is based on two theories. First, the species-energy theory suggests that the amount of resource supply determines the number of coexisting species ( Fig. 1 , [7] ). Second, the studies in the field of biodiversity-ecosystem functioning (BEF) are built on the premise that the species richness controls the biomass production of a community ( Fig. 1 , [8] ). Combined with the resource ratio theory [9] , these theories have also led to formulation of the multivariate hypothesis of PDR [8] .


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a) At small and large scale, the relationships between species richness and nutrient supply are predicted to be unimodal or linear, respectively. b) Biomass production first increases with species richness but saturates at high richness levels. c) The causal relationships between resource availability, species richness, biomass and resource ratio. Figure modified from Cardinale et al (8).

Even though the PDR has been widely examined using experimental approaches and observations, the underlying mechanisms still remain poorly understood. However, one of the most common mechanisms behind the positive PDR is the sampling effect . This mechanism is based on the assumption that more diverse communities are more likely to include species that are especially effective in capturing resources and converting these into plant biomass [10] – [12] . The sampling effect is expected to affect ecosystem functioning especially in the studies spanning short temporal extents (reviewed in [13] ) or in studies conducted in homogenous environments [14] or the landscape [15] . Another mechanism driving the PDR is complementarity , i.e. niche differentiation between the species present in a community. A positive complementarity effect represents the sum of all biological processes involving two or more species, positively influencing a focal process, such as niche partitioning and facilitation [16] . This effect is based on a view that more diverse communities function more efficiently, because ecologically different species that compete for limiting resources are present and species thus complement each other in their resource use [10] , [11] . Niche complementarity is expected to affect productivity in the long-term only as species' differences in resource use typically needs enough time to have functional consequences in the ecosystems [13] .

Both sampling effect and complementarity may cause positive linear PDR, and this type of relationship is, indeed, common in nature. However, reviews suggest that unimodal relationships are also typical especially in plant communities and in aquatic ecosystems [4] , [17] . In unimodal relationships, the number of species peaks at intermediate productivity. The low number of species at low and high ends of productivity gradient can result from small amount of resources and intense competition, respectively [4] . Moreover, positive interspecific interactions (i.e., facilitation ) can explain the coexistence of large number of species at the intermediate productivity [18] .

Besides being affected by biological processes, the shape of the PDR is likely to be driven by the spatial scale of the study. In aquatic ecosystems, unimodal PDR are more common in studies covering small (local) scales, while positive linear relationships tend to dominate in studies covering larger (regional) scales [19] , [20] . The main reason for the scale-dependency is the increase of species dissimilarity with productivity within regions, i.e., more productive lakes or streams have more multiple stable states [20] , [21] . The generality of this scale-dependency in PDR across organisms has, however, remained unresolved, as studies testing the scale-dependency are usually conducted in disparate systems using different methods. The cross-taxonomic group comparisons of PDR are, however, important given that PDR can be mediated by different mechanisms across organism groups that vary in body size, trophic position [22] or dispersal capacity [23] . Some pioneer studies on PDR in phytoplankton communities have been conducted (e.g. [20] , [24] ), but more large-scale studies on PDR across taxa in natural unmanipulated ecosystems are urgently needed. We emphasize also that the PDRs are largely understudied for small organisms such as lake bacteria (but see [25] , [26] ). Bacteria are interesting not only due to their small size and efficient dispersal, but also because they have a unique functional role representing decomposers in nature.

In this study, we first (i) examine the relationships between resource supply and richness of bacterio-, phyto-, and zooplankton in 100 small lakes in Finland. We expect that the patterns in PDR between micro- (bacterio- and phytoplankton) and macroorganisms (zooplankton) may well differ because, being small and often highly abundant, microorganisms may show virtually unrestricted dispersal [27] . According to Pärtel & Zobel [23] , species that show dispersal limitation are likely to show unimodal PDRs if species pool size and the degree of biotic interactions do not vary along productivity while patterns are more likely to be linear for highly dispersive taxa. We examine the PDR at two different scales: (A) locally among 20 lakes sampled within each drainage system, and (B) regionally among the five sampled drainage systems, i.e., across all 100 lakes that were sampled. We thus vary the extent of the study from one to five drainage systems but keep the focus of research the same (one lake). Second (ii), we examine the relationship between phytoplankton species richness and standing biomass and expect that biomass increases with species richness because of enhanced ecosystem functioning ( Fig. 1 , [8] ). Here, we also relate phytoplankton community composition with biomass to see if species composition is related to biomass, suggesting that the productivity is also affected by composition effects [28] . Finally (iii), we test the multivariate hypothesis of PDR suggested by Cardinale et al [8] and study the relationships between resource availability, species richness, biomass and resource ratio (N∶P) in phytoplankton communities using Structural Equation Modeling (see [8] ). Following Cardinale et al [8] , we expect that phytoplankton biomass in lakes is largely determined by resource availability, yet is also driven by phytoplankton richness and resource ratio ( Fig. 1 ).

Materials and Methods

Bacterio-, phyto-, and zooplankton were collected once from 100 small lakes in Finland during July in 2008 and 2009. In case of residential areas (summer cottages), we asked oral permissions from the land owners to take water samples from the nearby lakes. However, most of the study lakes were several kilometers away from the nearest settlements. Therefore, the everyman's right of Finland allowed us to access the lands and lakes as we did not fish, harm or disturb the natural environment.

The sites were sampled at five drainage systems, 20 lakes per system. In 2008, we sampled 60 lakes at three drainage systems and in 2009 40 lakes at two drainage systems. We acknowledge that between-year variation in environmental conditions may increase the residual variation in the data that could not be controlled. However, sampling of 100 lakes during a single summer was not possible due to seasonality and substantial increase of within-year variation in the data. We also acknowledge that a single sampling may not always accurately reflect the true number of species occupying lakes. However, according to Shurin et al [29] , daily richness and annual richness were highly correlated for zooplankton in 36 lakes in a temperate region. We thus think that our sampling design represented among-lake differences in richness relatively well.

The sampled drainage systems were (1) Vantaanjoki, (2) Karjaanjoki, (3) Kokemäenjoki, (4) Upper Kymijoki, and (5) Koutajoki ( Fig. 2 ). These drainage systems were chosen, because they cover a large geographical extent and their nutrient concentrations vary from ultraoligotrophic to highly eutrophic. Latitudinal gradient between the southernmost and the northernmost sites was more than 700 km. We sampled only small lakes and ponds to ensure that plankton sampling covered the site as well as possible. Most of the lakes within the drainage systems were not readily inter-connected to each other via water routes. For more information on the environmental characteristics of the lakes within the drainage systems, please see Table S1 .


The study areas were: 1) Vantaanjoki, 2) Karjaanjoki, 3) Kokemäenjoki, 4) Upper Kymijoki, and 5) Koutajoki. On the right, small maps show geographical positions of each lake in the same study areas.

Sampling and sample processing

Plankton samples were collected from the middle of each lake using a tube sampler (V = 2.3 L) at three locations, which were pooled.We collected the samples in the middle of the lakes in order to avoid benthic taxa from the littoral entering the samples. The samples were collected at 0.5 m below the surface of the water. Our sampling protocol for bacteria followed the method by Longmuir et al [30] . First 250 mL of water was filtered through a 0.42 µm pore-sized nitrocellulose filter (diameter 25 mm, Millipore, Durapore®) to remove larger particles. Bacteria cells were then collected on a 0.22 μm pore-sized nitrocellulose filter, which was frozen immediately in the field. Phytoplankton subsamples were mixed, and a sample of 0.5 L was fixed immediately with acid Lugol's iodine solution in the field. Zooplankton samples (6.15 L in total) were filtered through a 50 μm net and preserved with formaldehyde in the field.

The maximum depth of the lakes as well as surface water temperature was measured. We included surface water temperature as an explaining variable in the data because it showed notable differences among the sampled drainage systems ( Table S1 ). Area of each lake was measured using Geographic Information System. Samples for water chemistry analyses were collected simultaneously with the plankton sampling and analyzed in the laboratory for conductivity, chlorophyll a (Chl a), water colour, total nitrogen, and total phosphorus using national standards. Water colour was determined using a comparator and nutrients using Lachat Quik-Chem 8000.

In the laboratory, the phytoplankton samples were concentrated using an Utermöhl chamber and counted with a light microscope (magnification 400×). For each sample, 50 fields were counted typically detecting 200–500 specimens (individuals or colonies). For zooplankton, all individuals (typically 50–200 individuals per sample) were counted at magnification of 125–400× using an inverted microscope. Both crustacean zooplankton and rotifers were included in countings. We acknowledge that our methodology for zooplankton does not detect as many individuals as it detects for bacteria or phytoplankton because of relatively limited amount of water filtered for the samples. However, as there was great among-lake variability in zooplankton richness, we feel that this methodology is adequate for inter-lake comparisons for zooplankton richness. For phyto- and zooplankton, most individuals were identified to species level. However, some of the taxa (<20%) were identified to genus level only. We thus acknowledge that within the data sets, not all taxa represent a species but rather a genus or even higher taxonomic groups for bacteria. This may mask some structure in the data only observable if species level data were used.

Nucleic acid extraction and polymerase chain reaction (PCR)

For examining the community composition of bacteria, we used standard fingerprinting methods (see details below). Nitrocellulose filters were cut in half and placed into a 1.5 mL microtube which was then dipped in liquid nitrogen. The filters were then roughly ground with a plastic pestle and deoxiribonucleic acid (DNA) was extracted with a protocol of Griffiths et al [31] with the following modifications: 0.6 mL of extraction buffer and zirconium beads (Qiagen) were added to the ground filters in 2 mL tubes and mixed by vortexing. Once all the samples contained the extraction buffer, 0.6 mL of buffered (pH 8) Phenol∶Chloroform∶Isoamyl alcohol was added to each tube and vortexed again. Mechanical lysis was performed on a bead-beating device for 120 seconds at maximum speed (1800 rounds per minute). DNA was finally resuspended in 20 μL Tris Ethylenediaminetetraacetic (EDTA) acid buffer (10 mmol −L Tris 1 mmol −1 EDTA).

As a molecular fingerprinting method, we used terminal restriction fragment length polymorphism (tRFLP) analysis [32] . It is a popular method for generating a fingerprint of an unknown microbial community. Although it may underestimate the true number of bacteria taxa present, our consistent methods allow us to investigate the distribution patterns of bacteria among the lakes. For the tRFLP analysis, PCR amplification of 16S ribosomal genes for tRFLP was achieved by using primers FAM-E8F (FAM- 5′-AGAGTTTGATCCTGGCTCAG-3′ ) and E939R ( 5′-CTTGTGCGGGCCCCCGTCAATTC-3′ ) [33] with reaction conditions optimized for the enzyme DyNAzyme II (Finnzymes). PCRs were run in triplicate reactions, aliquots were checked by agarose gel electrophoresis separately and the rest of the volume was pooled. The pools were purified with a Millipore Multiscreen plate. The clean PCR products were digested with 5 units of restriction enzyme (HhaI, Fermentas) for 18 hours in duplicate reactions. Dilutions of the digested and undigested samples were run on an Applied Biosystems (ABI) 3130xl device at 60°C. The resulting peak profiles (taxonomic units) were analyzed using the ABI PeakScanner software. All peaks with a size of 50–940 base pairs (bp) and a relative height of at least 0.1% above the baseline present in both digestions were manually recorded for each sample and compared to profiles from undigested PCR products. The peaks that located closer than 2 bp from each other were binned. We used the limit of 2 bp for all fragment sizes.

Statistical analyses

The degree of saturation in local communities was assessed using species-accumulation curves across sampled sites in each drainage system. We used the freely available software package Ecosim 7.0. ( ). The procedure was done to ensure that 20 sampled lakes covered the regional species pool sufficiently, e.g. included more than 70% of the species. Our data showed that a sample of 20 sites per region is likely to be adequate, as the curves seem steadily approach the asymptotes ().

The relationship between species richness and nutrient supply was analyzed using linear and quadratic regression with AIC (Akaike's Information Criterion) to select the best model. The relationships were analyzed at two spatial scales: within and across the drainage systems.

Moreover, we used regression analysis to test the relationship between phytoplankton species richness and biomass. Analyses were done using SPSS 15.0 (SPSS, Inc.). Besides using observed richness values, we conducted analyses with richness values modified using Chao1 formula [34] , [35] , which should be useful for small organisms with highly skewed rank frequency distributions. However, the results were qualitatively highly similar with the observed species richness data (results not shown). Therefore, we used observed species richness as an indicator for diversity. All analyses were conducted using both non-transformed richness values and log-transformed values. As the main patterns were qualitatively similar, we show here the results for non-transformed richness values (except in Fig. 3 ). We also studied if phytoplankton community composition was related to phytoplankton biomass. This was done by regressing site NMDS (Non-Metric Multidimensional Scaling, [36] ) 1 scores against phytoplankton biomass of a site. NMDS analysis was conducted using presence-absence data of the phytoplankton species with the R package 2.8. ( ).


The relationships between resource ratio (N∶P), resource availability, species richness and phytoplankton biomass were examined using Structural Equation Modeling (SEM, [37] ). We used SEM analysis only for the phytoplankton data as we did not have biomass measures for bacteria or zooplankton. SEM is an extension of GLM (General Linear Model) in which a set of regressions is solved simultaneously to examine whether a covariance matrix complies with a set of causal pathways set a priori . Total N and total P values were standardized to have a mean of zero and standard deviation of 1. The resource availability (a) and resource ratios (θ) were calculated using resource vectors from the two resource values (total N and total P) according to equations 2 and 4 in Cardinale et al [8] . This was done to separate the resource availability from resource imbalance between N and P. Resource ratio (θ) ranges from 0–90 with 0 meaning perfect balance, and 90 perfect imbalance, relative to the total variation among the sampled lakes. The goodness of fit of the full model was tested using Chi-square test. Chi-square with non-significance test indicates that there is no deviation between the observed covariance matrix and that predicted by SEM. Akaike's Information Criterion (AIC) was used to select the most parsimonious model. Using AIC, the final model was chosen based on the likelihood (AIC L ) that the model was the best fit to current data set among the candidate models. We also conducted a full path model without model selection to show all related individual pathways. SEM was conducted in Amos 18.0 (SPSS, Inc.).

Finally, we studied which environmental, geographical or biological factors were strongest determinants of species richness for each planktonic group. We calculated the relationship between species richness and water chemistry (total P, total N, color, conductivity), water temperature, surface area, maximum depth and geographical location (latitude and longitude) of the lake using GLM with the best model selection by AIC. As the PDR is frequently unimodal, we also included the second order terms of total P and total N in the candidate models. The cross-taxon concordance between zooplankton, phytoplankton and bacterioplankton richness was analyzed including richness values into GLM models as well as with the separate correlation analyses. Analyses were conducted using R package 2.8. ( ).

At within-drainage system scale, the PDR showed highly variable patterns in all organism groups ranging from positive linear and unimodal relationships with total P to negative linear relationships in some of the drainage systems. In zooplankton, the PDR was significantly unimodal only in the Koutajoki drainage system ( Table 1 , Figure S2 ). In the four other drainage systems, the PDR varied from positive linear to slightly negative linear but none of the relationships was significant ( Table 1 ). In phytoplankton, two out of five drainage systems (Vantaanjoki and Upper Kymijoki) showed a significant PDR with positive linear and unimodal relationships, respectively ( Table 1 , Figure S2 ). Bacterioplankton richness and total P were unimodally related only in the Karjaanjoki drainage system ( Table 1 ). All other relationships were non-significant.


