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

• COLIN S. REYNOLDS’ LEGACY
• Review Paper
• Open access
• 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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Introduction

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.

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.

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.

Conclusions

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 - https://www.alpine-space.eu/projects/eco-alpswater ). 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). https://doi.org/10.1007/s10750-020-04332-9

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Temporal changes in plankton diversity in relation to hydrographical characteristics at Perumal Lake, Cuddalore District, Tamil Nadu, India

• Annamalai Aravinth 1 , 2 ,
• Radhakrishnan Kannan 2 ,
• Gunasekaran Chinnadurai 2 ,
• Narasimman Manickam 1 ,
• Piliyan Raju 1 ,
• Pachiappan Perumal 1 &
• Perumal Santhanam 1 , 3  

The Journal of Basic and Applied Zoology volume  84 , Article number:  13 ( 2023 ) Cite this article

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The fresh water environment supports the productivity of phyto- and zooplankters and fin and shell fishes. The rate of fish productivity of an aquatic ecosystem solely depends on the rate of plankton productivity, and which in turn critically depend on the concentration and variation of hydrographical features. The current investigation was focused on the distributional pattern of phyto- and zooplankton vis-à-vis physicochemical characteristics in Perumal Lake, Cuddalore District, Tamil Nadu State (India).

The hydrographical factors and phytoplankton as well as zooplankton diversity were studied at the monthly interval of 12 months by following the standard methods in freshwater of Perumal Lake from September 2018 to August 2019. Presently a total of 15 species of phytoplankton and 15 species of zooplankton were recorded in Perumal Lake. The present study reveals good variation in the hydrographical characteristics, such as temperature (24.2–30.1 °C), turbidity (10.4–43.2 NTU), total suspended solids (300.2–1800.8 mg/L), conductivity (3.25–10.54 mhos/cm), pH (6.92–8.2), total hardness (8.58–23.8 mg/L), dissolved oxygen (2.8–7.26 mg/L), dissolved carbon dioxide (0.96–13.2 mg/L), chloride (1.92–23.8 mg/L), nitrate (0.28–3.18 mg/L), sulphate (1.1–8.2 mg/L) and phosphate (0.19–3.34 mg/L).

Conclusions

The findings of the present study indicate that the temperature has influence on phytoplankton as well as zooplankton diversity of species. Regular monitoring of hydro-biological parameters is necessary to assess the health of the lake ecosystem.

The freshwater environment is represented by different ecosystems like lakes, rivers, ponds, streams, temporary puddles and thermal springs. Although freshwater environment accounts for a small portion of the world’s total aquatic part, the inland aquatic habitats shows far more heterogeneity in their physicochemical characteristics that houses unusually large portion of the world’s biodiversity. Lakes, both the natural and artificial, are the important freshwater ecosystem that have varied utilization. They support a variety of flora and fauna, viz. phytoplankton, macrophytes, zooplankton, benthos and nekton. The plankton are defined as the heterogeneous assemblage of organisms which float as well as passively drift along the course of water current of aquatic environments. The phytoplankton (plants) and zooplankton (animals) are the important components of aquatic environments. In view of their high sensitivity to the water quality changes, plankton are considered to be indicator species (Jena et al., 2017 ). The variation in the density and diversity of plankton is an important criterion to assess the health of aquatic ecosystems. Aquatic population is represented by many species (Manickam et al., 2020 ). The freshwater ecosystems are of great help for human welfare as they are the sole habitat for an extraordinarily rich endemic and sensitive biota (Jasmine et al., 2013 ).

The interactions between the physical and chemical properties of water play an important role in the abundance, distribution, diversity, growth, reproduction and the movements of aquatic organisms (Anne Rebecca, 2019 ; Deepak & Singh, 2014 ). Plankton are often used as indicator of environmental and aquatic health because of their high sensitivity to changes such as eutrophication and pollution. The plankton are divisible into two main groups, the phytoplankton and the zooplankton (Jena et al., 2017 ). Together with the various physicochemical characteristics of water and soil, such biotic communities form an interdependent and balanced ecological system. The hydrographical features of an aquatic environment have been found to be greatly influencing the biological productivity (Ahmed et al., 2013 ; Bais & Agarwal, 1990 ). The lakes are largely being used for the purposes of drinking, irrigation, fishing, eco-tourism, etc. (Bhatt et al., 2014 ). Generally, the lakes situated in urban areas are mainly used for recreational purposes like swimming, bathing and other water sports. Unfortunately, such aquatic ecosystems are also being used for the discharge of industrial and domestic wastes and thereby the degradation of the water quality considerably. The plankton productivity rate is determined by the physical and chemical parameters (e.g. temperature, light availability, micro- and macro-nutrients) of the water as well as the soil nature. Data on the abundance and diversity of plankton in relation to inorganic factors provide information of energy turnover of aquatic ecosystems (Forsberg, 1982 ). Damodharan et al. ( 2010 ) have stated the importance of zooplankton as live feed to fish larvae in wild. Hence, it is important to investigate the plankton resources of freshwater ecosystems (Kather Bee et al., 2015 ).

In view of the movement of nutrient from sediments to water column, bloom formation occurs (Ekholm and Mitikka, 2006 ). While the oligotrophic lakes are transparent and hypertrophic lakes are turbid, the shallow lakes (at intermediate nutrient concentrations) may exhibit either clear water or turbid state (Scheffer et al., 2001 ). Research studies on plankton were carried out in India since 1950 onwards (Rajashekhar et al., 2009 ). The present investigation pertains to the spatio-temporal variation of phyto- and zooplankton in relation to hydrography at Perumal Lake.

The study area, Perumal Lake, is situated in Cuddalore District, Tamil Nadu, with an area of 500 acres, and is 24 km east of Neyveli town and 16.7 km south of Cuddalore. The lake is also used for Agricultural and regular fishing by local fishermen. Totally, three sampling stations were covered, viz. ST-1 (11.5474° N; 79.6542° E), ST-2 (11.6143° N; 79.7017° E) and ST-3 (11.5806° N; 79.6754° E) ( Fig.  1 ). Monthly samplings were carried out for 1 year from September 2018 to August 2019.

Map showing the study area of Perumal Lake (Cuddalore District, Tamil Nadu, India)

Hydro-biological samplings

Plankton and water samplings were made every month’s 1st week (September 2018–August 2019). For zooplankton quantification, 100 L of water sample was filtered through conical-shaped plankton-net mesh size (150 µm). The samples were then taken to the laboratory and preserved with 5% formalin (Ajithamol et al., 2014 ).

Analyses of hydro-biological parameters

Various parameters like salinity, temperature, pH, electrical conductivity and total dissolved solids were analysed through a standard kit “µP based Water & Soil Analysis Kit Model 1160”. Winkler’s method was used to determine the primary production, and the light and dark bottle method was followed to estimate the dissolved oxygen and inorganic nitrate, phosphate, sulphate and chloride (Parsons et al., 1984 ; Strickland & Parsons, 1968 ). The plankton species were studied under the light microscope, and the identification was made by referring the standard works (Battish, 1992 ; Murugan et al., 1998 ; Altaff, 2004 ; Manickam et al., 2017 ). Phytoplankton counting was made by drop method and zooplankton quantification by employing Sedgwick Rafter’s cell. And 1 mL of sample was taken with a wide mouthed pipette and poured into the counting cell of the Sedgwick Rafter. After allowing for settlement, they were counted. Each plankton was counted five times, and the average value was obtained. The total number of plankton present in 1 L of water sample was calculated (Santhanam et al., 2019 ) by using the following formula: N  =  n  ×  v / V ; where N  = total number of plankton per litre of water filtered; n  = average number of plankton in 1 ml of plankton sample; v  = volume of plankton concentrated (ml); and V  = volume of total water filtered (litre).