Across regions comprising all 100 lakes that were sampled, there were significant linear relationships between log-transformed phytoplankton and zooplankton species richness and total P (R 2  = 0.237; P = 0.001, R 2  = 0.067, P = 0.009, respectively; Fig. 3a, b ). Bacterioplankton richness did not show significant relationship with total P (R 2  = 0.002 for the linear model ; P = n.s.; Fig. 3c ). Relationships were slightly weaker, yet significant, for phyto- and zooplankton when non-transformed data were used (results not shown). Given that we found linear relationships across drainage systems covering the larger study scale, but variable patterns within the drainage systems, these results give overall partial support for the scale-dependency of the PDR in our study system.

Phytoplankton richness and the amount of chlorophyll a (µg/l) showed a positive linear relationship across the whole set of lakes (R 2  = 0.0.068, P = 0.009; Fig. 4 ). This may indicate that the communities consisting of higher number of species were able to produce higher levels of biomass from basal resources. However, we acknowledge that the relationship can also be caused by the increasing number of species (and chlorophyll a), thus leading to a more species rich community. It also seems that community composition has either direct or indirect effects on standing biomass, as community composition (summarized by NMDS 1 scores) was related to phytoplankton biomass (R 2  = 0.121, P<0.001; Fig. 4 ).


In the SEM analysis for phytoplankton data, the assumptions concerning linear relationships were fulfilled (linearity of relationships, one-way causal flow, and variables measured on an interval or ratio scale). The chi-squared test indicated that there was no significant deviation between the observed covariance matrix and that predicted by the proposed SEM (Chi-square = 0.725, df = 2, P = n.s.). According to the best SEM model identified by AIC, phytoplankton biomass was largely related to resource availability (coefficient = 0.58), yet there was also a pathway via community richness (coefficient = 0.22) ( Table S2 , Fig. 5a ). Surprisingly, there were no significant effects of resource availability on richness and resource ratio on richness in this model. However, full path model without model selection ( Fig. 5b ) showed a significant effect of resource availability on richness (coefficient = 0.10). In both models, the correlation between the resource supply and resource ratio was −0.44. Resource ratio and phytoplankton biomass were positively related. Overall, the best multivariate model explained 32% of the variation in phytoplankton biomass.


SEM was conducted to test whether covariance among variables collected from 100 lakes could be produced by a covariance matrix set a priori (shown in Fig. 1c ). The coefficients next to arrows represent the standard deviation change between variables. R 2 values indicate the amount of explained variation in species richness and phytoplankton biomass. The correlation between the resource supply and resource ratio (R) was −0.44. Dashed lines denote non-significant relationships.

As planktonic richness was not only determined by ecosystem productivity, we studied whether it was related to some other physicochemical factors, location of the lake or richness of the trophic levels other than the focal planktonic group. The most parsimonious model for the whole zooplankton data included three variables (water temperature, bacterioplankton and phytoplankton richness), which were all positively correlated with the zooplankton richness ( Table 2 ). The three variables jointly explained 21% of the variability in zooplankton richness. For the phytoplankton data set, the best model included five factors ( Table 2 ). Electrical conductivity, longitude, total N, and zooplankton richness showed positive relationships with phytoplankton richness, while latitude and phytoplankton richness were negatively correlated ( Table 2 ). The five variables jointly explained 48% of the variation in phytoplankton richness. Variation in bacterioplankton richness, in turn, was mainly related to geographical position of the lake and zooplankton richness. Longitude was negatively correlated, while latitude and zooplankton richness showed positive correlations with bacterial richness ( Table 2 ). The three variables jointly explained 15% of the variation in bacterioplankton richness.


Finally, we conducted separate correlation analyses to test the cross-taxon concordance between the three organism groups. We found that zooplankton richness was significantly correlated with both phytoplankton and bacterioplankton richness (R = 0.231; P = 0.019 and R = 0.274; P = 0.006, respectively) ( Figure S3 ). However, phytoplankton and bacterioplankton richness were not correlated (R = 0.022, P = n.s.).

The taxonomic details of the community compositions observed in this study can be found in [38] .

Lakes are largely underutilized, but highly useful for studying the PDR, as they are bounded ecosystems embedded in a terrestrial matrix and enumeration of locally coexisting species is thus relatively reliable. For example, earlier works by Dodson et al [39] , Hessen et al [40] and Ptacnik et al [24] have shown that there may be predictable large-scale patterns in plankton richness mediated by productivity. The PDR is often studied using small-scale field experiments, or studies are conducted in laboratory microcosms [11] , [41] , [42] . However, species diversity may have an even higher effect on productivity in natural unmanipulated systems than in artificial ecosystems [43] . The caveats for experimental studies can include small spatial scale, small temporal extent of the study, the lack of natural disturbances and unnatural species compositions that do not exist in nature [28] , [44] – [46] . Therefore, large-scale observational studies are also fundamental to long-term biodiversity inventories and conservation programs [47] . Our study is, to our knowledge, the first study that examines PDR at large scales and that also considers microbial organisms including lake bacteria.

Regardless of the great potential of lakes for studying PDR, our survey showed large variability in the PDR among the five drainage systems for all planktonic groups. We initially predicted that the relationship between species richness and productivity would be unimodal at a small scale and positive linear at a larger scale ( Fig. 1a , [19] , [20] ). At the small scale, the observed relationships varied, however, from non-significant and negative linear to significant positive linear and unimodal [see also 25] , [48] . This is in line with Witman et al [48] who also found variable PDRs in Arctic macrobenthos indicating that PDRs may often be highly context dependent.

At the larger scale instead, we found that two out of three PDRs were significant and positive linear as we expected. Therefore, our hypothesis on scale-dependency in PDR was partly supported. The reason for the lack of clear relationships within drainage-systems remain speculative at present but may be related to facts that (i) planktonic organisms were overall largely driven by some other factors than productivity and (ii) productivity gradients were not long enough for producing a possible “hump-shaped” PDR in these unmanipulated systems. However, we would like to emphasize that the study by Chase & Leibold [20] was conducted in much smaller spatial extent than our study as they compared PDR within single pond with PDR among multiple ponds sampled in one drainage system only (versus our 20 lakes sampled in five drainage systems). They collected samples twice per year, over two years. Therefore, their findings are not fully comparable to our results because of substantially larger spatial scale in the present study and different amounts of sampling occasions.

Besides studying the scale-dependency of the PDR, one of our main goals was to investigate if increasing species richness is related to higher levels of phytoplankton biomass. Traditionally, biomass is expected to be driven by nutrient availability (e.g. [49] ). However, recent studies have viewed the PDR from a different angle, asking how species richness can control biomass production instead of only responding to it [8] . We found that species-rich communities also maintained higher biomass than communities consisting of fewer species. Our results thus seem to agree with Ptacnik et al. [50] who found that resource use efficiency (RUE, calculated as a ratio between chl a and total P) and phytoplankton richness were positively correlated. We would like to emphasize, though, that in our data, phytoplankton biomass was slightly more strongly related to community composition summarized by the NMDS 1 scores of sites than to the pure species number at each site. This may indicate that composition effects are nonetheless stronger than pure richness effects in our study system. This is in line with Downing & Leibold [28] who observed that species composition within richness levels can have equal or more marked effects on functions than average effects of richness in pond ecosystems. We admit, though, that teasing apart the richness effect from the composition effect in our study is not clear-cut because of field observations only - one would need carefully replicated experiments in the field to examine this more closely. For example, it is likely that the composition effect is mediated by changes in resource availability, and resource availability seems strongly affect the amount of biomass in our study system ( Fig. 5 ).

As multiple ecosystem processes may act simultaneously, we studied the concomitant pathways between richness, resource availability, resource ratio and biomass for the phytoplankton communities and formally tested the multivariate hypothesis of the PDR introduced by Cardinale et al [8] . The results of the SEM analysis showed a strong pathway between resource availability and biomass, thus agreeing with the results of Cardinale et al. [8] . It should be noted, however, that biomass is not determined by resource availability only, as there was a positive link between richness and biomass. This finding suggests that richness is related to more efficient ecosystem production. Cardinale et al [8] also proposed that as resources become increasingly imbalanced, biomass production slows down. However, we could not detect such a negative effect of resource imbalance on standing biomass. Rather, our data showed a positive, albeit relatively weak effect of resource ratio on biomass. Moreover, we did not find a strong pathway between resource availability and species richness. This counterintuitive result is in line with e.g. Longmuir et al [30] , and Dodson et al [39] who did not detect clear relationships between resource availability and species richness. Altogether, we could explain quite reasonable proportion (32%) of phytoplankton biomass using resource availability, resource ratio and phytoplankton richness alone and thus conclude that our data partly support the multivariate hypothesis by Cardinale et al [8] .

As productivity alone could nonetheless explain only a relatively small portion of the variability in species richness for all three planktonic groups, we studied whether species richness was correlated with some other factors. In general, it seemed that factors related to productivity were not often incorporated into the best regression models. For zooplankton, water temperature was positively correlated with species richness. We speculate that the positive relationship between richness and temperature may stem from higher energy-input supporting more species as predicted of the species-energy theory [2] , [51] . Zooplankton results further suggest that there is concordance in richness between different trophic levels as both phytoplankton and bacterioplankton richness were included in the best GLM model for zooplankton. As bacterioplankton and zooplankton richness were positively related, this means that the positive feedbacks between trophic levels can maintain species diversity in these communities. Positive correlations in richness between the trophic levels have been found in several studies of terrestrial systems [52] – [54] , but in aquatic ecosystems correlations in richness across trophic levels have been weak or non-significant [30] , [55] , [56] . Due to these disparate results, it has been suggested that the degree of concordance in species richness patterns among trophic levels generally differ between terrestrial and aquatic systems [30] . In our study, the major environmental factors affecting species richness were different for each trophic level. One may suggest that that the similar accumulation of species across trophic levels may be driven by species interactions between trophic levels in the planktonic food web rather than similar responses to environmental gradients. However, the possible cross-taxon concordance remains speculative as we did not study but the lowest levels of food web of the lakes.

As our study organisms ranged from unicellular bacteria to visible meiofauna, we initially expected notable differences in richness patterns between the organism groups. Traditionally, microscopic organisms are expected to be unlimited in their dispersal ability due to their small size and high abundance (e.g. [27] ) and thus lack any notable biogeographical patterns. Cross-taxon studies that include bacteria and examine large-scale patterns in biodiversity are still very rare. Our data seem to disagree with the theory of ubiquity of microorganisms, as both phytoplankton and bacterioplankton richness were significantly related to geographical location of the lakes, and surprisingly, zooplankton richness seemed to be the most weakly related to sampling location. Although the location of a lake always includes a signal of unmeasured environmental variables, our results seem to agree with Hillebrand et al [57] , Soininen et al [58] , and Heino et al [59] on microorganisms having restricted biogeographical distributions perhaps similar to the patterns observed for macroorganisms.

Although we could explain a considerable portion of the variation in species richness using multiple abiotic and biotic factors, some important aspects concerning the PDR remain speculative. For instance, we were not able to estimate the importance of colonization or extinction on patterns in the PDR in our study regions due to static snapshot sampling for each site. We were also unable to assess fish diversity and abundance in the lakes, although predation can reduce interspecific competition and thus promote species coexistence of the zooplankton, for example [40] . Further, other vital factors, such as disturbances [60] – [62] , chemical and thermal variability [63] , evolutionary history [64] , or the history of community assembly [65] , can also influence the patterns in species richness. The importance of different mechanisms behind the PDR (e.g. sampling effect and complementarity) also remain equivocal as the independent effects of these factors cannot be reliably identified using a large-scale field data only. We thus encourage ecologists to further study these factors more thoroughly in aquatic environments.

To conclude, we found that the PDRs are variable in plankton communities of small boreal lakes. These data showed unimodal and linear PDRs at local scale, yet also including many non-significant PDRs. At the regional scale in turn, we found linear PDRs for phyto- and zooplankton and conclude thus that PDR may vary with spatial scale. Our GLM analyses further suggested that there are correlations in richness across trophic levels in freshwater plankton. The concordance likely results from species interactions between the trophic levels as there were no common responses to measured environmental gradients. Finally, we found that both resource availability and species richness contributed to biomass production in phytoplankton.Our study thus emphasizes the need for conserving diversity in order to maintain ecosystem processes in freshwaters.

Supporting Information

Environmental variables. Means and ranges for the main environmental variables for each drainage system.

The results of Structural Equation Modeling. The individual pathways in the model with (A.) and without (B.) best model selection. The significance is indicated by the P value. Resource availability and resource ratio were significantly related to biomass.

Accumulation curves. Species accumulation curves for a–b) zooplankton, c–d) phytoplankton, e–f) bacterioplankton data sets. The left column indicates the accumulation of species in the most species-rich areas and the right column shows the accumulation curves in the most species-poor areas.

The relationships between species richness and total P. The relationships between local species richness and total P (µg/l) in zooplankton (a–e), phytoplankton (f–j), and bacterioplankton (k–o) for data sets at five drainage systems each consisting of 20 lakes. Solid lines indicate significant relationships between species richness and total P. Dashed lines denote non-significant relationships. Linear or quadratic model was used depending on the AIC value (see Table 1 ).

Cross-taxon concordance. Concordance between observed richness for a) zooplankton and phytoplankton (P = 0.019), b) bacterioplankton and phytoplankton (P = n.s.), and c) bacterioplankton and zooplankton (P = 0.006).


We thank Jani Heino, Helmut Hillebrand, and Jan Weckström for constructive comments on the manuscript. We are also grateful for the anonymous reviewers for their fruitful comments.

Author Contributions

Conceived and designed the experiments: JJK JS. Performed the experiments: JJK JS. Analyzed the data: JJK JW. Contributed reagents/materials/analysis tools: JJK. Wrote the paper: JJK JS.

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  • Published: 21 December 2021

Zooplankton diversity monitoring strategy for the urban coastal region using metabarcoding analysis

  • Chi-une Song 1 ,
  • Hyeongwoo Choi 1 ,
  • Min-Seung Jeon 1 ,
  • Eun-Jeong Kim 1 ,
  • Hyeon Gyeong Jeong 2 ,
  • Sung Kim 3 ,
  • Choong-gon Kim 3 ,
  • Hyenjung Hwang 3 ,
  • Dayu Wiyati Purnaningtyas 3 , 4 ,
  • Seok Lee 3 ,
  • Seong-il Eyun 1 &
  • Youn-Ho Lee 3  

Scientific Reports volume  11 , Article number:  24339 ( 2021 ) Cite this article

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  • Environmental sciences
  • Ocean sciences

Marine ecosystems in urban coastal areas are exposed to many risks due to human activity. Thus, long-term and continuous monitoring of zooplankton diversity is necessary. High-throughput DNA metabarcoding has gained recognition as an efficient and highly sensitive approach to accurately describing the species diversity of marine zooplankton assemblages. In this study, we collected 30 zooplankton samples at about 2-week intervals for 1 year. Zooplankton diversity showing a typical four season pattern. Of the “total” and “common” zooplankton, we assigned 267 and 64 taxa. The cluster structure and seasonal diversity pattern were rough when only the “common” zooplankton was used. Our study examined how to maximize the benefits of metabarcoding for monitoring zooplankton diversity in urban coastal areas. The results suggest that to take full advantage of metabarcoding when monitoring a zooplankton community, it is necessary to carefully investigate potential ecosystem threats (non-indigenous species) through sufficient curation rather than disregarding low-abundance operational taxonomic units.