Statistical analysis and diversity indices

The population of each group of zooplankton was expressed in average (number of individuals per litre). The data between zooplankton versus physicochemical characteristics were subjected to correlation and linear regression analysis using IBM-SPSS (v20.0). The different diversity indices such as species dominance ( D ), Shannon’s diversity index ( H ′), species evenness and species richness were calculated by using the PAST (Paleontological Statistics) software package (PAST, v2.02).

Hydrographical features

The water temperature was recorded at three stations for 12 months, and the results are given in Fig.  2 a. Water temperature varied from 24.2 to 29.9 °C at station1 followed by station 2 which ranged between 24.5 and 30.1 °C and at station 3, it ranged between 24.6 and 30.0 °C. The turbidity at station 1 was ranged between 10.46 and 38.6 NTU, and at station 2, it ranged between 11and 40 NTU. And at station 3, it varied from 11.2 to 43.2 NTU (Fig.  2 b). Total suspended solids (mg/L) were: 455–1820 (Stn.1), 320.2–1800.8 (Stn. 2) and 300.2–1790.8 (Stn. 3) (Fig.  2 c). The conductivity values ranges (mhos/cm) were: 3.4–10.54, 3.4–9.8 and 3.25–9.8 at stations 1, 2 and 3, respectively (Fig.  2 d). The average pH value recorded at station 1 was 7.48 followed by stations 2 and 3 with 7.53 and 7.44, respectively (Fig.  2 e). The total hardness values at station 1 were ranged between 8.58 and 22.8 mg/L, whereas at station 2 it was 9.8 and 22 mg/L and at site 3 it was 10 and 23.8 mg/L (Fig.  2 f).

Seasonal variations of physicochemical parameters in Perumal Lake during September 2018 to August 2019 (Temperature, Turbidity, Total Suspended Solids, Conductivity, pH and Total Hardness)

The dissolved oxygen concentration ranges (mg/L) were: 2.8–7.08, 3.4–6.9 and 3.6–7.26 at stations 1, 2 and 3 respectively (Fig.  3 a). Dissolved carbon dioxide (mg/L) fluctuated from 0.96 to 13.2. The values at station 1 were ranged between 0.96 and 12.1, whereas at station 2 it was 1.2 and 12.08 and at station 3 it was 1.6 and 13.2 (Fig.  3 b). Nitrate content (mg/L) varied from 0.28 to 3.20. The values varied from 0.36 to 3.2, 0.32 to 3.12 and 0.28 to 3.18 at stations 1, 2 and 3, respectively (Fig.  3 c). The ranges of sulphate values (mg/L) were: 1.12–7.24; 1.1–7.84 and 1.22–8.2 at stations 1, 2 and 3, respectively (Fig.  3 d), and correspondingly the chloride values were: 3.6–22.3, 1.92–22.3 and 10.0–23.8 (Fig.  3 e). Phosphate values (mg/L) ranged between 0.32 and 3.3, 0.25 and 3.25 and 0.19 and 3.1 at stations 1, 2 and 3, respectively (Fig.  3 f). The statistical - analytical values are presented in Tables 1 , 2 and 3 .

Seasonal variations of physicochemical parameters in Perumal Lake during September 2018 to August 2019 (Dissolved Oxygen, Dissolved Carbon dioxide, Nitrate, Sulphate, Chloride content and Phosphate)

The identified phytoplankton and zooplankton species and their month-wise occurrence are listed in Tables 4 , 5 , 6 for the stations 1, 2 and 3 respectively. Presently, totally 15 species of phyto- and 15 zooplankters were found (Tables 7 ,  8 ). The total plankton volumes recorded during the study period at all the stations are presented in Table 9 . The total count of phytoplankton (cells/m) at station 1 varied from 11 (February 2019) to 22 (August 2019) whereas at station 2 it was 18 (November) and 34 (July 2019) and at the sampling site 3 it was 12 (February 2019) to 32 (August 2019). The density of zooplankton (Ind/L) at station 1 fluctuated from 10 (February 2019) to 15 (September 2019), whereas at station 2 it was 14 (February) to 42 (August 2019) and at station 3 it was 16 (January 2019) to 24 (July 2019) (Fig. 4 ).

Seasonal variations of plankton abundance in Perumal Lake during September 2018 to August 2019

The intensity and interval of occurrence of rainfall cause significant changes in the physical and chemical properties of the freshwater environments. Also, temperature variations in freshwater systems can have a major impact on the other physicochemical characteristics. The presently observed temperature variations could be related to the changes in the heat intensity of the sun. The range of temperature is basically important for its influence on the various biochemical events in the aquatic organisms (Gupta et al., 2012 ). The values of hydrographical features were found be to fluctuate greatly, during the different months presently, which might have been due to the changing environmental/climatic conditions. (Devika et al., 2006 ) reported that the physical–chemical conditions exhibited positive co-relationship with the phytoplankton diversity of aquatic ecosystems. Such a type of variations of hydrography and phytoplankton was reported earlier by Manickam et al. ( 2018 ).

Anil et al. ( 2023 ) have reported the influence of various ranges of hydrographical parameters on the density and composition of plankton and the recorded high temperature during the summer season could have been den to the non-monsoonal clear sky (Gupta et al., 2012 ). The variations in the pH, O 2 , alkalinity and trace metals are having a major impact on phytoplankton productivity (Bais & Agarwal, 1990 ). The lake water’s alkalinity could be due to the presence of carbonates. Currently, very little pH variation has been recorded, and the changes in pH could be caused by algae photosynthesis (Das and Srivastava, 1956 ). Various human activities like detergent usage and release of untreated sewages are also contributing to the raised pH level. In this study, the recorded higher pH (in summer and pre-monsoon) accounts for good primary and secondary productivity. An earlier investigation revealed that dissolved oxygen possesses an indirect relation with temperature (Ashok et al., 2015 ). The rate of dissolving capacity of oxygen is inversely related with the intensity of temperature of aquatic ecosystems (Zhang et al., 2019 ). Further, the concentration of water–oxygen is also influenced by the rate of atmospheric pressure and photosynthetic rate (Singh et al., 2008 ).

The highest values of dissolved oxygen are generally coincided with the lowest temperature. Dissolved oxygen exhibited positive correlation with pH value, as reported earlier by Sukhija ( 2007 ). The recorded low summer DO was because of dissolving of organic matter and the respiratory process of zooplankton. Electric conductivity (EC) is a numerical value of the capacity of aqueous solution to convey electric current (Joseph, 2017 ). Presently, the EC was noticed in the range of 3.4–10.54 (Stn. 1), 3.4–9.8 (Stn. 2) and 3.25–9.8 (Stn. 3). And interestigly high values of phosphate (3.25 mg/L) and nitrate (3.2, mg/L) were found during the month of January to May 2019, as reported earlier by Joseph, ( 2017 ) in artificial pond. Higher levels of total dissolved solids can often indicate pollution by an extraneous source (Aboo and Manuell, 1967 ). Earlier, Daoudi et al. ( 2013 ) highlighted the significant relationship between the phytoplankton density and nutrient concentration especially during summer months. And the high concentration of nutrients like phosphorus and sulphate is responsible for the algal blooms formation as reported earlier by many researchers. The atmospheric events and anthropogenic activities are the causes for the observed/recorded variation in hydrography. The phytoplankton is initiating the aquatic food chain, and the higher trophic organisms like zooplankters as well as the fishes are depending on the rate of primary productivity. The presently recorded maximum density of phytoplankton in summer might be due to the maximum sunlight besides conducive temperature, as reported earlier by Murugavel and Pandian ( 2000 ), Hujare ( 2005 ). The role of light and temperature in determining the density of phytoplankton promotion has been reported earlier by Nazneen ( 1980 ). Additionally, the shallowness of the lake water and the high temperature induced water evaporation might have caused phytoplankton aggregation. Murulidhar and Murthy ( 2014 ) opined that the higher pH (8.0) is favourable for the growth of phytoplankton and such observations were earlier recorded by Ekhande et al. ( 2013 ) in Yashwant Lake Toranmal, Maharashtra, India. The pH of water could be changed with the changes in the climatological and vegetational factors as discussed earlier by Tokatli et al. ( 2020 ).

Presently, the temperature and nutrients are positively correlated with the total density of phytoplankton. Available literature indicates that temperature is an important determining factor for phytoplankton productivity (Unni & Pawar, 2000 ). However, the thermal tolerance of phytoplankton is species dependent as discussed earlier by Christensen et al. ( 2004 ). The monsoonal heavy rainfall caused water stratification along with turbidity and reduced temperature, was the reason for the recorded lesser phytoplankton productivity. During the investigation period, the Chlorophyceae species group was found to be the dominant quantitative component of phytoplankton. The recorded pre-monsoonal stable hydrographical features were largely responsible for the good production of chlorophycean algae, as reported by earlier researchers (Islam et al., 2001 ; Kumar and Sahu, 2012 ). And the algal production was found to be low in monsoon season due to the water dilution. The Chlorophyceae was the dominant group recorded now, which coincided with the predominantly recorded diatoms, as repeated earlier by Ambili ( 2013 ). In cooler environs, the green and cyanophycean algae replace the diatoms because of intense incoming nutrients along the catchment area (due to anthropogenic activity). In the present study, diatom dominated over green algae quantitatively, as reported earlier by Giriayappanavar and Patil ( 2010 ) who have found it at Belgaum and Wadral Lake, Wadral, Karnataka, India.

Zooplankton consumes phytoplankton and then transfers the energy to higher-level organisms like fishes. Hence, more investigations on the various aspects of zooplankton are most essential that would forecast the future fish productivity potential. Further, some of the zooplankters are considered to be good indicator species (to assess the health of the aquatic ecosystems). The abundance of zooplanktonic population of an area largely depends upon the density of phytoplankton coupled with conducive hydrographical factors, and thus, the zooplankton coordinates a food chain between the fishes and phytoplankton. Presently, the high abundance of zooplankton was recorded at Perumal Lake during the winter season when the species diversity of zooplankton was high. A similar population structure was earlier recorded by Krishnamoorthi and Selvakumar ( 2012 ) in Veeranam Lake and Sharma and Rama Kumari ( 2018 ) in Sacred Lake Prashar, Himachal Pradesh, India. Presently recorded zooplankton included the groups like copepods, cladocerans, ostracods and similar plankton distributional pattern was earlier reported by many researchers (Gorsky et al., 2010 ; Halder et al., 2008 ). And the recorded temporal variation in the plankton density could be related to the seasonal oscillation of the hydrographical parameters. And  the observed predominance of Copepods (in relation to rotifers) could be considered to be an acceptable water quality (Ravichandran, 2003 ; Sladeck, 1983 ). The results of the present study indicate that the Perumal Lake located in warm rural area is quite productive one with sufficient nutrient input (through monsoonal-rains), which can be utilized for planning aquaculture practices also.

Freshwater ecosystems are providing essential resources to humans, and they are the only home to a diverse range of endemic and sensitive biota. Aquatic organisms, particularly, the plankton, are the most sensitive component of such ecosystem, as they are sensitive to the environmental disturbances. Moreover, the primary production/phytoplankton serves as food for herbivorous animals and also serve as a biological indicator of environmental quality. In view of importance of zooplankton, more studies are essential for clear understanding of the process of ecosystems. Studies on the assessment of plankton diversity regularly in the freshwater system are critical for determining the status of water quality that supports the productivity of fishes.

Availability of data and materials

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Electrical conductivity

Total hardness

Dissolved oxygen

Dissolved carbon dioxide

• Phytoplankton
• Zooplankton

Total suspended solids

Nephelometric turbidity unit

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Acknowledgements

The authors are thankful to the Principal, Periyar Government Arts College, Cuddalore-607 001, and to the Head, Department of Marine Science, and the higher authorities of Bharathidasan University, Tiruchirappalli 620 024, for the necessary laboratory facilities provided. P.P and A.A are grateful to the UGC, New Delhi, for the grant of fellowship under the BSR Faculty Scheme (Ref. No.F.18-1/2011 (BSR), 26.06.2018).

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Annamalai Aravinth, Narasimman Manickam, Piliyan Raju, Pachiappan Perumal & Perumal Santhanam

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AA contributed to methodology, investigation, conceptualization and writing—review and editing—original, draft, RK was involved in writing—review and editing, data curation and formal analysis, and GC contributed to formal analysis, resources and data curation. NM, PR and PS were involved in writing—review and editing and data curation, and PP contributed to writing—review and editing, and data curation. All authors have read and approved the manuscript.

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Aravinth, A., Kannan, R., Chinnadurai, G. et al. Temporal changes in plankton diversity in relation to hydrographical characteristics at Perumal Lake, Cuddalore District, Tamil Nadu, India. JoBAZ 84 , 13 (2023). https://doi.org/10.1186/s41936-023-00337-7

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• Published: 16 August 2021

Decline in plankton diversity and carbon flux with reduced sea ice extent along the Western Antarctic Peninsula

• Yajuan Lin   ORCID: orcid.org/0000-0002-9057-9321 1 , 2 , 3 ,
• Carly Moreno   ORCID: orcid.org/0000-0002-3046-1014 4 ,
• Adrian Marchetti   ORCID: orcid.org/0000-0003-4608-4775 4 ,
• Hugh Ducklow   ORCID: orcid.org/0000-0001-9480-2183 5 ,
• Oscar Schofield 6 ,
• Erwan Delage 7 ,
• Michael Meredith 8 ,
• Zuchuan Li 1 , 9 ,
• Damien Eveillard   ORCID: orcid.org/0000-0002-8162-7360 7 , 10 ,
• Samuel Chaffron   ORCID: orcid.org/0000-0001-5903-617X 7 , 10 &
• Nicolas Cassar   ORCID: orcid.org/0000-0003-0100-3783 1 , 2  

Nature Communications volume  12 , Article number:  4948 ( 2021 ) Cite this article

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• Carbon cycle
• Marine biology
• Water microbiology

Since the middle of the past century, the Western Antarctic Peninsula has warmed rapidly with a significant loss of sea ice but the impacts on plankton biodiversity and carbon cycling remain an open question. Here, using a 5-year dataset of eukaryotic plankton DNA metabarcoding, we assess changes in biodiversity and net community production in this region. Our results show that sea-ice extent is a dominant factor influencing eukaryotic plankton community composition, biodiversity, and net community production. Species richness and evenness decline with an increase in sea surface temperature (SST). In regions with low SST and shallow mixed layers, the community was dominated by a diverse assemblage of diatoms and dinoflagellates. Conversely, less diverse plankton assemblages were observed in waters with higher SST and/or deep mixed layers when sea ice extent was lower. A genetic programming machine-learning model explained up to 80% of the net community production variability at the Western Antarctic Peninsula. Among the biological explanatory variables, the sea-ice environment associated plankton assemblage is the best predictor of net community production. We conclude that eukaryotic plankton diversity and carbon cycling at the Western Antarctic Peninsula are strongly linked to sea-ice conditions.