Zooplankton play a key role in marine biodiversity and thus have critical impacts on marine ecosystem processes 1 , 2 , 3 , 4 . These animals are integral to the functioning of aquatic food webs because they constitute the major link for energy transfer between phytoplankton, the primary producers, to higher species and further to predators, such as commercially important fish larvae 5 , 6 , 7 . Hence, information on zooplankton communities and diversity is an important aspect of understanding marine ecosystems. Most of the fluctuations in zooplankton communities are caused by environmental factor changes and the relatively short life-cycle of zooplankton (from a few months to 1 year). Therefore, sampling at 2-week to 1-month intervals can be sufficient to track the changes in the marine environment’s seasonal and interannual conditions both directly and indirectly 8 .

Metabarcoding has revolutionized biomonitoring in marine and freshwater ecosystems 4 , 9 . Not only has metabarcoding allowed researchers to examine the relationship between environmental changes and aquatic communities in benthic environments 10 , 11 , but it is also a highly effective approach for large-scale biodiversity assessment, large-scale community structure, and diversity analysis of zooplankton in the oceanic zones of the Pacific Ocean and the Arctic Ocean 6 . Research using metabarcoding can help overcome some of the weaknesses of traditional analysis. Metabarcoding analysis has high-throughput sequencing sensitivity and can discriminate cryptic species and rare species with low abundances such as early invaders that would be missed in traditional classification 12 , 13 . Furthermore, it can be useful when it is difficult to identify the morphological classification key such as physically damaged samples or the larval stage of invertebrates, which can decrease the classification’s resolution. In addition, metabarcoding is more practical and cost-effective than the traditional method which requires many experts’ labor and time to study the wide diversity of marine zooplankton 3 , 4 , 9 , 14 , 15 , 16 . Due to these advantages, it is essential to study zooplankton diversity using metabarcoding on a global to local scale.

Urbanized coastal areas may have various harmful influences on the original ecosystems due to the population increase and the artificial development of ports and reclamation areas. Therefore, continuous monitoring of ecosystem and biodiversity changes is necessary. In the urbanized and industrialized coastal inner bays and ports, environmental changes frequently occur because of artificial factors such as pollutants from cities or ships 17 . Alien species in the ballast water of large vessels that traverse the ocean may also disturb ecosystem 9 , 18 , 19 . Continuous monitoring of the zooplankton community and diversity in the region will provide useful data in responding to human activities and ecosystem changes and crises. Thus, we chose the biggest port, Busan in Korea. The northeastern part of Yeongdo-gu, Busan (South Korea), is actively urbanized. It is the entrance to Busan Port, one of the world’s largest ports and is visited by many ships. Busan port development, reclamation, and installation of water breakers have decreased the length of the natural coastal line and velocity of seawater flow in this area 20 . Potential and persistent environmental pollution from the increase in human activities in this area can also cause marine ecosystem instability 21 .

In the current study, zooplankton samples collected at 2-week intervals from February 2019 to April 2020 from a sampling station in Yeongdo-gu, Busan, were analyzed by metagenomics to reveal the pattern of changes in zooplankton diversity and community structure over time (Fig.  1 ; Table 1 ). Using “common” and “total” zooplankton data, we investigate the process most suitable for low-abundance operational taxonomic units (OTUs) when performing comprehensive and long-term coastal ecosystem monitoring. In addition, we compare the species identified in this study with previously reported species and choose candidates for potential non-indigenous species (NIS) that could cause a disturbance in the marine ecosystem. Furthermore, we discuss the limitations of zooplankton diversity studies that use molecular methods and how to overcome these problems and improve accuracy in the future. The results from this study will play an important role in studying zooplankton diversity and long-term variability in the port area.

figure 1

Location of the sampling site (red dot) in Busan Bay, southern coast of Korea. The maps were created using QGIS (v.3.16; ). The base map is from OpenStreetMap and OpenStreetMap Foundation under the Open Database License ( ).

Environmental conditions and summary of DNA data and taxonomic assessment

Water temperature and salinity were measured at the same station and on the same dates as most zooplankton samplings from February 2019 to March 2020 at 2-week intervals. The average water temperature was 16.6 ℃, ranging from 11.4 to 27.6 ℃ (see Fig. S2). It gradually increased from February 2019, peaked on August 14, 2019, and then decreased. The average water temperatures in February and March of 2020 were slightly lower than those in 2019. During the same sampling period, the salinity (practical salinity unit, psu) ranged from 30.2 to 34.5 psu, with an average of 33.0 psu. Contrary to water temperature, salinity generally decreased and increased again from February 2019 to September 2019 (Table S1 ; Fig. S2). These seasonal changes in water temperature and salinity are consistent with previous studies in the Busan Bay and the Southern coast of Korea 22 , 23 , 24 , 25 .

A total of 9,163,971 amplicons were sequenced from the 30 samples and 8,490,439 reads (92.7%) remained after the stringent quality filtering of chimeras (Table 1 ). The number of OTUs (at the 98% similarity level) was 4,204 for all samples, varying between 166 and 601 OTUs for each sample (Table S1 ). Sequence reads were normalized to the minimum reads per sample (81,811 reads, YD56) for a sample-to-sample analysis due to different samples showing different numbers of sequence reads (Fig. S3a). After rarefying, the number of OTUs was 3,486 for all samples, varying between 147 and 473 OTUs for each sample (Table S1 ). After BLAST search to the NCBI nt database, 426 OTUs (932,472 reads) were retained. Approximately 63% (267 OTUs and 771,849 reads) were classified as zooplankton taxa from the taxonomic assessment (Tables S1 and S2 ; Fig. S4). The proportion of reads for the zooplankton communities in each sample was summarized in Fig. S4 and Table S2 . Of 267 “total” zooplankton OTUs, 72 taxa were identified as copepods, representing 38.6% of the total reads. The BLAST scores and modified zooplankton species name were listed in Table S3 .

Filtering for common OTUs contributing > 0.5% of sequence reads in at least one sample resulted in 239 OTUs, representing 95% of the total reads. Finally, 64 “common” zooplankton taxa were classified from the taxonomic assessment (Table S1 ). Rarefaction was performed after taxonomic classification because the rarefaction curve reached a plateau due to the decrease in rare OTUs (Fig. S3b).

Seasonal trends of α-diversity and taxonomic composition of zooplankton based on metabarcoding data

The average Chao1 index of “total” zooplankton was 42, ranging from 21.00 ± 0.16 (YD50) to 77.00 ± 30.34 (YD44). The overall trends of the Chao1 index was low in April and high in September (Fig.  2 a; Table S4 ). The average Shannon diversity index of “total” zooplankton was 1.66, which varied from 0.43 (YD62) to 2.53 (YD38). Similar to the Chao1 index, the overall trends of the Shannon diversity was low in April and high in September to October, showing a seasonal pattern. However, diversity declined slightly in early 2020 compared with a similar period in 2019 (Fig.  2 b; Table S4 ). The observed taxa and Shannon diversity index of “common” zooplankton had similar distribution patterns to those of “total” zooplankton throughout the sampling period. However, the R 2 -value of the polynomial regression analysis for the observed taxa of “common” zooplankton was higher than that for the Chao1 index of “total” zooplankton (Fig.  2 c, d; Table S4 ).

figure 2

Temporal distribution of the α-diversity (species richness and Shannon index). ( a ) Chao1 index for “total” zooplankton species. ( b ) Shannon diversity index for “total” zooplankton. ( c ) Observed species richness for “common” zooplankton. ( d ) Shannon diversity index for “common” zooplankton. Standard errors and the regression lines are indicated by the vertical and red lines. Figures were produced using R (v4.0.3, ).

With the environmental data measured on the zooplankton sampling day (accepting ± one date gap), the correlation between the α-diversity index (Chao1 and Shannon) and environmental factors was confirmed. The α-diversity indices were positively correlated with water temperature and negatively correlated with salinity (Fig. S5). The α-diversity showed a slightly higher correlation with water temperature than salinity because salinity was more conserved throughout the year (Fig. S5). Seasonal pattern was analyzed by dividing into three main mesozooplankton groups 4 , 24 ; copepods (average of 47%), meroplankton (43%), and non-copepod holoplankton (10%) (Fig. S6). The three groups showed highly seasonal dynamic patterns; the dominant group was copepods in January (average of 94.5%) and Meroplankton in September (88.89%).

To confirm the overall species composition, the total number of species was divided into 19 taxonomic groups at the phylum or class level (subclass or infraclass). The relative abundances in each sample were shown as a bar plot (Fig.  3 ; Table S5 ). Copepoda (average of 47%) and Cirripedia (28%) appeared in all 30 samples. Additionally, the frequency of reads was relatively high in Branchiopoda (7.9%, n  = 23 where n is the number of samples appeared), Echinodermata (5.7%, n  = 29), Cnidaria (3.6%, n  = 27), Malacostraca (3.0%, n  = 26), and Mollusca (2.8%, n  = 29). The remaining 12 taxonomic groups (Annelida, Bryozoa, Chaetognatha, Chordata, Entoprocta, Nemertea, Ostracoda, Pantopoda, Phoronida, Platyhelminthes, Porifera, and Rotifera) were relatively rare taxonomic groups (average of relative abundance < 1%). When the same analysis was performed with only “common” zooplankton species, the results were similar to the above but only 11 taxonomic groups remained (Fig. S7).

figure 3

Taxonomic composition of zooplankton for 30 samples with 19 taxonomic groups. The bar heights are indicated proportion (percentage of reads) of each taxonomic group. Figures were produced using R (v4.0.3, ).

The temporal distribution patterns of “common” zooplankton were indicated in a heatmap (Fig.  4 ; Table S6 ). Copepod species remained the most dominant with 24 taxa from 64 zooplankton taxa (Fig. S8) followed by Cirripedia with 10 species. Chthamalus challengeri (Cirripedia) (mean of abundance [MA]: 2.2; the number of samples in which this species existed [NS]: 27) appeared dominantly throughout the sampling periods and Perforatus perforatus (MA: 2.0, NS: 28) and Balanus trigonus (MA: 1.3, NS: 17) followed. Seven species of Echinodermata were identified. Ophiuroglypha kinbergi was found in most samples (MA: 1.5, NS: 24) and Schizaster doederleini (MA: 0.7, NS: 12) existed only in 12 samples between July and October with high average abundance. Six molluscan taxa were identified of which Mitrella bicincta (MA: 0.7, NS: 14) was the most dominant in winter. Four Mollusca taxa ( Magallana gigas , Reishia clavigera , Ostrea circumpicta , and Crepidula sp.) were more prevalent in summer than in winter. The Mollusca species Lirularia iridescens was found only once on May 22. There are 4 taxa in Malacostraca. Euphausia pacifica (MA: 0.9, NS: 18) was the most prevalent and appeared mainly in winter, while Belzebub intermedius was found mainly between August and October (MA: 0.5, NS: 8). Three species of Branchiopoda were identified. Evadne nordmanni (MA: 1.1, NS: 15) and Podon leuckartii (MA: 0.6, NS: 15) appeared mainly between January and May, while Pleopis polyphemoides (MA: 0.5, NS: 7) appeared relatively briefly, emerging between May and August. The remaining taxa also showed different distribution patterns along the sampling period. For data on all 64 “common” zooplankton species (Table S6 ).

figure 4

Heatmap of 64 “common” zooplankton taxa. Each read count is transformed log 10 (abundance + 1). The colors indicate relative abundance from high (purple) to low (white), and gray is 0. Figures were produced using R (v4.0.3, ).

Copepods were the most prevalent and abundant group in our samples. They consisted of five orders (Calanoida, Cyclopoida, Harpacticoida, Monstrilloida, and Poecilostomatoida) and their temporal distributions were visualized in a heatmap (Fig.  5 ). With 72 taxa (68 species, 2 genera, 2 families), Calanoida (54 species) showed the most common order followed by Harpacticoida (7 species), Cyclopoida (4 species), Poecilostomatoida (3 species), and Monstrilloida (1 species). The most prevailing species (that which appeared in most samples) was Acartia omorii (MA: 2.7, NS: 28) followed by Centropages abdominalis (MA: 1.6, NS: 25). Each of the others showed various temporal distribution patterns (Fig.  5 ; Table S7 ).

figure 5

Heatmap of subclass Copepoda. Each read count is transformed log 10 (abundance + 1). The colors indicate relative abundance from high (purple) to low (white), and gray is 0. Figures were produced using R (v4.0.3, ).

Seasonal differences in zooplankton communities

Clustering analysis was performed to investigate community structure changes over time (Fig.  6 ). Non-metric multidimensional scaling (NMDS) analysis [log 10 ( x  + 1) transformation, Bray–Curtis] of the “total” zooplankton was broadly divided into four main groups (dissimilarity cutoff 0.68) and three single-clustered samples (YD42, YD44, and YD54) (Fig.  6 a, c). The four main groups were roughly divided temporally into the “spring” group (G1) (February 13 to May 22), “summer” group (G2) (June 5 to July 31), “late summer-autumn” group (G3) (August 14 to November 6), and “winter” group (G4) (December 18 to February 26) (ANOSIM significance = 0.001, R  = 0.9221).

figure 6

Results of the clustering analysis. Cluster dendrogram of ( a ) “total” zooplankton and ( b ) “common” zooplankton. NMDS plot of ( c ) “total” zooplankton and ( d ) “common” zooplankton. The colors of cluster dendrograms and the NMDS plot indicate the seasonally divided groups. Black indicates a single cluster with the minimum dissimilarity cutoff (“total” species: 0.68, “common” species: 0.56). Figures were produced using R (v4.0.3, ).

The same NMDS analysis was performed on the “common” zooplankton [log 10 ( x  + 1) transformation, Bray–Curtis] (Fig.  6 c, d). Similar to the above, it was also largely divided into four main groups according to the season, but there were differences in some samples. The samples clustered in G2 and G3 were the same as those in the “total” zooplankton analysis. However, sample YD46 was included along with YD44 in G1 instead of G4, and G1 was divided into two subgroups (G1-1 and G1-2). Furthermore, there were three single-cluster samples (YD42, YD50, and YD54). Cluster analysis using only the “common” zooplankton did not well differentiate the temporal and seasonal differences in the zooplankton community compared with the “total” zooplankton analysis (ANOSIM significance = 0.001, R  = 0.8785).

We then conducted a SIMPER analysis to determine each taxon’s average percentage contribution to each of the four seasonal groups [standardized log 10 ( x  + 1)-transformed data]. The top five highest contributing species to seasonal differences ( p -value < 0.05) are indicated in Table 2 . It was found that 15 species, except for the duplicates in the list, greatly contributed to the cluster structure variations according to time and season. Therefore, we confirmed their appearance to observe which seasonal group they represented. P. leuckartii (Branchiopoda), E. nordmanni (Branchiopoda), and Eudactylopus yokjidoensis (Copepoda) showed higher abundance in spring (G1) than in other seasons. P. polyphemoides (Branchiopoda), B. trigonus (Cirripedia), Membranipora villosa (Bryozoa), and C. challengeri (Cirripedia) were appeared in summer (G2) compared to other seasonal groups. A large number of A. omorii (Copepoda) represented throughout all seasons but their abundance decreased in late summer-autumn (G3). Amphibalanus amphitrite (Cirripedia), B. intermedius (Malacostraca), Liriope tetraphylla (Cnidaria), S. doederleini (Echinodermata), and Paracalanus gracilis (Copepoda) were the representatives of late summer-autumn (G3) species. Finally, Clausocalanus furcatus (Copepoda) appeared in greater abundance in winter than in other seasons (Fig.  5 ; Table 2 ). All top 5 taxa which contributed significantly to distinguishing each seasonal group were included in the “common” taxa. The result of the full SIMPER analysis is attached (Supplementary Analysis S1).

Searching for candidates of potential invasive species

In order to evaluate the reliability of the overall metabarcoding classification, the results of our analysis were compared with the national list of marine species (NLMS) 26 . Of the 267 species identified in our data (including sequence-read depth < 10), ~ 75.7% (202/267) was confirmed to be the correct taxonomic name (species level: 192; genus level: 9; family level: 1) by NLMS and ~ 24.3% (65/267) of the remaining species were not found (Table S8 ). One of them was a freshwater taxon ( Cyclops vicinus ) 27 , 28 and 39 species in our data were only the same genus name in NLMS (Table S8 ). Moreover, 26 taxa did not have genus names as well as species names. C. vicinus is suggestive of debris that probably inflowed from the Nakdong River 29 . We confirmed whether the COI sequences for the 38 species that only existed in the NLMS with the same genus name had their congeneric species registered in NCBI. Twenty taxa of the 38 species have COI sequences for their congeneric species in the NCBI database. The remaining 18 taxa do not have COI sequences registered for any congeneric species in NCBI. Therefore, in the 18 cases, there might be a misannotation caused by a lack of sequence information. As a result, a final total of 46 taxa, 26 taxa without both species and genus name in NLMS and 20 taxa that have only the same genus name in NLMS and do not have the COI sequence for all other congeneric species within NLMS, was listed as candidates for potential invasive species or NIS (Table S8 ). It would be worth exploring the taxonomic identification in future studies.

In the study area (Yeongdo-gu, Busan), zooplankton diversity was highest in autumn (October) and lowest in spring (April) (Fig.  2 ; Table S4 ). This seasonal pattern was similar to previous observations of the zooplankton community in the Busan Bay and the southern coast of Korea 24 , 25 . In addition, the seasonal pattern is thought to have a relatively higher correlation with water temperature than salinity 30 . Copepod species dominated the zooplankton composition, followed by cirripedian larvae and branchiopods (Figs. 3 and S4). In previous studies, copepods were most dominant in the zooplankton communities in the coastal regions, followed by branchiopods or Cirripedia larvae, depending on the season or environment 24 , 25 , 31 .

It was confirmed that 30 temporal samples were roughly divided by season into four groups (Fig.  6 ). In addition, the 14 species that contributed significantly to each seasonal group as a result of SIMPER analysis were Podon leuckartii (Cladoceran), Evadne nordmanni (Cladoceran), Eudactylopus yokjidoensis (Harpacticoida), Pleopis polyphemoides (Cladoceran), Balanus trigonus (Sessilia), Membranipora villosa (colonial marine bryozoan), Chthamalus challengeri (Sessilia), Acartia omorii (Calanoida), Amphibalanus amphitrite (Sessilia), Belzebub intermedius (Decapoda), Liriope tetraphylla (Cnidaria), Paraster doederleini (Sea urchins), Paracalanus gracilis (Calanoida), and Clausocalanus furcatus (Calanoida). Note that species names are followed by WoRMS ( ). On the southern coast of Korea, P. leuckartii is most abundant in April and reported to be negatively correlated with water temperature and salinity 31 , 32 . Likewise, in our study, P. leuckartii was most abundant in spring (G1) and not detected in summer (G2) when the water temperature was high (Fig.  5 ; Table S7 ). E. nordmanni , which is known to appear briefly in the spring when the water temperature is between 10 and 17 °C 31 , 33 , was analyzed as a representative of spring (G1; February to May) in our study (Fig.  5 ; Table S7 ). E. yokjidoensis , a new species reported in 2018, was collected from the southern coast of Korea in April 2016 34 . It showed high abundance in spring (G1), indicating that this new species may exist in our study area, but very little was found in other seasons in our samples.

P. polyphemoides (Cladoceran) appears throughout the year in Chinhae Bay, Korea although its abundance is especially high when the water temperature is 18 °C 33 . It was also reported in the Mediterranean Sea at 18–19 °C 35 . Similarly, it was found in summer (G2; June to July) within a temperature range of 17.4 to 21.8 °C in our study, representing this season. M. villosa (Colonial marine bryozoan) was reported in Busan during the summer (June) and was mainly distributed in coastal ports of Korea in summer and autumn (August to November) 36 . In our data, it appeared only in summer (G2).

L. tetraphylla and B. intermedius , representatives of late summer-autumn (G3; August to November) in our study, have not already been accurately modeled for their annual distribution on the southern coast of Korea. L . tetraphylla was only detected in the coastal region of Busan in late September 37 and B. intermedius was confirmed only in the southern Yellow Sea of Korea during October 38 .

P. gracilis and C. furcatus were dominant in late summer-autumn (G3) and in the winter group (G4), supporting previous studies 39 , 40 , 41 . A. omorii was dominant throughout all seasons but with relatively low abundance in G3. A. omorii is not reported to appear in the summer when the water temperature is high (average 24 °C) 24 . The high-water temperatures in August may explain this species’ disappearance in September and its low abundance in late summer-autumn (G3) (Figs. 5 and S2). In addition, this species frequently appears in the eutrophic inner bay 24 , 42 . Therefore, it indirectly shows that our study area, the entrance to Busan Port, may have undergone some degree of eutrophication.

Cirripedia larvae B. trigonus and C. challengeri were most abundant in summer (G2) and A. amphitrite in late summer-autumn (G3), respectively. It has been reported that C . challengeri appeared most intensively in August–October near Oryuk Islets off Busan 43 , the outer area of our study area. However, because it is difficult to classify Cirripedia larvae down to the species level, no seasonal changes in the distribution of the other two Cirripedia species have been reported. According to a previous study, Cirripedia larvae are relatively abundant in summer and autumn than in other seasons 31 . Finally, S. doederleini is mainly distributed in the Caribbean 28 , 44 , and no record was found in Korea.

Our metagenomic analysis results revealed that the seasonal zooplankton community could be largely divided into four groups corresponding to the four seasons. The distribution pattern of species representing each seasonal group has shown to be largely consistent with past research 24 , 25 . It was also possible to estimate Cirripedia larvae species, which was not identified in previous studies. Given these results, the study of marine zooplankton community and diversity by metabarcoding is efficient and enhances understanding of the dynamics of the zooplankton community throughout the year. Moreover, should the metabarcoding sequence data and the analyzed results be stored and remain available for future analyses, it will allow easy detection of changes in species composition and any introduction of invasive species into the Busan Port ecosystem by simply uploading their COI sequences to the database.

Most of the species not recorded in NLMS (average read counts per sample: 23.74) showed relatively lower abundance than the identified species (119.85). For this reason, they may have been relatively rare in the coastal region of Korea and difficult to find. In addition, these species may pose a potential threat to marine ecosystems as invasive species, introduced by ship movements or climate change 45 . Therefore, in future monitoring of zooplankton in the region, it is necessary to investigate these species’ presence or absence carefully. Adding the presence or absence of barcode sequences (e.g., COI, 18S rRNA, ITIS) and database registration information to the NLMS in the future can greatly contribute to the improvement of the accuracy of future studies using metabarcoding. With only one year of observation, although it is difficult to state these are early invasive species in the Busan Port ecosystem, if we monitor them for a long time using metabarcoding, it should reveal their appearance trends. Hence, it may be possible to judge whether their abundance is increasing or just a short-term influx.

All 64 taxa identified in the “common” zooplankton were included in the “total” zooplankton taxa and accounted for 24.0% (64/267) of “total” zooplankton species. The small number of taxa in the “common” zooplankton accounted for about 97.8% (2,485,517/2,542,332) of the final read count for “total” zooplankton. Some zooplankton taxa occupy most of the abundance in the study area, and a large number of the other taxa show a very low frequency of appearance. Even if only “common” zooplankton was used, similar to the use of “total” zooplankton, the change in the temporal community structure was divided into the four seasonal groups. Moreover, in the SIMPER analysis, the top 5 species that showed significant differences among the seasons were included in the “common” zooplankton (Fig.  6 ; Table 2 ). Nonetheless, if we include the species that occupied a small proportion in the analysis, it provides a better distinction of seasonal changes in community composition (Fig.  6 ). The inclusion of rare species also helps detect early invaders or NIS introduced by climate change or human activities to predict and prepare for their impact on the ecosystem. Therefore, a species with low abundance should be reflected in the ecological analysis after sufficient data curation.

Continuous and extensive zooplankton ecological monitoring studies involving metabarcoding methods have several advantages. First, this method can be more efficient than traditional methods 46 . As exemplified by E . yokjidoensis in our study, if the researchers only register the COI (or any other marker sequence) for a new species that has just been reported, it is possible to quickly screen and predict where the new species is distributed within the stored metagenomic library data. Although morphological methods can produce similar results by re-analyzing previously-stored samples, this work requires relatively more experts’ labor than the metagenomic methods. Second, it can be possible to classify zooplankton larvae difficult to identify morphologically, like Cirripedia larva. Lastly, species that are difficult to detect by any morphology-based methods due to very small populations, such as early invaders and NIS candidates, can be detected with high sensitivity by metagenomics 47 . Nonetheless, even with metagenomic methods, it is hard to distinguish whether a species is a real member of the study area or a fragment of a dead specimen flowed from a river such as C. vicinus , a freshwater species identified in our data. Therefore, complementing metagenomic analysis with traditional morphology will enable understanding marine ecosystems more specifically and clearly than either approach alone, especially in extensive and continuous ecosystem monitoring.


Our study investigated how to maximize the advantages of metabarcoding for monitoring of zooplankton community structure and diversity in urban coastal regions like the entrance of Busan Port, Korea. In this study, the zooplankton community showed a typical four-season pattern and the species representing each seasonal group were generally consistent with previous studies. Even after the rare species were removed, “common” zooplankton enabled us to confirm the approximate pattern of change in zooplankton diversity. However, using all the OTUs, “total” zooplankton yielded a relatively more pronounced seasonal change in the zooplankton community structure, and potential candidates for early invasive species in the port ecosystem were identified. Although our observations were conducted over a relatively short period at one sampling station, it suggests that regular monitoring of urban coastal areas by metabarcoding could be useful for understanding this ecosystem and detecting potential hazards. Furthermore, it is expected that the accumulation of monitoring data using metabarcoding will enable predicting and responding quickly to changes in zooplankton diversity.

Material and methods

Sampling sites and collection.

The samples were collected at about 2-week intervals from February 13, 2019, to April 16, 2020, from a sampling site in Busan Bay (35.077° N, 129.083° E) off the southern coast of Korea, near the Korea Institute of Ocean Science & Technology (KIOST) (Fig.  1 ; Table 1 ). A plankton net with 200-μm mesh and 60-cm opening diameter was towed horizontally for a distance of 100 m (total filter volume, 20.6 m 3 ) across the water surface (< 1 m depth) for zooplankton sampling. Temperature and salinity were measured at 0.5 m and 1.0 m depth using a conductivity meter (YSI 30, OH, USA) and the average of these two values was used for further analysis.

DNA isolation and amplification by PCR

The collected zooplankton sample was transferred to the laboratory, where DNA extraction was undertaken immediately from 10 mL of the approximate 200-mL sample. The rest of the sample was stored in alcohol for later use. The TIANamp Marine Animals DNA Kit (Tiangen Biotech, China) was used to isolate DNA from the zooplankton sample.

The gene for eukaryotic mitochondria cytochrome c oxidase I (COI) was amplified by using the degenerate primer set mlCOIintF (5′-GGWACWGGWTGAACWGTWTAYCCYCC-3′) and jgHCO2198 (5′-TAIACYTCIGGRTGICCRAARAAYCA-3′) 48 . Amplification reactions were performed in 0.2-mL PCR tubes in a 30-μL mixture containing 1.8 μL of 1 × 10 –5  μM of primers, 2 μL of DNA template, 11.8 μL of ultrapure water, 15 μL of 2X DNA-free Taq Master Mix (CellSafe, Korea). Samples were amplified for 40 cycles using a MaxyGene Gradient Thermal Cycler (Axygen, CA, USA) under the following conditions: initial denaturation at 95 °C for 5 min (1 cycle), denaturation at 95 °C for 30 s, annealing at 46 °C for 30 s, and extension at 72 ℃ for 60 s. The final extension was performed at 72 ℃ for 5 min. A negative control (without DNA template) was performed for the PCR step to detect potential contamination. The PCR product was purified using the Universal DNA Purification Kit (Tiangen Biotech, China). Quantity and quality analyses of the PCR amplified fragments and purified product were estimated using capillary electrophoresis and an ND-1000 spectrophotometer (Nanodrop, Power Lab, Korea). To ensure a homogeneous number of sequencing reads from each sample, 100 ng of each amplicon DNA was taken and diluted to 4 nM with determination of the size of the DNA with Agilent Technologies 2100 Bioanalyzer (DNA 1000 Chip, USA). All the diluted samples were pooled and used in end-repair and ligation of adaptors followed by sequencing in the MiSeq platform according to the manufacturer’s protocol. Next-Generation Sequencing library constructed using the Nextera XT Index Kit and the TruSeq Nano DNA Sample Prep Kit as the main capture kit and sequenced using the MiSeq platform were performed at Theragen facilities (Theragen Biotech, Korea).

Quality control and merge

To filter low-quality reads, cutadapt (ver. 2.8) 49 was used to remove amplicon sequences and to discard any unknown nucleotide “N” and reads that had no primer sequences or < 200 bp in length. To maximize the read depth for each sample, low-quality score cutoff values were set differentially in forward-end reads ( q  = 30) and reverse-end reads ( q  = 20). Reads without a mate (singletons) were discarded using the pairfq script (ver. 0.17.0; ). Merging of paired-end reads was conducted by pear (Paired-End reAd mergeR, ver. 0.9.2) 50 with the following parameters 51 : v  = 30, t  = 50, n  = 250, m  = 350, and q  = 20.

OTU clustering

The resulting FASTA files were clustered using the vsearch tool (ver. 2.7.0) 52 . Next, sequences were dereplicated, sorted ( -derep_fulllength ), and those with < 2 clusters (singleton) were removed. The outputs that passed the previous steps were pre-clustered at a similarity threshold of 99% ( -cluster_size ). After pre-clustering, chimeras were de novo detected and removed using the UCHIME algorithm ( -uchime_denovo ) 53 . Lastly, final OTUs were clustered at a similarity threshold of 98% ( -cluster_size ) from pre-clustered OTUs, and all sequences were assigned to OTUs. A flowchart of the steps involved in metagenomics analysis is given in Fig. S1.

Taxonomic identification

Taxonomy was assigned to the OTU table using blastn of the Basic Local Alignment Search Tool (BLAST, ver. 2.10.0 +) 54 against the National Center for Biotechnology Information (NCBI, ) non-redundant nucleotide (NCBI nr/nt) database (as of August 26, 2020, N = 60,251,963 sequences) with an E-value < 1 × 10 –10 , database size of 3 × 10 11 , and percentage identity ≥ 99% options for genus or species level. The following three steps were performed on the BLAST results to increase the accuracy of taxonomic assessment and eliminate chimeric OTUs: (1) accept only OTUs having a length of gaps and mismatches ≤ 5; (2) accept only aligned lengths ≧ 200 bp and bit-score ≧ 100; (3) select one with the longest alignment length if there are many OTUs aligned with the same query sequence. The remaining OTUs were then processed for final taxonomic identification.