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Predicting global distributions of eukaryotic plankton communities from satellite data

Ocean-wide comparisons of mesopelagic planktonic community structures

Towards omics-based predictions of planktonic functional composition from environmental data

Introduction.

The Southern Ocean disproportionally contributes to the global climate system, accounting for almost half of the anthropogenic CO 2 and 75% of the heat uptake by the oceans 1 , 2 . The Western Antarctic Peninsula (WAP) system has exhibited some of the most significant changes in the Southern Ocean 3 , 4 , with rising air temperature up to 7 °C since 1950 5 , warming and freshening of the upper ocean 6 , warming of the deeper ocean 6 , deepening of the mixed layer depth (MLD) 7 , and the fastest sea ice decrease in Antarctica (Fig.  1 ) 8 , 9 . Whilst atmospheric warming trends at the Antarctic Peninsula have paused or even reversed in places since the end of the twentieth century, this is understood as natural interannual climate variability that is superposed on the longer-term trends 10 . There have been observed ecosystem changes throughout the entire Antarctic marine food web 7 , 11 , 12 , 13 , 14 . At the base of the food web, WAP eukaryotic plankton including phytoplankton and microzooplankton support higher trophic levels ranging from krill to penguins and whales 13 , drive biogeochemical cycles 15 , 16 , 17 , and regulate oceanic carbon uptake 7 . Thus, given the fundamental importance of eukaryotic plankton at the WAP, it is imperative to understand and predict the changes in plankton community structure, biodiversity, and carbon flux in this rapidly changing environment 18 .

a Location of the study area (red box) and the Palmer LTER sampling grid with hydrostations (blue dots). b Time-series of monthly averaged sea-ice area (SIA) anomalies for Palmer LTER sampling area from 1979 to 2017. Blue (red) bars represent negative (positive) SIA compared to a 39-year climatology for a particular month. SIA data were downloaded from Palmer LTER DataZoo ( http://pal.lternet.edu/data ). Cyan arrows highlight the sampling periods in this study.

The WAP system is characterized by a short but highly productive growing season during austral spring and summer 19 . The net community production (NCP) represents the balance between gross primary production and community respiration. When the organic carbon pool at the mixed layer is under a steady state, the net carbon flux in (i.e., NCP) equals the net carbon flux out (i.e., carbon export). Therefore, NCP reflects the amount of organic carbon available for export out of the surface MLD.

Here, we analyze five years of high-resolution NCP and high-throughput DNA sequencing data to explore the contribution of polar eukaryotic plankton to biological carbon fluxes. We show that among the considered environmental factors (iron not included), SST and sea-ice condition are strong predictors for community structure and NCP. We find that biodiversity is reduced when SST is high at the WAP. Finally, in order to improve NCP predictions, we build machine-learning models including in-depth community structure, community co-occurrence patterns, and physical conditions. Among the top-performing NCP models, a sea-ice associated plankton assemblage is a key predictor, with central (i.e., most connected) taxa identified as Thalassiosira , Odontella , Porosira , Actinocyclus , Proboscia , Chaetoceros, and Gyrodinium . The combination of biogeochemical tools and DNA metabarcoding sheds a unique insight into environmental forcings, plankton diversity, community structure and interaction, and biological carbon flux variability in a rapidly changing polar environment.

Results and discussion

O 2 /Ar-based in situ NCP observations at the WAP in austral summer from 2012 to 2016 demonstrate substantial spatial heterogeneity and interannual variability (Fig.  2 ). This was a period of moderately positive sea-ice area (SIA) anomalies following a more prolonged period of anomalously low SIA (Fig.  1 ). During the summer, NCP was highest in the shelf zone and decreased offshore, which is consistent with previous ship-based 15 , 20 and satellite-based observations 19 . In addition, the observed NCP exhibited marked interannual variability related to ice conditions. The two years that feature late sea-ice retreat (2014 and 2016) were associated with abnormally high summer NCP (t-test, p < 0.0001) (Fig.  2 ). In previous studies, elevated NCP or primary production under high sea-ice conditions were attributed to ice-melt enhanced water column stability, thus higher light availability, and potential iron supplied by sea ice 7 , 20 . From a decadal study considering climate oscillations 13 , bloom-favorable conditions at the WAP have been linked to negative winter and spring phases of the Southern Annular Mode (SAM), the dominant mode of extra-tropical climate variability in the Southern Hemisphere 21 . Negative SAM leads to increased ice extent in winter, restricting deep mixing, and then enhanced ice-melt in spring/summer, resulting in intensified stratification.

January averaged sea-ice concentrations derived from passive microwave satellite measurements ( top ). Red contours represent the biologically relevant ‘ice-edge’ defined as an ice concentration threshold of 5%. Underway estimates of NCP using O 2 /Ar method from the annual PAL-LTER sampling cruises along the WAP grids ( bottom ).

Eukaryotic plankton, including phytoplankton and microzooplankton, are key drivers of carbon fluxes at the WAP 16 . Based on a five-year WAP DNA sampling, we explored the plankton community structure and diversity via sequencing of the 18S rRNA gene marker. At the phylum level, four eukaryotic plankton dominated the WAP surface water, including diatoms (25.0%), cryptophytes (23.0%), dinoflagellates (19.6%), and haptophytes (11.3%) (Fig.  S3 ). Other eukaryotic plankton groups, mostly heterotrophic protists, contributed less than 5% of the 18S reads. Community composition differed substantially between years with high and low sea ice extent (Fig.  S4 ). For the years 2014 and 2016 with high sea ice, eukaryotic plankton communities comprised on average 39.5 ± 3.0% diatoms, 20.5 ± 1.3% dinoflagellates, 15.1 ± 6.1% cryptophytes, and 7.1 ± 2.4% haptophytes. In contrast, for warm years with less sea ice in 2012, 2013, and 2015, eukaryotic plankton communities comprised on average 28.9 ± 8.3% cryptophytes, 19.0 ± 1.6% dinoflagellates, 14.4 ± 4.4% haptophytes, and 14.0 ± 1.7% diatoms, all significantly different from cold years (two-sided t-test, p < 0.0001).