The taxonomic assignment and hierarchical classifications from NCBI accession numbers were done using the ‘ taxonomizr ’ package 55 in R software. Next, the OTU tables were modified to the species or genus level (in case that the NCBI database did not contain species level information) and were used for further analyses. After BLAST, all OTUs corresponding to the shown taxa were identified using a custom Perl script ( ). The Perl script is available upon request from the authors. In addition, ‘Bacteria’, ‘Fungi’, ‘Fish’, ‘Insect’ (from inland), ‘Mammalian’, ‘Phytoplankton’, and ‘Environmental’ or ‘Unclassified’ OTUs were removed from the OTU count table because we focused on marine zooplankton diversity. Finally, the synonymized taxa were combined into one species by referring to the World Register of Marine Species (WoRMS, ) 28 .

Biodiversity analysis

In zooplankton studies using metagenomics, it is common that low-abundance reads are discarded, and further analysis is performed to remove contamination or PCR artifacts or compare morphological analysis data with the metagenomically dominant species 3 , 56 , 57 . Instead, in this study, we applied two methods to determine the difference between the data with the low-abundance OTUs removed (“common” zooplankton) and the data with all OTUs (“total” zooplankton). To confirm the difference between the two methods, the OTU removal standard used in the high contamination risk sampling method was followed. Thus, for “common” zooplankton, OTUs that contributed > 0.5% of sequences in at least one sample were retained 3 . Conversely, “total” zooplankton was used for analysis without removal of low-coverage OTUs.

All samples were rarefied at the lowest sequencing depth to reduce biases resulting from differences in sequencing depth using vegan 58 in R software. As there were many un-assigned OTUs in taxonomic assessment, rarefaction was performed at the OTU level to secure as much read depth as possible for “total” zooplankton. For “common” zooplankton, rarefaction was performed at the remaining species level after taxonomic assessment. Using phyloseq 59 in R software, species richness (observed or Chao1 index) and Shannon diversity index were estimated. Linear regression models were used to examine the relationship between environmental variables and biodiversity with R software 60 .

NMDS was employed to cluster samples according to seasonally different community compositions using the phyloseq and vegan packages in R. The original species-level OTU data were transformed to log 10 (abundance + 1) before NMDS. Then, NMDS for transformed data was conducted using the Bray–Curtis distance method (100 permutations). Analysis of similarity (ANOSIM) was applied to test each seasonal group’s significant effects on community composition (999 permutations). Similarity percentages (SIMPER) analysis was conducted on Bray–Curtis similarities from log 10 (abundance + 1) transformed (100 permutations) data using the vegan package implemented in the R programming language 58 , 60 . All figures (except for Figs. 1 , S1, and S2) were initially created using R 60 .

By comparing the species shown in our analysis results with NLMS published by the National Marine Biodiversity Institute of Korea 26 , we confirmed the species listed as candidates for NIS. First, all the species identified by metabarcoding were checked if any records had appeared in the coastal region of Korea. For the species in our results that did not exist in NLMS, the congeneric species in NLMS were checked if they had taxonomy information and COI sequence registered in the NCBI. Finally, the taxa without both species and genus name in NLMS plus COI sequence for all other congeneric species within NLMS were estimated as potential early invader species or NIS.

Data availability

All sequencing data are archived in the NCBI Sequence Read Archive (SRA) database under BioProject number PRJNA739266.

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We thank members of Eyun and Lee’s laboratory and the anonymous reviewers for their valuable comments. We also would like to thank Dr. Hyung-Ku Kang for confirming the morphological identification.

This research was supported by the Chung-Ang University Graduate Research Scholarship in 2020 and the National Research Foundation of Korea Grant (2018R1C1B3001650) to SE. This work was also supported by the Korea Institute of Ocean Science and Technology (KIOST) Grant (PE99712) to YHL.

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Chi-une Song, Hyeongwoo Choi, Min-Seung Jeon, Eun-Jeong Kim & Seong-il Eyun

Department of Taxonomy and Systematics, National Marine Biodiversity Institute of Korea, Seocheon-gun, Chungchungnam-do, 33662, Korea

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Sung Kim, Choong-gon Kim, Hyenjung Hwang, Dayu Wiyati Purnaningtyas, Seok Lee & Youn-Ho Lee

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Y.L.: Conceptualization; S.K., C.K., S.L., Y.L.: Investigation; C.S., H.C., M.S.J., E.J.K., D.W.P., S.E., Y.L.: Writing—original draft; C.S., H.G.J., H.H., S.E., Y.L.: Writing—review and editing; S.E., Y.L.: Supervision.

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Song, Cu., Choi, H., Jeon, MS. et al. Zooplankton diversity monitoring strategy for the urban coastal region using metabarcoding analysis. Sci Rep 11 , 24339 (2021).

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Researchers introduce new model that bridges rules of life at the individual scale and the ecosystem level

by Michigan State University

MSU, Carnegie Science introduce a big new idea with the help of tiny plankton

Researchers at Michigan State University and the Carnegie Institution for Science have developed a model that connects microscopic biology to macroscopic ecology, which could deepen our understanding of nature's laws and create new opportunities in ecosystem management.

Reporting in the journal Science the team showed how microscopic relationships in plankton—such as between an organism's size and nutrient consumption—scales up to predictably affect food webs.

"Using data that other researchers have measured at the microscale about these organisms, our model can predict what's happening at the scale of whole ecosystems," said Jonas Wickman, a postdoctoral research associate with MSU's College of Natural Science and first author of the new paper.

"We can now show how lower-level rules of life feed into these higher levels based on ecological interactions and evolutionary considerations," said Elena Litchman, a senior staff scientist at Carnegie's Biosphere Sciences and Engineering division. "Up until now, people had mostly considered these levels in isolation."

This new report will enable the team and its peers to design new experiments to test, refine and expand the model by extending it to other species and ecosystems. This could ultimately lead to the model being able to inform ecosystem management strategies in various environments around the globe.

Small organisms, global impact

The team is also interested in what more they can learn from their model and the plankton they study.

"We chose them as a model system for a few reasons," said Christopher Klausmeier, an MSU Research Foundation Professor at the W. K. Kellogg Biological Station. He's also a faculty member with the Department of Plant Biology, the Department of Integrative Biology and the Ecology, Evolution and Behavior, or EEB, program at MSU.

One of the reasons is that plankton are the primary research focus for the research group led by Litchman and Klausmeier.

"They're relatively simple organisms. If anything is going to follow the rules, plankton are a good candidate," Klausmeier said. "But they're also globally important. They're responsible for about half of the primary production on Earth and are the base of most aquatic food webs."

Primary producers use biochemical processes such as photosynthesis to turn the Earth's carbon and raw nutrients into compounds that are useful for the organisms themselves and their predators. This means plankton are a critical cog in the natural machinery that cycles the planet's life-essential elements, including carbon, nitrogen and oxygen.

Having this scaling model that describes plankton can thus be useful for better understanding those key processes, as well as if and how those are changing with the planet's climate.

The team did not include climate-associated variables like temperature in this study, but the researchers are already planning their next steps in that direction.

"The effects of global warming could alter the lower-level physiological processes," Litchman said. "We could then use this framework to see how those effects bubble up to different levels of organization."

Eye-popping simplicity

Wickman hasn't always been a plankton ecologist. His undergraduate degree was in physics, but he switched to ecology during his doctoral studies in Sweden before joining the Klausmeier-Litchman lab in 2020.

The team said his physics background shaped his approach to developing this model, which Litchman described as "beautiful—stripping out everything except the essential processes."

To begin, Wickman built from fundamental theories describing his system of interest. Only in this case, the system wasn't, say, quantum mechanical particles. It was tiny organisms linked by a simple food web.

Within that web, phytoplankton are the primary producers and zooplankton are their predators.

"Well, grazers really," Wickman said of the zooplankton. "We don't usually call cows predators of grass."

To fully appreciate the workings of this important relationship and its global implications, researchers have been breaking it down into its components driven by ecology and evolution.

For example, microscopic considerations like the size of a phytoplankton affect its ability to compete for nutrients, which in turn influence how big cells can get and how likely it is to become food for zooplankton.

These microscopic factors are thus connected to macroscopic variables, including the distribution of nutrients and how densely or sparsely different plankton populate their environments.

Over the past several decades, scientists have formulated mathematics that describe important relationships at the micro scale and macro scale individually. Attempts to bridge the scales, however, have left researchers wanting, Wickman said.

That's because previous attempts to make that connection have had to make compromises. Some previous models have chosen simplicity at the expense of accuracy and realism. Others have confronted that complexity with brute computational force, making them less accessible and harder to work with.

"Our model includes actual ecological and evolutionary mechanisms but is simple enough to use," Wickman said.

The work began as pure theory, but Litchman suggested that it should be possible to test its predictions using existing data. "When I saw how well the model matched the observations, my eyes popped out," she said.

The team had been working on this problem for several years and had published an earlier paper developing the eco-evolutionary modeling techniques they relied on.

Now, the team has showcased the potential of their model by uniting it with real-world data.

"The revelation that patterns emerging at macroecological scales can be explained by properties of individual organisms at microecological scales is as compelling as it is elegant," said Steve Dudgeon, program director in NSF's Directorate for Biological Sciences, which helped fund the work.

"The study provides new avenues of research that could enhance prediction of how ecosystems, and the relationships among the organisms in them, will change with eco-evolutionary dynamics interacting in changing environments."

Because of the natural variation of biological systems, the model and its results may seem messy to someone used to the precision of physics, but Wickman views them with excitement.

"We actually achieved quite good accuracy for ecology," he said. "We may not have the same level of theoretical elegance as physics, but that just means we have much more territory to explore."

Journal information: Science

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Original research article, plankton diversity in tropical wetlands under different hydrological conditions (lake tana, ethiopia).

  • 1 Department of Aquatic and Wetland Management, Bahir Dar University, Bahir Dar, Ethiopia
  • 2 Department of Biology, Ecology and Biodiversity Research Unit, Vrije Universiteit Brussel, Brussels, Belgium
  • 3 Laboratory of Aquatic Ecology, Evolution and Conservation, KU Leuven, Leuven, Belgium
  • 4 Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
  • 5 Department of Biology, Bahir Dar University, Bahir Dar, Ethiopia
  • 6 Department of Biology, Ghent University, Ghent, Belgium
  • 7 Department of Natural Resource Management, Bahir Dar University, Bahir Dar, Ethiopia
  • 8 Multidisciplinary Institute for Teacher Education (MILO), Vrije Universiteit Brussel, Brussels, Belgium

Plankton is an integral part of wetland biodiversity and plays a vital role in the functioning of wetlands. Diversity patterns of plankton in wetlands and factors structuring its community composition are poorly understood, albeit important for identifying areas for restoration and conservation. Here we investigate patterns in local and regional plankton richness and taxonomic and functional community composition in riverine papyrus swamps, river mouth wetlands, and lacustrine wetlands in the Lake Tana sub-basin, Ethiopia. Data on phytoplankton, zooplankton, and environmental variables were collected from 12 wetlands during the dry and wet seasons of 2018. Redundancy analysis, and linear mixed effect models, were used to investigate differences in local environmental conditions and variation in plankton community richness and composition between wetland types. We also assessed the ecological uniqueness of the plankton community by calculating the contribution of a single wetland: local contributions to overall beta diversity (LCBD) and contributions of individual species (SCBD) to overall beta diversity (BD Total ). Beta regression models were used to investigate the relationships of LCBD and SCBD to environmental variables, wetland, and taxa characteristics. A total of 85 phytoplankton taxa, distributed among 18 Reynolds functional groups, and 57 zooplankton taxa were observed over the entire set of samples. Local plankton taxon richness was significantly higher in riverine papyrus swamps (mean taxa of 30 phytoplankton and 21 zooplankton) compared to river mouth wetlands (mean taxa of 27 phytoplankton and 13 zooplankton). Several local environmental variables and the composition of the plankton community differed significantly between the three wetland types. The highest phytoplankton ecological uniqueness (LCBD) was detected in lacustrine wetlands, whereas the riverine papyrus swamps had the highest zooplankton ecological uniqueness. Based on our analyses, we recommend protecting the wetlands with high LCBD values and stress the importance of various wetland types for preserving the diverse plankton communities of Lake Tana wetlands.


Wetlands are one of the most productive habitats and are of immense socio-economic and ecological importance ( De Groot et al., 2012 ) because they provide a wide range of ecosystem services such as groundwater recharge, water purification, nutrient cycling, shoreline stabilization, cultural, recreational, and educational resources, flood protection, and carbon storage ( Everard et al., 2019 ). Wetlands also host exceptionally high levels of biodiversity, which is attributed to their high spatial and temporal environmental heterogeneity ( Keddy et al., 2009 ). Wetlands are home to numerous organisms including phytoplankton and zooplankton species (hereafter: plankton) ( D’Ambrosio et al., 2016 ; Gogoi et al., 2019 ), macroinvertebrates ( Mereta et al., 2013 ; Gezie et al., 2019 ), aquatic plants ( Getnet et al., 2021 ), fish ( Aziz et al., 2021 ), amphibians ( Nagel et al., 2021 ), waterbirds ( Aynalem and Bekele, 2008 ; Chawaka et al., 2018a ; Chawaka et al., 2018b ), and mammals ( Chatterjee and Bhattacharyya, 2021 ). Phytoplankton form the base of the food chain in aquatic ecosystems and play an important role in overall wetland productivity ( Chengxue et al., 2019 ), while zooplankton are recognized as the main primary consumers. Due to their short life cycle and thus potentially rapid response to anthropogenic disturbance and environmental changes, planktonic organisms are also regarded as ideal bioindicators for assessing the environmental status of wetlands ( Wijeyaratne and Nanayakkara, 2020 ; Chaparro-Herrera et al., 2021 ).

The plankton community structure differs between tropical and temperate lakes, but research on wetlands is still limited. One of these differences is that smaller zooplankton species (including rotifers) often predominate in tropical lakes, whereas large cladocerans are dominant in temperate lakes ( Fernando, 1980 ; Green, 1994 ). However, in some Ethiopian tropical highland lakes, the zooplankton communities are dominated by large Cladocerans ( Dejen et al., 2004 ; Fetahi et al., 2011 ; Haileselasie et al., 2012 ). In temperate lakes, diatoms often dominate the phytoplankton community during winter and phytoflagellates during the summer ( Widdicombe et al., 2010 ). Chlorophyta and Cyanophyta, as well as diatoms in some cases, generally dominate tropical lakes ( Ndebele-Murisa et al., 2010 ). Wide temperature and light intensity fluctuations are widely regarded as the primary drivers of variation in plankton community structure in temperate systems, whereas hydrological conditions are important in the tropical systems ( Loverde-Oliveira et al., 2009 ; Shatwell et al., 2016 ).

Hydrological conditions, such as water availability and depth in wetlands affect the interchange of water, sediment, nutrients, organic matter, and organisms between the wetlands and their adjacent water bodies (i.e., river, river-lake, or lake; Chaparro et al., 2018 ; Feng et al., 2021 ; Rideout et al., 2021 ). During high water level periods, inputs from the river, and lake water can also modify local environmental conditions in wetlands by altering dissolved oxygen concentration, conductivity, turbidity, and nutrient levels ( Alvarez-mieles et al., 2013 ; Castillo, 2020 ). Conversely, during periods of isolation or lower exchange with the river and lake, environmental conditions in the wetlands may be largely driven by autochthonous processes occurring in the water column of the wetland ( Cardoso et al., 2012 ). Thus, variation in hydrological conditions in wetlands results in highly heterogeneous habitats at the wetland scale, ranging from lotic to lentic, turbid to clear water, nutrient-rich to nutrient-poor, frequently disturbed versus relatively stable, and well-vegetated to almost barren conditions ( Chaparro et al., 2018 ). Consequently, plankton communities comprise a huge diversity of life-history traits that vary in response to the environmental conditions in the wetland. These range from small fast-growing phytoplankton taxa adapted to turbulent waters to large slow-growing organisms adapted to more stable conditions ( Reynolds, 2002 ; Padisák et al., 2009 ; Bhat et al., 2015 ), and from pelagic filter-feeding to scraping zooplankton taxa associated with vegetation ( Gebrehiwot et al., 2017a ).

Wetlands can be found in all climatic regions of Ethiopia, with the wetlands of Lake Tana being the second largest in the country after the Gambella wetlands ( Menbere and Menbere, 2018 ). In the Lake Tana sub-basin, wetlands are found mostly along lakeshores, rivers, and stream banks and cover 248 km 2 of land ( Hunegnaw et al., 2013 ; Stave et al., 2017 ; Mengistu, 2018 ). Previous studies in the Lake Tana sub-basin were focused on the abundance and species richness of lake plankton ( Dejen et al., 2004 ; Imoobe and Akoma, 2008 ; Gashaye, 2016 ; Melaku, 2017 ), fish, waterbirds, and mammals ( Aynalem and Bekele, 2008 ; Mengistu, 2018 ; Zelelew and Archibald, 2021 ). Specifically for the wetlands in the sub-basin, benthic invertebrates ( Gezie et al., 2017 ; Gezie et al., 2019 ), fish ( Anteneh et al., 2012 ), macrophytes ( Getnet et al., 2021 ), and water-birds ( Aynalem and Bekele, 2008 ) have been studied. The capacity of these systems for sediment and nutrient retention has also been investigated ( Mucheye et al., 2018 ). They act as a buffering zone, as nurseries for most of the fish populations in the lake, and as breeding and feeding grounds for waterfowl and mammals ( Aynalem and Bekele, 2008 ; Getahun and Dejen, 2012 ; Zelelew and Archibald, 2021 ). Lake Tana and most of these wetlands are also a UNESCO Biosphere Reserve ( Kalmbach, 2017 ) due to their high and unique biodiversity.

This study is focused on river-connected riverine papyrus swamps, river mouth wetlands at the interface between river and lake, and lake-connected lacustrine wetlands ( Hunegnaw et al., 2013 ; Aynalem et al., 2017 ; Getnet et al., 2021 ). These wetlands are characterized by seasonal water level variations, which are mostly caused by the unimodal rainfall pattern in the area ( Jemberie et al., 2015 ). The river mouth and lacustrine wetlands are increasingly threatened by livestock grazing and crop cultivation ( Wondie, 2018 ; Abera et al., 2021 ; Chandrasekharan et al., 2021 ). Additional environmental pressures for river mouth wetlands are the infestation by the non-native floating water hyacinth ( Eichhornia crassipes ) ( Asmare et al., 2020 ) and the accumulation of pollutants from nearby cities and human settlements ( Abebe and Minale, 2017 ). Excessive water abstraction for small-scale irrigation, particularly at river mouth wetlands, is particularly common during the dry season ( Abebe et al., 2020 ). Lake water level fluctuations caused by lake water abstractions for major development projects in the sub-basin also had a significant impact on lacustrine wetlands ( Alemayehu et al., 2010 ). These anthropogenic disturbances have been proven to influence the hydrological conditions in other lakes and consequently their plankton diversity ( Gownaris et al., 2018 ; Napiórkowski et al., 2019 ), and therefore, can be expected to cause both temporal and spatial variation in plankton communities in wetlands of Lake Tana. Generally, plankton communities in wetlands under different hydrological conditions are less studied (but see Cardoso et al., 2012 ; Rojo et al., 2016 ; Chaparro et al., 2019 ). Similarly, plankton communities in Lake Tana wetlands under different hydrological conditions are poorly understood.

In this study, we examined differences in local taxonomic richness and the community composition of plankton, as well as the Reynolds functional phytoplankton composition among wetlands under different hydrological conditions. In addition, we aimed to identify wetlands that support unique plankton communities and therefore require special attention with respect to conservation and restoration. The wetlands studied were (i) riverine papyrus swamps (connected to rivers, R) with substantial papyrus stands, and distant from the lake; (ii) river mouth wetlands at the river-lake interface (connected both to the tributary rivers and the lake, RL), and (iii) lacustrine wetlands (only connected to the lake, L). We hypothesize that plankton communities from wetlands under various hydrological conditions differ in their taxonomic richness and community composition and that relatively undisturbed wetlands (i.e., riverine papyrus swamps) are more diverse and unique than those influenced by human activities.

Material and Methods

The Lake Tana sub-basin is located between 10.95 ◦ N to 12.78 ◦ N latitude and from 36.98 ◦ E to 38.25 ◦ E longitude in the highlands of northwestern Ethiopia ( Figure 1 ). The sub-basin covers an area of 15,000 km 2 , including the Lake, the largest surface water body in the country, with a total surface area of 3,080.8 km 2 . It is 67 km wide, 84 km long and its mean depth is 9 m ( Kebede et al., 2006 ). The seasonal variation in rainfall is controlled by the northward and southward movement of the intertropical convergence zone resulting in a single rainy season between June and October ( Fetene et al., 2018 ). The hydrogeomorphology of the Lake Tana sub-basin is diverse, with alluvial sediments found in the lower reaches of the tributary rivers ( Poppe et al., 2013 ). Approximately 4.5 million people are living adjacent to the lake and its associated wetlands. More than 500,000 people are directly and indirectly dependent on the lake and its associated wetlands ( Vijverberg et al., 2009 ). The land use in the Lake Tana basin is predominantly cultivated land (7,608.7 km 2 ), water area (2,152.7 km 2 ), bushland (1,438.9 km 2 ), grassland (1,242.1 km 2 ), and forest (546.9 km 2 ) ( Tewabe and Fentahun, 2020 ).

FIGURE 1 . Map of Lake Tana and its wetlands; dots show the investigated wetlands divided into the different wetland types (for individual wetland code; see Supplementary Table S1 .

The wetlands of Lake Tana are permanent or seasonal with a total area of 248 km 2 and distributed from the headwaters of the Guna and Gish-Abay streams to the Fogera and Demba floodplains ( Hunegnaw et al., 2013 ). The majority are located mainly around the lake shores, in the lower reaches of the Gilgel Abay River, and at the river mouths of Gilgel Abay, Ribb, Gumara, and Megech Rivers ( Hunegnaw et al., 2013 ).

The three most common wetland types: riverine papyrus swamps, river mouth wetlands, and lacustrine wetlands in the Lake Tana sub-basin were studied ( Figures 1 , 2 ). A total of twelve wetlands (i.e., four wetlands from each wetland type) were selected ( Supplementary Table S1 ). The riverine papyrus swamps sampled are mainly located in the lower reaches of the Gilgel Abay River and are dominated by papyrus ( Cyperus papyrus ) and other less dominant emergent vegetation ( Getnet et al., 2021 ). With little grazing, settlement, or agricultural activity, these wetlands are characterized with >80% vegetation coverage ( Wondie, 2018 ). These wetlands are permanent swamps with little fluctuation in water level and remain flooded well into the dry season. River mouth wetlands are located at the entrance of the rivers Dirma, Gilgel-Abay, Gumara, and Megech into Lake Tana. The river mouth wetlands are influenced by both lake water levels fluctuation and river flows. During the dry season, most river mouth wetlands have very low water depths due to excessive water abstraction from the inflowing rivers for irrigation by local farmers. River mouth wetlands are characterized by some exotic species and grassy vegetation, with less than 20% cover. The river mouth wetlands included in this study, except the Gilgel Abay wetland, are infested with the non-native water hyacinth ( Eichhornia crassipes ) ( Asmare et al., 2020 ). Rivers input a large amount of sediment into these wetlands, resulting in the formation of deltas in the majority of them (e.g., in the Gilgel Abay and Gumara wetlands) ( Abate et al., 2017 ; Lemma et al., 2020 ). Lacustrine wetlands are mostly found on the lake’s southern shores. Lacustrine wetlands have open water-dominated microhabitats with some emergent, submerged, and freely floating macrophyte species with plant cover ranging from 30 to 50%. The river mouth and lacustrine wetlands are home to 27 fish species, which belong to four families: Cichlidae, Clariidae, Nemacheilidae, and Cyprinidae ( Dejen et al., 2017 ).

FIGURE 2 . Field pictures showing the typical habitats for riverine papyrus swamps (R), river mouth wetlands (RL), and lacustrine wetlands (L).

Field Sampling and Sample Analysis

Sampling for phytoplankton, zooplankton, and environmental variables was carried out from March to May 2018 (in the dry season) and July to October 2018 (in the wet season) in the 12 selected wetlands. A transect line was drawn using Google Earth in the middle part of each wetland ensuring that the entire width of the wetland was covered. In lacustrine and river mouth wetlands, transects started at the edge of the open water and ended at the most outward part of the emergent vegetation that is associated with the wetland. In the riverine papyrus swamp, transects perpendicular to the flow direction were established. Along each transect, 6 plots of 5 × 5 m, at equal distance from each other, were established and marked using GPS (GPSMAP ® ×64). Dissolved oxygen, specific conductance, turbidity, pH, water temperature, water depth, and silt layer (hereafter: sediment depth) were measured from all established plots. Water temperature, pH, specific conductance, and dissolved oxygen were measured in-situ using Multi-probe field meter (YSI 556 MPS). Chlorophyll-a concentration was used as a proxy for phytoplankton biomass and was measured in vivo in the field using a handheld fluorometer (AquaFluor™, Turner Designs). Similarly, turbidity was measured in the field using an aqualytic Turbidimetere (ALT250-IR). Water depth and sediment depth were measured with a wooden stick and a metal measuring tape. For total nitrogen (TN) and total phosphorus (TP), 50 ml water samples were collected from each plot close to the water surface. Samples from different plots along the transect were pooled. The pooled water samples were kept cool in the dark in the field and frozen (−20°C) in the laboratory until further analysis ( American Public Health Association, 2005 ). TN and TP concentrations were measured using a spectrophotometer (HACH, DR/6000) after alkaline persulfate digestion ( American Public Health Association, 2005 ).

Water samples (200 ml) for phytoplankton analysis were collected from the surface of the wetland in each plot. Samples from different plots within each wetland were pooled and subsequently preserved with Lugol’s iodine solution and stored in the dark. In the laboratory, the phytoplankton samples were sedimented for 48–72 h on a heat-free, vibration-free surface, and used for species identification and quantification ( Bellinger et al., 2015 ). A total of 1,190 ml of the supernatant was removed, and the remaining 10 ml concentrated phytoplankton sample was used for further analysis. A 1 ml sub-sample was transferred into the Sedgwick-Rafter cell, which had 1,000 grids, after being fully homogenized by gentle inversion and agitation. Phytoplankton was identified to the species level using taxonomic literature ( Tilman et al., 1986 ; Taylor et al., 2007 ; Bellinger et al., 2015 ) and an inverted microscope at a ×1,000 magnification (Krüss, A. Krüss Optronic, Germany). The numbers of phytoplankton cells (single-cell, filament, and colony) of different phytoplankton species in 100 random fields were determined ( Hötzel and Croome, 1999 ).

Samples for zooplankton were collected from the surface of the wetland in each plot by filtering 40 L of water through a 64 µm mesh. Samples from different plots within each wetland were pooled and subsequently preserved in 4% formaldehyde solution in the field and stored at 4°C. For identification and estimation of density, the concentrated initial sample was mixed homogeneously and a 1 ml subsample was taken with a wide opening Stempel pipette and then poured into a Sedgwick–rafter cell ( Wetzel and Likens, 2013 ). This process was replicated until at least 300 rotifer individuals were counted. Additional subsamples were taken for Copepods and Cladocerans when the number of individuals was less than 50 for at least one species ( Mack et al., 2012 ). Zooplankton was identified to the species level under a stereoscopic microscope at ×400 magnification, by using the relevant taxonomic literature ( Idris, 1983 ; Van de Velde, 1984 ; Koste and Shiel, 1987 ; Koste and Shiel, 1989 ; Defaye, 1988 ; Sinev, 2016 ). Calanoids and cyclopoids were enumerated according to their developmental stages (adults, copepodites, and nauplii), whereas for rotifers and cladocerans all stages were counted as one age class.

Data Analysis

Data were analyzed using a combination of univariate and multivariate statistical methods. The environmental variables that were measured at several plots within each wetland were averaged for each wetland for the wet and dry seasons separately. Local diversity was calculated for each wetland as the local number of taxa (α - richness) in a given wetland) during the wet and dry seasons separately. Similarly, we determined regional taxon richness (γ-richness) as the total number of taxa across wetlands for the dry and wet seasons separately ( Magurran, 2004 ). The recorded phytoplankton species were classified into Reynolds Functional Groups (RFGs) following Reynolds (2002) and updated by Padisák et al. (2009) to identify dominant functional groups in each wetland.

An ordination plot of a Principal component analysis (PCA) based on standardized and centered environmental data was used to visualize the relationship between environmental variables and wetland types during the dry and wet seasons. The overall effect of wetland type, season, and their interactions on environmental variables, plankton community composition, and Reynolds Functional groups was tested using redundancy analysis (RDA). The significance of RDA models was evaluated with 999 Monte Carlo permutations. The dependency of multiple observations within each wetland type was taken into account by restricting the permutations to blocks ( Supplementary Table S5 ). Prior to these analyses, environmental variables were standardized by subtracting their mean and dividing by their standard deviation, whereas plankton communities data were Hellinger transformed ( Legendre and Gallagher, 2001 ). RDA was also used to test the effect of environmental variables on the composition of plankton communities (ter Braak and Schaffers, 2004 ).

Linear mixed-effects models (LMMs) were constructed with the package lme4 version 1.1–27.1 ( Bates et al., 2011 ) to test for differences between wetland types and seasons for each environmental variable, local taxonomic richness, and Reynolds Functional groups. Wetland type and season were included as fixed factors in these analyses, while wetland ID was included as a random factor to take into account the temporal dependency of observations from the wet and dry seasons of each wetland. Distributional assumptions of linear models (normality and homoscedasticity of residuals) were checked for each response variable prior to the analyses. Statistical significances of the fixed factors were tested using Satterthwaite’s approximation method of the lmerTest R package version 3.1–3 ( Kuznetsova et al., 2017 ). Posthoc tests were implemented in the lsmeans R package version 2.30–0 ( Lenth and Lenth, 2018 ). The relative importance of fixed and random effects in LMMs was assessed using the marginal R 2 and conditional R 2 ( Nakagawa and Schielzeth, 2013 ).