At the finest taxonomic resolution, 2480 amplicon sequence variants (ASVs) were identified from the five-year amplicon dataset (119 samples). Canonical correspondence analysis (CCA) illustrates that either ice conditions or SST is the dominant driver on community structure at the ASV level (Fig.  3 ). The first axis CCA1 (17.5% of the variance) separates samples from late (2014 and 2016) and early (2012, 2013, and 2015) ice-retreat years. The most substantial abiotic factor associated with CCA1 is SST (negatively correlated), and the most substantial biotic factors associated with CCA1 are Chl and biological O 2 (both positively correlated), consistent with ice-melt enhanced biomass and productivity. Freshwater inputs were estimated from oxygen isotope signatures. Low salinity, as well as high fractions of sea-ice melt and meteoric water, are also associated with CCA1 but to a lesser extent than SST. CCA2 (7.3% of the variance) separates mainly the offshore and nearshore samples, with distance to coast (X grid) and MLD being the top two associated environmental factors. CCA3 (5.7% of the variance) indicates community differentiation along the north-to-south gradient (Y grid), potentially reflecting a long-term ice retreat impact on communities and/or a north-to-south climate gradient along the WAP 11 , 19 ; CCA3 could also reflect interannual variability in sea ice extent. Overall, sea-ice conditions and associated environmental parameters, such as low SST (Fig.  S1 and S2 ) and low salinity, are the primary drivers of community differentiation at the ASV level.

Each point represents the eukaryotic plankton community composition from a surface ocean sample with year indicated by color and station type (i.e., offshore, nearshore north, or nearshore south) indicated by shape. CCA1 and 2 are depicted ( a ) as well as CCA1 and 3 ( b ). Vectors indicate stepwise selected environmental constrains, both biotic and abiotic, with factor names marked at the end. Acronyms for selected environmental factors: SST – sea surface temperature, SiO4 – silicate concentration, mldst – mixed layer depth defined by potential density, XgridCal – X grid or grid station calculated from GPS, YgridCal – Y grid or grid line calculated from GPS, f sim – fraction of sea-ice melt estimated from δ 18 O, f met – fraction of meteoric water estimated from δ 18 O, Chl – chlorophyll concentration, o2ar – biological oxygen supersaturation. Source data are provided as a Source Data file.

To further investigate the effect of temperature as one of the major abiotic factors influencing polar plankton composition 22 , we examined the temperature effect on biodiversity (ASV based) using three diversity indices, Chao1, Pielou’s evenness, and Shannon. Chao1, a measure for species richness, demonstrated an evident decline towards higher SST (Fig.  4 ), with a 40% decrease in the index for a 4 °C rise in SST. Pielou’s evenness and Shannon, which consider both species richness and evenness, also decreased significantly with increasing SST. It indicates that communities in warmer WAP waters show lower richness and lower evenness, i.e., that a few taxa dominate. Interestingly, in a recent global analysis on plankton biodiversity from Tara Oceans 23 , the temperature was also identified as the major explanatory factor for global-scale eukaryotic plankton biodiversity estimated by the Shannon index, but with the opposite trend, i.e., a decreased diversity towards higher latitude or lower temperature. We note that whilst the Tara Oceans dataset represents the most comprehensive oceanic DNA sampling efforts to date, it featured only limited sampling in the Southern Ocean (three data points), and it did not include a longitudinal survey that captures the mesoscale effect of changing temperature on a given community; our five-year WAP sample collection hence complements well the Tara Oceans observations for the previously under-sampled Southern Ocean. One explanation for the unexpected high biodiversity observed under low temperature at the WAP is that the ice-associated plankton communities consist mainly of diatoms (Fig.  S4 ), which are highly diverse and can thrive at lower temperatures compared to other phytoplankton 24 . As a unique longitudinal test case (e.g., as documented in 25 ), our results suggest that global warming may decrease plankton diversity in coastal Antarctica.

Three diversity indices, including Chao1 ( a ), Pielou’s evenness ( b ), and Shannon ( c ), plotted against SST, with year indicated by color and station type indicated by shape. Reads from each sample were rarefied to an even depth. Solid red lines represent linear fittings and the gray bands represent 95% confidence intervals. All three diversity indices show significant negative correlations with SST (two-sided t-test, p < 0.01). Source data are provided as a Source Data file.

Due to the high dimensionality of the ASV dataset, it is challenging to model NCP based on community structure. Thus, we applied a weighted gene correlation network analysis (WGCNA) approach to delineate clusters of 18S ASVs into subnetworks or modules (Fig.  S5 ) 26 , 27 . This approach allows us to reduce the total number of variables while preserving information on ASV abundances and potential interactions 28 . In total, we identified 12 modules from the five-year global community structure (Fig.  S5 ). Each module represents an assemblage of predicted highly interconnected plankton community members, potentially indicating a group of organisms with strong ecological overlap and/or interactions 29 . The eigenvalue of each module represents the overall abundance of the assemblage. In addition, in order to investigate the niche partitioning of the different community assemblages or modules, correlation analysis was performed in WGCNA to link them to different abiotic and biotic factors (Fig.  S5 ). Next, we applied Genetic Programming (GP), a machine learning approach based on evolution computation 30 , 31 , to generate and parameterize statistical models that predict NCP based on the WGCNA generated bio-assemblages ( n = 12) and physical factors ( n = 6) (see “Methods” for a detailed list). The relationships between carbon-based plankton biomass, their physiology (indirectly modeled as functions of environmental factors), and biogeochemical rates are often non-linear and could involve multiple layers of interactions. GP allows us to capture the complex and non-linear relationships between these different factors to predict NCP without an a priori assumption. The overall idea of this modeling approach is that biogeochemical rates are a function of (i) the community composition and abundance; and (ii) the specific metabolic rates regulated by environmental factors, such as the photosynthesis-irradiance curve and the productivity−temperature ( Q 10 ) relationship. The top four GP solutions (Table  S2 , ranked by mean square error (MSE)) provide good predictions on NCP with R 2 ranging from 0.70 to 0.80. Among the explanatory variables selected by the models, the top two physical factors ranked by selection frequency are SST and MLD, followed by surface photosynthetically active radiation (PAR). This suggests that temperature and light are likely the primary physiological limiting factors on NCP in the WAP system. As an alternative, SST could be an indirect proxy for time since ice-retreat, i.e., higher SST indicates a longer time after the initial ice retreat.

Among the community-assemblage factors in GP solutions, module turquoise (MET) is the most important predictor for NCP. MET is also the largest module identified, which consists of 126 ASVs, mainly representing diverse groups of diatoms and dinoflagellates (Fig.  5a ; Supplementary Data  2 ). The central nodes in the MET network, which represent the top-10 most connected ASVs or the central ASVs for the network structure 32 , include the diatom genera Thalassiosira , Odontella , Porosira , Actinocyclus , Proboscia , Chaetoceros , and the dinoflagellate Gyrodinium . MET appears in all top-four performed GP solutions, and it is the sole biological factor in solution 1 explaining a majority of the NCP variability ( R 2 = 0.70) (Table  S2 ). The overall spatial distributions of MET are consistent with the January averaged sea-ice distribution estimated by satellite (Fig.  5c ). In the WGCNA correlation analysis (Fig.  S5 ), MET is significantly correlated with low SST ( R = −0.51, p = 1 × 10 −8 ), low salinity ( R = −0.48, p = 8 × 10 −8 ), shallow MLD ( R = −0.36, p = 1 × 10 −4 ), elevated sea-ice melt ( R = 0.33, p = 3 × 10 −4 ) and meteoric freshwater ( R = 0.34, p = 3 × 10 −4 ). Although sea-ice melt and meteoric freshwater both show positive effects on MET, they could exert this through different mechanisms. Because iron concentrations were not included in this analysis, we cannot discern whether sea ice and/or glacial ice melt impact productivity through altering light and/or iron levels by increased stratification or fertilization. However, according to a recent study at the eastern Antarctic Peninsula, sea-ice melt could mostly influence carbon fixation through water column stabilization, while the effect of glacial melt could be through providing a significant amount of iron to the system 33 . In the Southern Ocean, photosynthetic efficiency (Fv/Fm) varies with an iron status where lower values suggest iron stress and higher values of iron sufficiency 34 . Previous studies have found NCP to be positively correlated with Fv/Fm 35 . Although no direct iron measurements were made in this study, iron availability being a first-order factor regulating NCP at the WAP cannot be ruled out. Furthermore, besides NCP ( R = 0.57, p = 8 × 10 −11 ) ship-based observations of primary production (PP) and bacterial production (BP) are also both positively correlated with MET with R = 0.47, p = 1 × 10 −7 and R = 0.61, p = 2 × 10 −12 , respectively. This indicates that MET-dominated regions have high biological activities. The high NCP values associated with MET are largely driven by autotrophs; otherwise, we would expect a negative correlation between MET/NCP and BP.

a The MET subnetwork presents a diverse group of correlated ASVs or nodes, with each node colored by its centrality (i.e., darker color for higher centrality). The hub nodes of the network, i.e., the top-10 ASVs with the highest connectivity or the central members for the network, were identified at the genus level as Thalassiosira , Odontella , Porosira , Actinocyclus , Proboscia , Chaetoceros, and Gyrodinium . b ASVs with higher module membership, i.e., higher intramodular connectivity, are more correlated with volumetric NCP ( R = 0.7, two-sided t-test p = 9.6 × 10 −16 ). c The biogeography of MET at the WAP, with color indicating the eigenvalue in the relative unit. Black points show the DNA sampling locations. Source data are provided in Supplementary Data  2 and 4 .

Two other community-assemblages, MER (module red, 27 ASVs) and MEG (module green, 29 ASVs), also contribute to NCP models but to a lesser extent (Table  S2 ). In the GP solutions (1) and (2), MER contributes to NCP positively, and the addition of MER marginally improves NCP prediction ( R 2 from 0.70 to 0.72, MSE from 1.18 to 1.12). The MER assemblage is dominated by the cryptophyte Geminigera that appear in warmer waters (SST, R = 0.45, p = 6 × 10 −7 ) and towards the north WAP (Y grid, R = 0.4, p = 2 × 10 −5 ) (Figs.  S5 and S6 ). MEG contributes to NCP negatively in GP solutions (2) and (3). It represents a group of heterotrophic protists, dominated by Picomonas and Telonema . The top two environmental factors correlated with MEG are MLD ( R = 0.5, p = 2 × 10 −8 ) and distance to shore (X grid, R = 0.49, p = 6 × 10 −8 ).

Based on our observations and analyses, we hypothesize that the summer plankton community—NCP system at the WAP mainly follows three broad patterns: (i) large centric diatoms associated with ice-melt form intensive blooms and fuel a short food chain from krill to other top predators 13 . In particular, the spring melt of sea ice and glacial discharge could work in concert to stabilize the water column and provide a source of iron. This high productivity combined with small losses through trophic transfer results in high export production. (ii) In warmer water, small cryptophytes dominate. Compared to large diatoms, their growth could be more efficiently checked by small microzooplankton grazers 36 , thus resulting in lower biomass for export 16 . Moreover, the food chain starting from small phytoplankton is longer due to more trophic level transfers, and the organic matter could be more subject to remineralization 37 . (iii) With deep mixing, primary production in the water column is low due to light limitation. Because of the limited food resource, heterotrophic protists feeding on bacteria and detritus dominate the microzooplankton system. Compared to scenarios (i) and (ii), more organic carbon may be recycled through the microbial loop, which further reduces carbon export and air−sea CO 2 fluxes 7 . The last pattern displays the lowest NCP. Previous WAP studies using an inverse food web model illustrated that microzooplankton grazing and the microbial loop could consume a significant amount of carbon 38 , 39 . With climate change, the WAP region is projected to have a significant loss in summer sea ice, a rise in sea surface temperatures, and deeper mixing associated with more open water and stronger winds. Consequently, the latter two scenarios may become more prevalent in the upcoming years to decades.

Although our study represents the longest record of eukaryotic DNA-based community structure and NCP in coastal Antarctica, our observations are limited to seasonal snapshots of the (summer) WAP system. These observations need to be expanded to larger spatial and temporal scales in the Southern Ocean. In the future, correlation-based analyses and statistical models will need to be further validated with field incubations and lab experiments. Non-targeted omics-based surveys (e.g., metagenomic, metatranscriptomic, and proteomic studies) will provide additional insights into the microbial metabolic pathways, which are directly linked to the biogeochemical rates and associated ecosystem functions. Moreover, they need to be coupled with high-resolution time-series studies to help us unravel changes in phytoplankton phenology and predator-prey dynamics. Despite the methodological limitations and uncertainties, our results indicate that temperature and sea ice extent are two important environmental factors regulating the summer WAP eukaryotic plankton community structure, biodiversity, productivity, and associated carbon export potential. To the extent that the observed interannual variability in the influence of sea ice extent on ecosystem structure and functioning serves as a proxy for broader, longer-term ecological consequences associated with climate change, the WAP and other coastal Antarctica regions could be destined for reduced biodiversity and biological carbon drawdown. However, a longer time series will be needed to confirm the pattern.

Environmental data and DNA sampling

Environmental data from the Palmer Long-Term Ecological Research (LTER) cruises can be accessed from the online data repository Palmer LTER DataZoo ( http://pal.lternet.edu/data ). The detailed sampling methods and in situ biological rates measurements were described previously in 40 . In brief, each year in January, a research vessel conducted intensive oceanographic and biological surveys across the shelf-transects and a north−south gradient at the West Antarctic Peninsula (WAP). During the annual LTER cruises, underway measurements and surface water sampling were conducted from the ship’s continuous flow-through system. Discrete water samples in-depth profiles were collected using a Conductivity−Temperature−Depth (CTD) rosette. Mixed layer depth (MLD) was estimated from the ship’s CTD profiles by Δσ θ = 0.03 kg m −3 using a threshold method 41 .

PAR above the water was continuously recorded from the mast PAR sensor of the ship. It was converted to PAR just beneath the water surface using a constant of 0.92. Average PAR in the mixed layer (PAR_mld) was then calculated following the method described in 42 .

Freshwater fractions were estimated from salinity and oxygen isotope signatures (δ 18 O) in seawater detailed in 43 . In brief, sampled seawater was assumed to be a mixture of ice-melt, meteoric meltwater, and Circumpolar Deep Water (CDW). A three-end member mass balance method was used to calculate the fractions, with salinity and δ 18 O values in 7 and 2.1‰ for sea-ice melt, 0 and −16‰ for meteoric meltwater, and 34.73 and 0.1‰ for CDW.