To identify wetlands supporting a unique planktonic community, Local Contribution to Beta Diversity in plankton data sets (LCBD; ecological uniqueness of each wetland in their phytoplankton and zooplankton communities) was calculated following the method proposed by Legendre and De Cáceres (2013) . Wetlands with higher LCBD values exhibit substantial dissimilarity in species compositions and may have high or low species richness, which should be given more attention in terms of conservation ( Legendre and De Cáceres, 2013 ; da Silva Brito et al., 2020 ). In addition, calculations were extended to measure variability in plankton communities (BD Total ) and Species Contribution to Beta Diversity (SCBD) ( Legendre and De Cáceres, 2013 ). BD Total varies from zero (totally similar wetlands) to 1 (dissimilar wetlands). The SCBD value denotes the relative importance of each species in affecting BD Total patterns. LCBD, SCBD, and BD Total were calculated for each wetland type and the entire study region from Hellinger transformed plankton abundance data using the beta. div function in the adespatial R package version 0.3–14 ( Dray et al., 2018 ). The significant difference in individual LCBD (each wetland’s contribution to BD Total ) was tested by permutation (999 runs) for the entire study region according to the procedures described in Legendre and De Cáceres (2013) . The significant difference in overall LCBD (wetland types’ contribution to BD Total ) was tested using the linear mixed effect model. Wetland type and season were included as fixed factors in these analyses, while wetland ID was included as a random factor to take into account the temporal dependency of observations within each wetland type. We used beta regression as our modeling tool to investigate the relationship between LCBD and environmental variables, LCBD, and species data, SCBD and sampling sites, and SCBD and species data because LCBD and SCBD varied between 0 and 1 ( Zeileis et al., 2010 ). We used beta regression with logit link function from the betareg R package version3.1-4 ( Zeileis et al., 2010 ) for three separate models. First, we related LCBD to species richness, community abundance, and their quadratic terms. Second, we ran beta regression of LCBD using ten environmental variables as predictors (i.e., pH, specific conductance, water temperature, water depth, turbidity, sediment depth, chlorophyll-a, total nitrogen, total phosphorus, and DO). Third, we used beta regression to relate LCBD to the species richness and community abundance (TotAbu). Forth, we used beta regression to relate SCBD to the number of sites occupied (NumSit), the species abundance (SpeAbu), and their quadratic terms.

All basic statistics were conducted in R ( CoreTeam, 2020 ; Version 3.6.3), ggpubr R package version 0.4.0 ( Kassambara, 2020 ). Figures were produced using the ggplot2 R package version 3.3.5 ( Wickham, 2016 ).

Environmental Variables

RDA analyseis revealed that wetland type and season both had a significant effect on the entire set of investigated environmental variables ( R 2 adj: = 38.6, 4.13%, respectively; Table 1 ). The first and the second PCA axis jointly explained 54.42% of the overall variation in environmental variables ( Figure 3 ). The first axis explained 33.57% of the variation and was positively associated with water depth, and negatively with TP and pH, generally separating the riverine papyrus swamps from the other wetland types. The second axis explained 20.85% of the variation of the environmental variable and was closely associated with turbidity, specific conductance, and sediment depth. The linear mixed model revealed that, except for chlorophyll-a and total nitrogen, the measured environmental variables differed significantly among wetland types ( Supplementary Table S2 ). The concentration of dissolved oxygen, pH and concentration of total phosphorus were significantly higher in river mouth wetlands compared to riverine papyrus swamps and lacustrine wetlands ( Figures 4B, D, I ). Water temperature was significantly higher in river mouth wetlands compared to riverine papyrus swamps ( Figure 4C ). Turbidity was significantly higher in river mouth wetlands compared to riverine papyrus swamps and lacustrine wetlands ( Figure 4E ). Riverine papyrus swamps had significantly higher sediment depth and electrical conductivity (expressed as specific conductance) compared to lacustrine wetlands ( Figures 4F,G ). Water depth was significantly higher in riverine papyrus swamps compared to river mouth wetlands ( Figure 4H ). We also observed considerable differences in water temperature and pH between seasons and the values were higher during the dry season ( Supplementary Table S2 ). Overall, fixed factors ( R 2 marginal) explained more variance in local environmental conditions than random factors ( R 2 condition- R 2 marginal). However, for chlorophyll-a and total nitrogen concentration, the random factor (Wetland ID) was relatively more than fixed factors ( Supplementary Table S2 ).

TABLE 1 . Results of redundancy analysis (RDA) testing for the effect of wetland types, season, and their interactions on the environmental variables, plankton community composition, and Reynolds functional groups.

FIGURE 3 . Biplots of Principal Component Analysis (PCA) of environmental variables in relation to different wetland types during the dry and the wet seasons. pH, SD sediment depth , WD water depth , Chl chlorophyll-a, EC specific conductance , Turb turbidity , DO dissolve oxygen concentration , and Temp temperature.

FIGURE 4 . Boxplot plots with the median (solid line) for each measured environmental variable (A–J) in R = riverine papyrus swamps, RL = river mouth wetlands, and L = lacustrine wetlands (data were combined from dry and wet seasons). pH, SD sediment depth , WD water depth , Chl chlorophyll-a, EC specific conductance , Turb turbidity , DO dissolve oxygen concentration , and Temp temperature. Boxes that do not have a letter in common are significantly different from each other.

Local, and Regional Taxonomic Richness in Phytoplankton and Zooplankton

A total of 87 phytoplankton taxa belonging to seven main divisions, namely Chlorophyta (43 taxa), Bacillariophyta (25 taxa), Cyanophyta (10 taxa), Euglenophyta (3 taxa), Dinoflagellates (3 taxa), Xanthophyta (2 taxa), and Cryptophyta (1 taxon) were identified as regional taxon richness ( Supplementary Table S3 ). The linear mixed model revealed that phytoplankton local taxonomic richness differed significantly among wetland types ( Supplementary Table S2 ; Figure 5A ). Seasonal variation, on the other hand, was insignificant ( Supplementary Table S2 ). The overall mean local taxonomic richness of phytoplankton (data were combined for the dry and the wet seasons) was 29 taxa (25-34 taxa) in the lacustrine wetlands, 30 (20-44 taxa) in the riverine papyrus swamps, and 21 (14-31 taxa) in the river mouth wetlands.

FIGURE 5 . Boxplot with the median (solid line) for local taxonomic richness (number of taxa) of phytoplankton (A) and zooplankton (B) in R = riverine papyrus swamps, RL = river mouth wetlands, and in L = lacustrine wetlands. Data were combined for the dry and the wet season. Boxes that have no letter in common significantly differ from each other.

A total of 57 zooplankton taxa representing Cladocerans (24 taxa), Copepods (9 species), and Rotifers (24 taxa) were identified ( Supplementary Table S4 ). Copepods were composed of Cyclopoids and one Calanoid species, i.e., Thermodiaptomus galebi lacustris . The linear mixed model revealed that zooplankton local taxonomic richness differed significantly among wetland types and seasons ( Supplementary Table S2 ; Figure 5B ). The overall mean local taxonomic richness of zooplankton (data were combined the dry and the wet seasons) was 21 taxa (13-31 taxa) in the lacustrine wetlands, 27 (25-37 taxa) in the riverine papyrus swamps, and 13 (9-17 taxa) in the river mouth wetlands.

Plankton Community Composition

The redundancy analysis (RDA) revealed a significant variation in plankton community composition and Reynolds Functional groups among wetland types and seasons ( Table 1 ). The RDA analyses revealed a significant effect of wetland types on phytoplankton community composition ( R 2 adj: = 37.9%; Table 1 ). The season had a minor impact, despite being statistically significant (R2 adj: = 0.45%). The first and second axes of the RDA based on phytoplankton, explained 36.62% of the total compositional variation in phytoplankton community ( Figure 6A ). The first axis explained 21.15% of the total variation and mainly separated river mouth wetlands with high turbidity from lacustrine wetlands and riverine papyrus swamps. The second axis, which explained 15.45% of the total variation, separated riverine papyrus swamps with deep water levels from the lacustrine and river mouth wetlands. Although, the majority of phytoplankton taxa showed a positive association with lacustrine wetland types, Aulacoseira italica (AI), Mougeotia laetevirens (ML), Navicula cryptocephala (NAC), Nitzschia minuta (NM), Spirogyra fluviatilis (SF), Synedra ulna (SU), Pediastrum simplex (PS), and Tribonema minus (TM) tended to be slightly more abundant in riverine papyrus swamps. In river mouth wetlands, Cymbella minuta (CM), Gomphonema minutum (GM), Fragilaria capucina (FC), Gomphonema gracile (GG), Nitzschia reversa (NR), Rhoicosphenia abbreviate (RA), and Rhopalodia gibba (RG) were more abundant.

FIGURE.6 . Triplots of Redundancy analysis (RDA) for phytoplankton taxa (A) , zooplankton taxa (B) and Reynolds Functional Groups (C) in L = lacustrine wetlands (orange color), R = riverine papyrus swamps (blue color), and RL = river mouth wetlands (red color) during the dry season (circle) and the wet season (triangle); pH, SD sediment depth, WD water depth, EC specific conductance, Turb turbidity, DO dissolve oxygen concentration, and Temp temperature. The full names of the plankton taxa indicated as code are given in Supplementary Tables S3, S4 .

The phytoplankton species were grouped into 18 Reynolds functional groups ( Supplementary Table S3 ). The functional groups B, MP, N, P, J, and TB accounted for more than 85% of the total phytoplankton abundance. Group B represented by the diatom Aulacoseira italica was the most dominant species comprising 29.4% of the total abundance followed by groups P (15.6%), J (13.71%), MP (12.11%), N (12.02%), and TB (5.97%). Differences in Reynolds functional groups abundance were especially strong between wetland types (R 2 adj: = 59.8%), whereas season explained a relatively small proportion of variation ( R 2 adj: = 3.3%; Table 1 ). The first and the second RDA axis jointly explained 48.99% of the overall variation in Reynolds functional groups abundance ( Figure 6C ). The first axis comprised 40.21% of the compositional variation in the Reynolds functional groups. The first axis clearly differentiated the river mouth wetlands from the lacustrine wetlands and riverine papyrus swamps ( Figure 6C ). Groups B and MP were dominant in river mouth wetlands (towards the positive side of the first axis), which was correlated with turbidity. Groups N and P were dominant in lacustrine wetlands (towards the negative side of the first axis), which was correlated with TP and TN concentrations. The second RDA axis accounted for 8.78% of the variation in Reynolds functional groups. Linear mixed models revealed that the abundances of groups B, J, N, MP, and P differ significantly among wetland types, while only group TB significantly varied between seasons ( Supplementary Table S2 ). The group MP was significantly higher in river mouth wetlands compared to lacustrine wetlands ( Figure 7A ). The group TB did not differ between wetland types ( Figure 7B ). Group B was significantly higher in riverine papyrus swamps compared to lacustrine wetlands ( Figure 7C ). The group N was significantly higher in lacustrine wetlands compared to river mouth wetlands ( Figure 7D ). The groups P and J were slightly higher in lacustrine wetlands compared to the other two wetland types ( Figures 7E,F ). Except for group B, where wetland identity was more relevant, wetland types and season (Marginal R 2 ) explained more variance in most RFGs ( Supplementary Table S2 ). The habitat preferences, representative taxa, tolerance, and sensitivity of dominant phytoplankton RFGs in the studied wetland types are shown in Table 2 .

FIGURE 7 . Boxplot with the median (solid line) for the abundance of Reynolds functional groups (A–F) . Data from the dry and wet seasons were combined. Boxes that have no letter in common significantly differ from each other.

TABLE 2 . Description of the main phytoplankton RFGs (with>3% contribution) in wetlands of lake Tana and representative taxa ( Reynolds, 2002 ; Padisák et al., 2009 ).

Zooplankton community composition differed between wetland types (R 2 adj: = 23.6%) and season. However, season only explained a relatively small proportion of variation ( R 2 adj: = 9.4%; Table 1 ). The first and the second RDA axis jointly explained 34.56% of the overall variation in zooplankton community composition ( Figure 6B ). The first axis comprised 19.51% of the compositional variation in the zooplankton community and differentiated riverine papyrus swamps from the lacustrine and river mouth wetlands. Zooplankton taxa associated with these riverine papyrus swamps were Acroporus harpae (AH), Alona spp. (AL), Chydorus spp. (CH), Ectocyclops rubescens (ER), Mesocyclops kieferi (MK), Microcyclops varicans (MV), Macrothrix triserialis (MT), and Kurzia longirostris. In river mouth wetlands, Brachionus caudatus (BC), Brachionus diversicornis (BD), Brachionus quadridentatus (BQ), and Branchionus calyciflorus (BrC) tended to be slightly more abundant during the dry season, while Thermocyclops ethiopiensis (TE), Thermodiaptomus galebi (TG), Keratella tropica (KT), Lecane bulla (LB), and Lecane luna were abundant during the wet season.

Local Contribution and Species Contribution to Beta Diversity (BD Total ) of Plankton

The total beta diversity (BD Total ) of phytoplankton for the entire data set was 0.61 and 0.62 during the dry and the wet season, respectively ( Supplementary Table S6 ). LCBD values of phytoplankton for the entire data set ranged between 0.05 and 0.12 during the dry season and between 0.06 and 0.11 during the wet season. Lacustrine wetlands’ contribution to phytoplankton LCBD was significantly higher compared to riverine papyrus swamps ( Supplementary Table S2 ; Figure 8A ). For all data, the Zegie-yiganda lacustrine wetland was statistically significant for single wetland contribution (LCBD) of phytoplankton (P.adj = 0.037). The BD Total of zooplankton for the entire data set was 0.56 and 0.47 for the dry and the wet seasons, respectively ( Supplementary Table S6 ; Figure 8B ). LCBD of zooplankton for the entire data set ranged between 0.06 and 0.15 during the dry season and between 0.07 and 0.10 during the wet season. Although three of the four wetlands of riverine papyrus swamps (Amba-giorgis, Dehna-mesenta, and Legdiya) contributed more than the average to zooplankton BD Total (LCBD), there was no significant difference between wetland types ( Supplementary Table S2 ; Figure 8B ). For all data, the Megech river mouth wetland was statistically significant for single wetland contribution (LCBD) of zooplankton (P.adj = 0.003).

FIGURE 8 . Boxplot with the median (solid line) for Local Contribution to beta diversity (LCBD) values for phytoplankton data (A) and zooplankton data (B) in R = riverine papyrus swamps, RL = river mouth wetlands, and L = lacustrine wetlands. Data were combined for the dry and the wet season. Boxes that have no letter in common significantly differ from each other.

The LCBD of phytoplankton data was significantly related to pH (Estimate = 0.27; z = 2.08, p = 0.04), whereas the LCBD of zooplankton data was significantly related to water depth (Estimate = 0.00; z = 2.76, p = 0.005) and sediment depth ( Table 3 ; Estimate = 0.00; z = 2.41, p = 0.02). Also, pseudo R 2 values of these models were moderate, 56% of the variation in LCBD of phytoplankton and 41% in LCBD of zooplankton was explained by all environmental variables included ( Table 2 ). However, there was no significant relationship between LCBD values and taxa richness, abundance, or quadratic terms in either of the plankton communities ( Supplementary Table S7 ).

TABLE 3 . Results of beta regression analyses when the response variable, local contributions to beta diversity (LCBD), was explained by local environmental variables.

The SCBD values for the entire data set of phytoplankton ranged from 0 to 0.108 and 15 out of 80 phytoplankton taxa had an above average (0.0125) contribution to BD Total during the dry season. The SCBD for the wet season ranged from 0 to 0.12, with 21 out of 81 phytoplankton taxa contributing more than the average ( Supplementary Table S8 ). Zooplankton SCBD values for the entire data set during the dry season ranged between 0 and 0.08, and 15 out of 55 zooplankton taxa contributed above the mean (0.018) to BD Total . SCBD during the wet season ranged between 0.00027 and 0.074, and 11 out of 57 zooplankton taxa contributed above the average (0.017) ( Supplementary Table S6 ).

The SCBD of phytoplankton was related to the number of sites occupied and its quadratic term, which accounted for 29% of the variation, whereas abundance and its quadratic term accounted for 62% of SCBD variation of phytoplankton ( Table 4 ). SCBD of Zooplankton was found to be significantly related to the number of sites occupied and its quadratic term, which accounted for 47% of the variation. The SCBD of Zooplankton was also significantly related to abundance and its quadratic term, which accounted for 43% of the variation ( Table 4 ).

TABLE 4 . Results of beta regression analyses when species contributions to beta diversity (SCBD) was explained by species abundance, site occupancy, and their quadratic terms.

Wetlands in Lake Tana are heterogeneous, as evidenced by differences in environmental conditions such as the water depth, pH, dissolved oxygen, and turbidity of the water, as well as by the differences in the local richness and composition of the planktonic communities in the different wetland types. These differences could be attributed to differences in local habitat conditions as well as in variation in hydrological conditions between wetlands. In our study, river mouth wetlands were highly turbid, warmer, and shallower, whereas riverine papyrus swamps and lacustrine wetlands were characterized by extended areas of emergent macrophytes, low turbidity, and deeper water levels.

The observed lower phytoplankton local taxa richness in river mouth wetlands compared to riverine papyrus swamps and lacustrine wetlands might be related to the higher turbidity in the former wetland type. Increased turbidity results in lower light availability for wetland phytoplankton ( Allende et al., 2009 ), which in turn might select for a lower number of taxa specifically adapted to low light intensities. This is in line with earlier studies ( Sharma and Singh, 2018 ; Gogoi et al., 2019 ), which found low phytoplankton richness in turbid aquatic ecosystems. Many authors suggested that water depth is the primary hydrological factor influencing phytoplankton species richness and community composition in wetlands ( Casali et al., 2011 ; Chaparro et al., 2018 ; Xiao et al., 2020 ). This is because water depth influences the wetland’s persistence, stability, and size ( Chaparro et al., 2018 ), and local environmental conditions ( Xiao et al., 2020 ), which, in turn, have an impact on phytoplankton communities. Our findings revealed that water depths were shallower in river mouth wetlands than in riverine papyrus swamps and lacustrine wetlands, which might be another reason for the lower phytoplankton richness in the former wetland type. Similarly, phytoplankton species richness was found to be lower during the low water phase than during the high water phase in one of Brazil’s wetlands ( Cardoso et al., 2012 ). This, however, contradicted the broader paradigm, which holds that low water levels in wetlands result in heterogeneous microhabitats as the wetlands become more isolated from one another and disconnected from the main river channel ( Tockner et al., 2000 ). In addition to variation in water depth, the presence of macrophytes in riverine papyrus swamps might have benefited the shade-tolerant species of Bacillariophyta and small-sized species of Chlorophyta that were dominant in these wetlands ( Padisák et al., 2009 ). Macrophytes have also been shown to promote the growth of periphytic phytoplankton species such as Spirogyra fluviatilis , Mougeotia laetevirens, Aulacoseira italica , Gonatozygon monotaenium , and Oedogonium spp. ( Rodríguez et al., 2012 ), which were also dominant in the riverine papyrus swamps in our study.

The high local taxa richness of zooplankton in riverine papyrus swamps might be attributed to the varying water depth (30–300 cm depth) observed in these wetlands. In general, variation in water depth within wetlands promotes different niches that support many zooplankton species ( Adamczuk, 2014 ; Chaparro et al., 2018 ). Thus, various species can be seen along a water depth gradient, which can be related to UV exposure, thermal properties, and food resources associated with varying water depths. Here as well, the extensive macrophyte cover in riverine papyrus swamps might also provide a complex habitat (e.g., space and food resources) for zooplankton but especially for epibenthic generalists ( Bolduc et al., 2016 ; Gebrehiwot et al., 2017a ; Gogoi et al., 2018 ) such as Alona spp. Chydorus spp. and Macrothrix triserialis which were dominant zooplankton species in these wetlands. Epibenthic generalists attach to substrates such as underwater stems or leaf surfaces and their low activity makes them less likely to be detected by macrophyte-associated predators compared to planktonic species ( Gogoi et al., 2018 ).

The regional taxonomic richness in the plankton of the Lake Tana wetlands is difficult to compare with other wetlands because the number of species is strongly dependent on the number of samples analyzed and the taxonomic accuracy in the analysis. However, the 85 phytoplankton taxa found was higher than the 36 phytoplankton species in the wetlands of India’s Sundarbans wetlands ( Gogoi et al., 2019 ), 53 species in India’s Chatla wetlands ( Laskar and Gupta, 2013 ), and 36 species in Diyawannawa wetland of Sri Lanka ( Wijeyaratne and Nanayakkara, 2020 ). However, the regional phytoplankton taxa richness in our wetlands was lower than the 360 phytoplankton species in the Bhoj wetland of India ( Bhat et al., 2015 ), 97 species in of Pantanal wetland of South America ( Cardoso et al., 2012 ), and 200 species in Danube Riverine wetlands of Austria ( Chaparro et al., 2018 ). Similarly, the regional taxon richness of zooplankton in our study was higher than the 32 species reported from the wetland of Opa Reservoir ( Adebayo et al., 2021 ), but lower than the 128 species reported from the Yangtze River floodplain ( Lu et al., 2021 ).

In addition to differences in local taxonomic richness, we also found significant differences in plankton community composition, studied at the functional (RFG) and taxonomic level, and the zooplankton communities between the different wetland types. The phytoplankton in riverine papyrus swamps was dominated by Reynolds functional group B, which was represented by Aulacoseira italica ; a member of a cosmopolitan genus with a tychoplanktonic live style ( Denys et al., 2003 ; Bicudo et al., 2016 ). The phytoplankton communities in the river mouth wetlands were dominated by the MP group, which corroborates its presence in inorganically turbid lakes and rivers elsewhere ( Padisák et al., 2009 ; Stević et al., 2013 ; Udovic et al., 2014 ). This group was also dominant within the macrophyte stands in Lake Ziway ( Gebrehiwot et al., 2017b ), a lake that is highly turbid and heavily impacted by intensive agricultural activities and urbanization ( Gebrehiwot et al., 2017a ; Merga et al., 2020 ). In addition, species that predominantly contributed to the Reynolds functional group MP, such as Rhopalodia gibba , Gomphonema minutum , Rhoicosphenia abbreviate , Nitzschia reversa , Gomphonema gracile , Cymbella minuta, and Nitzschia closterium have been reported from running waters elsewhere ( Leira et al., 2017 ; Shen et al., 2018 ). The lacustrine wetlands are dominated by the J, P, and N groups which prefer shallow enriched water bodies with continuous or semi-continuous mixed layers of 2–3 m in thickness ( Reynolds, 2002 ). The zooplankton communities in the riverine papyrus swamps were dominated by the cladoceran functional groups. This is most likely due to the high density of macrophyte stands dominated by Cyperus papyrus , which may have served as a foraging and attachment site for non-active epiphytic zooplankton (e.g., Alona and Chydorus spp.; Choi et al., 2015 ). This is consistent with what was found in other studies in Lake Ziway ( Gebrehiwot et al., 2017a ) and Lake Tana ( Wudneh 1998 ). In the present study, a considerably higher diversity of rotifers was recorded in river mouth wetlands. The most abundant species found in these wetlands ( B. caudatus , B. diversicornis , B. quadridentatus , B. calyciflorus , K. tropica , L. bulla , and L. luna ) were cosmopolitan planktonic species that are generalists that feed on bacteria, detritus, and flagellates ( Yin et al., 2018 ; García-Chicote et al., 2019 ). Some of these species are also known to be tolerant of differences in temperature, dissolved oxygen, and turbidity ( Segers and De Smet, 2007 ). As a result, some of these species are expected to predominate in river mouth wetlands, which have higher turbidity, higher temperature, and low dissolved oxygen concentrations.

The phytoplankton in the lacustrine wetlands and particularly that in the Zegie-yiganda wetland, with its deep open water and diverse microhabitats, appeared to be relatively unique. The positive correlation between pH and LCBD values for phytoplankton in our study confirms that pH is one of the environmental factors that affect phytoplankton directly or through its influence on the bioavailability of nutrients ( Chakraborty et al., 2011 ). The zooplankton in the Megech river mouth wetland, but particularly the communities in the riverine papyrus swamps, were relatively unique. For the Megech river mouth wetland, this is due to its low richness and severely degraded state and therefore restoration actions are recommended. The zooplankton in the riverine papyrus swamps consisted of unique communities. This, together with the presence of unique taxa in waterbirds in these wetlands (e.g., Zelelew et al., 2020 ) underlines the need that these wetlands require special protection ( Brito et al., 2020 ). The LCBD values of zooplankton were found to be positively related to sediment and water depth. Indeed, water depth is likely to influence zooplankton community composition by altering the other environmental variables such as turbidity, dissolved oxygen, specific conductance, and nutrient concentrations, as well as the cover and structure of macrophytes in wetlands ( Chaki et al., 2021 ). Water depth in wetlands is known to be directly related to the establishment and survival of different macrophyte types ( Rolon et al., 2010 ), which are very important for zooplankton as a refuge, substrate, and food source, resulting in distinct zooplankton communities ( Nevalainen, 2012 ; Adamczuk, 2014 ). The positive relation between LCBD and sediment depth might be related to some taxa being present in the riverine papyrus swamps wetlands with deep sediments, such as several bottom-dwelling Cladoceran species ( Gogoi et al., 2018 ).

Finally, the effect of wetland type was more prominent than the effect of season on environmental variables, plankton communities, and RFGs. However, because only one-time sampling per season was used, the insignificance of the seasonal effect could be attributed to our under-sampling efforts.

Understanding local taxon richness, as well as variation in community composition, in combination with ecological uniqueness analysis (LCBD), could aid in the wetland restoration and conservation activities. The observed significant variation in plankton community composition among the three wetland types suggests that all the wetlands included in this study must be conserved, which may be difficult due to limited resources. Therefore, wetlands with a high plankton local taxon richness and a high LCBD (high ecological uniqueness) are worth special attention. Thus, conserving riverine papyrus swamps is critical for preserving the majority of phytoplankton and zooplankton taxa, as well as the most valuable sites. However, the taxa poor, degraded Megech river mouth wetland, which contributed significantly to zooplankton LCBD, requires restoration. Although we believe our findings can serve as a foundation for any conservation and restoration plans, we recommend more research is needed on the diversity, and community composition of other organisms in these wetlands to make better decisions and preserve the Lake Tana regional biodiversity.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author.

Author Contributions

AK contributed to data collection, analysis, and manuscript writing. AK, AW, IS, and PL designed the study and developed a data collection protocol. EV, IS, LT, and PL contributed to the critical revision of the manuscript for important intellectual content. EA, LDM, and MK contributed to the final version of the manuscript.

This study was financially supported by VLIR-UOS (ET2017IUC036A103).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The authors are grateful to Vlaamse Interuniversitaire Raad–Universitaire Ontwikkelingssamenwerking (VLIR-UOS) for financing this study. The first author was a recipient of an IUC (Institutional University Cooperation) Ph.D. scholarship from VLIR (ET2017IUC036A103) to carry out this work. Vrije Universiteit Brussel (VUB-BAS42) and Bahir Dar University are thanked for their financial and logistic support. We are grateful to Demeke Kifle of Addis Ababa University for his help in phytoplankton identification and for allowing us to work in his lab. Jacobus Vijverberg of the Netherlands Institute of Ecology (NIOO-KNAW) deserves special thanks for the zooplankton (the species Cladocerans and Copepods) identification. Two reviewers are thanked for their insightful comments on a previous version of the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at:

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Keywords: Lake Tana, Zooplankton, Phytoplankton, wetlands, community composition, species richness

Citation: Kahsay A, Lemmens P, Triest L, De Meester L, Kibret M, Verleyen E, Adgo E, Wondie A and Stiers I (2022) Plankton Diversity in Tropical Wetlands Under Different Hydrological Conditions (Lake Tana, Ethiopia). Front. Environ. Sci. 10:816892. doi: 10.3389/fenvs.2022.816892

Received: 17 November 2021; Accepted: 16 February 2022; Published: 09 March 2022.

Reviewed by:

Copyright © 2022 Kahsay, Lemmens, Triest, De Meester, Kibret, Verleyen, Adgo, Wondie and Stiers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Abrehet Kahsay, [email protected]

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The annual Cushing Prize honors the memory of David Cushing, Founding Editor of Journal of Plankton Research . The Cushing Prize distinguishes the best paper by an early career stage scientist (aged 30 or younger) published in the Journal of Plankton Research in the past year. The five Editors of the Journal of Plankton Research , among the published nominated submissions, choose the winning paper. The prize spotlights, and we hope helps to foster, interesting and high-quality papers by young scientists that David Cushing so actively supported.

For 2024 the David Cushing Prize for the best paper by an early career scientist published in 2023 has been awarded to Rose-Lynne Savage for her paper, “Symbiont diversity in the eukaryotic microbiomes of marine crustacean zooplankton” with co-authors Jacqueline L. Maud, Colleen T.E. Kellogg, Brian P.V. Hunt and Vera Tai. In her study, metabarcoding was used to characterize eukaryotes of the microbiomes of a variety of crusacean zooplankters: copepods, euphausids, amphipods and ostracods. While the prokaryotes have been studied, eukaryotes in their microbiomes have been relatively neglected. Reads associated with a variety of ciliate and dinoflagellate taxa were recovered, with most taxa showing some host specificity. Interestingly, reads from hydrozoans were found as well suggesting a new trophic pathway of crustacean zooplankton feeding on the remains of hydrozoans.

Rose-Lynne Savage is originally from Victoria in British Columbia (Canada). She completed her BSc degree in Biology at the University of British Columbia in Vancouver. Her favorite classes were in ecology and microbiology, in her own words, “especially Protistology”, explaining no doubt, her choice of a Masters thesis with Vera Tai at the University of Western Ontario on eukaryotic symbionts of crustacean zooplankton- the subject of her JPR article.

Rose-Lynne Savage is currently with the Hakai Institute, a private organization focused long-term ecological research in coastal ecosystems of British Columbia. She is working as a Genomics Technician and on her way to a career contributing to environmental and molecular research. At JPR we hope to see more papers by Rose-Lynne Savage. We wish her the best of luck in her endeavors and predict a bright future for her!


Rose-Lynne Savage, author of “Symbiont diversity in the eukaryotic microbiomes of marine crustacean zooplankton”, the winner of the 2024 Cushing Prize. The article was first published online on February 2, 2023 and appeared in issue 2 in 2023 ( J. Plankton Res ., 2023, 45(2): 338–359). It is available in Free Access:

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    Planktons ("planao" means to wander) are minute floating organisms, usually found in surface water. They are broadly divided into phytoplankton (plants category) and zooplankton (animal catego-ry). Zooplanktons are distinguished from phytop-lankton on the basis of morphology and mode of nutrition. Phytoplankton contain chlorophyll for ...

  25. 2024 David Cushing prize

    The annual Cushing Prize honors the memory of David Cushing, Founding Editor of Journal of Plankton Research.The Cushing Prize distinguishes the best paper by an early career stage scientist (aged 30 or younger) published in the Journal of Plankton Research in the past year. The five Editors of the Journal of Plankton Research, among the published nominated submissions, choose the winning paper.