In order to collect eukaryotic plankton DNA, surface seawater from the ship’s underway flow-through system was gently vacuum-filtered onto a 47 mm, 0.45 µm Supor filter (Pall Corporation, New York, NY, USA) for years 2012 and 2013, or a 47 mm, 0.2 µm Supor filter for years 2014, 2015, and 2016. The filtration volumes were about 4 L or less at high biomass stations. For each filtration, the exact filtrate volume was recorded for later quantitative microbiome profiling (QMP). The filters were immediately stored at −80 °C until further analysis.

Remote sensing data

January sea ice concentrations from 1979 to 2020 were downloaded from the National Snow and Ice Data Center website https://nsidc.org/ . The data are in the polar stereographic projection, with each grid representing a 25 × 25 km area. January sea surface temperature (SST) data from 1982 to 2012 were acquired by the AVHRR and downloaded from NOAA website https://www.ncei.noaa.gov/ . January SST data from 2013 to 2020 were acquired by MODIS-Aqua and downloaded from NASA ocean color website https://oceancolor.gsfc.nasa.gov/ . SST data have a spatial resolution of 4 × 4 km in the equatorial region. Finally, we extracted January SST and sea ice concentrations in the Palmer grid from lines 0 to 900 and stations 0 to 220 (Figs.  S1 and S2 ).

Underway O 2 /Ar—NCP measurements

The O 2 concentration in the mixed layer is influenced by physical and biological processes. Using Ar, an inert gas with similar solubility properties as O 2 , we decomposed total O 2 into physical and biological components. Seawater O 2 /Ar ratios were measured underway from the ship’s flow-through system, using an equilibrator inlet mass spectrometer (EIMS) 44 . Biological O 2 supersaturation was estimated as

High-resolution NCP in units of mmol O 2 m −2 day −1 , was then derived from Δ(O 2 /Ar) and NCEP reanalysis winds as previously described in 45 , except for a modification to the gas exchange weighting following 46 . NCP estimation can be expressed as the equation below,

Where k denotes the gas transfer velocity for O 2 (estimated based on 47 ) and [O 2 ] sat is the equilibrium saturation concentration of O 2 (calculated based on 48 ). According to 45 , the ship-based O 2 /Ar NCP estimates are highly correlated with NCP calculated from the seasonal DIC drawdown in this region ( R 2 = 0.83). Note that our O 2 /Ar-NCP measurements in this study only reflect the mixed layer carbon fluxes and we do not assess the sequestration timescales.

DNA extraction and metabarcoding

DNA extraction and PCR were conducted as previously described 40 . In brief, cells were lysed by bead-beating at 4800 rpm for 1 min with 0.2 g of 0.1 mm Zr beads in 400 µl of Qiagen lysis buffer AP1. DNA was then extracted using DNeasy Plant Mini Kit (Qiagen, Valencia, CA, USA) following the manufacturer’s instructions. rRNA gene amplicon libraries were constructed using dual indexed 18S rRNA gene V4 primer set 16 , EukF (5′–CCAGCASCYGCGGTAATTCC–3′) and EukR (5′–ACTTTCGTTCTTGAT–3′). For each sample, PCR amplifications were conducted in triplicates with one blank as a control for contamination. The PCR reactions followed a 30-cycle program with annealing temperature at 57 °C. The resulting PCR products were purified using QIAquick PCR Purification Kit (Qiagen), and were pooled in equimolar concentration to 10 ng/µl approximately. The pooled amplicon libraries were sequenced at Duke Center for Genomic and Computational Biology in three MiSeq 300PE runs.

Sequence processing

Paired-end reads with dual indices were assembled using VSEARCH v2.3.4 (Rognes et al. 2016) following the algorithm described in 49 . The merged reads were (i) demultiplexed in QIIME 1 50 ; (ii) trimmed to remove Illumina adapters, primers, and barcodes, using BBDuk (v38.29) ( http://jgi.doe.gov/data-and-tools/bb-tools/ ); and (iii) processed following the DADA2 pipeline (version 1.10.1) to infer ASVs 51 . Quality filtering and denoising with chimeras removal were performed using the incorporated functions in the DADA2 package. In total, 2480 ASVs were identified from the five-year amplicon dataset. The sequence counts per sample (after quality filtering) were reported in Supplementary Data  1 , with median = 67,547 reads per sample.

ASVs were then classified by the ‘assignTaxnonomy’ function in DADA2 following the naïve Bayesian classifier method 52 , using a DADA2 formatted Silva 132 reference database (DOI 10.5281/zenodo.1172783) 53 . The resulted classification for each ASV is presented in Supplementary Data  3 .

Alpha diversity

After discarding two samples with the lowest counts 2016S33 and 1016S34, the libraries ( n = 117) were rarified to even depth. Alpha diversity indices Chao1 and Shannon (H′) were calculated for each sample using R package Phyloseq v1.26.1 54 . Pielou’s evenness was calculated as J = H ′/ln( S ), where S is the total number of ASVs observed in a rarified sample.

Canonical correspondence analysis (CCA)

CCA was conducted to investigate the relationships between community composition changes and environmental constrains 55 using R package vegan (v2.5-4) 56 . In total, 14 environmental variables were initially examined, including XgridCal, YgridCal, PAR, Salinity, SST, Chl, mixed layer depth, volumetric NCP, biological oxygen supersaturation, PO 4 , SiO 4 , N+N, fsim, and fmet . Bacterial production (BP) and primary production (PP) were not included in this analysis due to a large number of missing values. After a stepwise variable selection based on Akaike Information Criterion (permutation = 1000 per step), the constrained community CCA was conducted with selected environmental variables and the results were presented in Fig.  3 .

Construct statistical models for NCP

Below we describe a three-step procedure: (i) ASV counts were normalized to generate QMP; (ii) in order to reduce the model dimension, a WGCNA was applied to QMP to generate modules or bio-assemblages, and (iii) the resulting WGCNA modules and environmental variables were fed to the genetic programming (GP) algorithm to construct predictive models for NCP.

Quantitative microbiome profiling (QMP)

All samples from the year 2014 and four samples from the year 2013 (2013SA, 2013SB, 2013SC, and 2013SD) were processed first, and 0.88 ng of Schizosaccharomyces pombe gDNA (ATCC #24843D-5, Manassas, VA, USA) in single-use aliquot was spiked into to each sample as an internal standard before DNA extraction. The S. pombe reads did not turn out high enough for normalization in the resulting library, i.e., ≤0.1%. For samples from the years 2012, 2013 (except for the previous four samples), and 2015, we increased the amount of S. pombe gDNA to 16.0 ng per sample and the internal standard proportion turned out appropriate for detection (0.7−5.7% of the total 18S counts). In the third batch, samples from the year 2016 were extracted with no internal standard due to a logistic issue in the lab.

For samples from the second batch, we normalized the ASV counts to QMP (in unit of 18S gene copy numbers L −1 ) using internal standards and recorded filtration volumes as described in 40 .

For samples from the first and third batches, reads were normalized to QMP using an empirical linear relationship 40 ( R 2 = 0.94) between x —cryptophyte Alloxanthin concentrations in μg/L, and y —cryptophyte 18S rRNA gene counts in copies/mL: y = 2.05 × 10 6 x . The Alloxanthin concentrations in the linear calibration range from 0.01 to 6.22 μg/L. Although Alloxanthin concentrations for all samples used in this calculation are above 0.01 μg/L, we note that there is higher uncertainty towards the lower concentration end. The resulted Phaeocystis 18S QMP in years 2014 and 2016 were strongly correlated with Phaeocystis CHEMTAX abundances ( R 2 = 0.62), except for two outliner samples 2014S17 and 2014S44, likely due to low Alloxanthin concentrations in these two samples. QMP for these two samples was then recalculated using an empirical linear relationship derived from Phaeocystis CHEMTAX abundance 40 .

As a complementary analysis, we recalculated QMP (QMP_recal) using the empirical HPLC-CHEMTAX normalization for samples from 2013 to 2016 (no HPCL data for the year 2012). The resulted QMP_recal is highly similar to the internal standard method QMP ( y = 0.99 x , R 2 = 0.87), except for one sample 2015S13.

Weighted gene correlation network analysis

ASVs which were not observed more than three times in at least 20% of the samples were removed from the count table. WGCNA was conducted to identify inter-connected plankton bio-assemblages (modules) and correlate them with environmental variables using R package WGCNA v 1.66 27 . The calculated QMP for 112 samples were included in one WGCNA run with all samples considered independent of each other. The QMP matrix was log-transformed. The detailed R codes for each step of this analysis are presented as a supplementary file. Soft thresholding power was set at 4, which was the minimum value for the scale-free topology fit reaching R 2 = 0.9. In module identification using dynamic tree cut, the minimum module size was set at 10 in order to generate medium to large modules. The analysis resulted in 12 WGCNA modules from thousands of ASVs, thereby significantly reducing the number of input variables for the NCP model. The co-occurrence network of each module was visualized using an open-source tool Cytoscape (v3.7.0).

Genetic programming to build NCP models

Based on community structure (modules) and abiotic environmental variables, GP was used to construct statistical models predicting volumetric NCP. GP is a machine-learning approach based on evolutionary computation and it has been successfully used to construct a NCP algorithm based on satellite observations in a previous study 31 . In this study, the input factors for GP are, (i) the eigenvalues for 12 WGCNA modules, representing the biological/community factors with resolution at ASV level, and (ii) a list of physical factors, including MLD, PAR, PAR_mld, Salinity, SST, f sim , f met , X grid, and Y grid, which may directly or indirectly influence plankton physiology. The combined dataset ( n = 112) was randomly split into even training and validation datasets. GP was then conducted using Eureqa (v1.24.0) following the recommendations by 57 . The candidate solutions with varying complexity were ranked by mean squared error  (Table  S2 ). In order to reduce the risk of overfitting, the complexity of the candidate solutions was kept to a minimum.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

DNA sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) under accession number PRJNA508517. Palmer LTER data are available through Datazoo ( http://pal.lternet.edu/data ). Silva 132 reference database used for taxonomy classification was downloaded from ( https://doi.org/10.5281/zenodo.1172783 ).  Source data are provided with this paper.

Code availability

The R codes for WGCNA analysis and the MATLAB codes for NCP calculation can be accessed from GitHub ( https://github.com/nicolascassar/WGCNA-Analyses and https://github.com/nicolascassar/O2Ar_calculations ).

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Acknowledgements

We thank the scientists, scientific support personnel, the crew on R/V Laurence M. Gould, and the PAL-LTER team for their assistance in sample collection and underway measurements. We are grateful to Hans Gabathuler for instrument support and Naomi Shelton for field sampling. This work is supported by NSF OPP-1643534 to N.C., NSF OPP-1341479 to A.M., and NSF PLR-1440435 to H.D. and O.S. (Palmer LTER). M.M. was supported by the UK Natural Environment Research Council via the BAS Polar Oceans program.

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N.C. and Y.L. conceived the study. Y.L., C.M., H.D., M.M., and O.S. collected the field samples and underway data. Y.L., C.M., and A.M. processed the DNA samples. Y.L., S.C., D.E., E.D. and Z.L. analyzed the data. Y.L. and N.C. wrote the papers with inputs from all other authors.

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Lin, Y., Moreno, C., Marchetti, A. et al. Decline in plankton diversity and carbon flux with reduced sea ice extent along the Western Antarctic Peninsula. Nat Commun 12 , 4948 (2021). https://doi.org/10.1038/s41467-021-25235-w

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ORIGINAL RESEARCH article

Phytoplankton diversity effect on ecosystem functioning in a coastal upwelling system.

• 1 Instituto de Investigaciones Marinas (IIM-CSIC), Vigo, Spain
• 2 Centro Oceanográfico de A Coruña, Instituto Español de Oceanografía, A Coruña, Spain

Species composition plays a key role in ecosystem functioning. Theoretical, experimental and field studies show positive effects of biodiversity on ecosystem processes. However, this link can differ between taxonomic and functional diversity components and also across trophic levels. These relationships have been hardly studied in planktonic communities of coastal upwelling systems. Using a 28-year time series of phytoplankton and zooplankton assemblages, we examined the effects of phytoplankton diversity on resource use efficiency (RUE, ratio of biomass to limiting resource) at the two trophic levels in the Galician upwelling system (NW Iberian peninsula). By fitting generalized least square models, we show that phytoplankton diversity was the best predictor for RUE across planktonic trophic levels. This link varied depending on the biodiversity component considered: while the effect of phytoplankton richness on RUE was positive for phytoplankton RUE and negative for zooplankton RUE, phytoplankton evenness effect was negative for phytoplankton RUE and positive for zooplankton RUE. Overall, taxonomic diversity had higher explanatory power than functional diversity, and variability in phytoplankton and zooplankton RUE decreased with increasing phytoplankton taxonomic diversity. Phytoplankton used resources more efficiently in warmer waters and at greater upwelling intensity, although these effects were not as strong as those for biodiversity. These results suggest that phytoplankton species numbers in highly dynamic upwelling systems are important for maintaining the planktonic biomass production leading us to hypothesize the relevance of complementarity effects. However, we further postulate that a selection effect may operate also because assemblages with low evenness were dominated by diatoms with specific functional traits increasing their ability to exploit resources more efficiently.

Introduction

Marine phytoplankton is responsible for roughly half of the global primary production (50 Pg C year −1 ; Chavez et al., 2011 ), contributes to nutrient cycling and regulation of climate dynamics, affects the fate of adjacent trophic levels ( Richardson and Schoeman, 2004 ), and, ultimately, constrains fishery catches ( Chassot et al., 2010 ). Marine phytoplankton is an extremely diverse group of organisms ( De Vargas et al., 2015 ), and this diversity, encompassing a large variety of life histories, is an essential factor that affects the whole structure of marine ecosystems ( Naeem, 2012 ). It is indeed the diversity of functional traits that differs among and within species and taxonomic groups, the key component determining the fitness of planktonic communities along environmental gradients and influencing the functioning of pelagic ecosystems ( Irwin and Finkel, 2018 ). Therefore, there is a need to better understand the relationship between the variability in phytoplankton diversity and its effects on ecosystem processes.

In recent years, various reviews have synthesized the effects of alterations of biodiversity (B) on ecosystem functioning (EF) concluding that biodiversity has a major role in sustaining the productivity of ecosystems and their stability ( Cardinale et al., 2012 ; Tilman et al., 2014 ). Most of this evidence comes from controlled experiments; however, field studies are also consistent with theory and experiments demonstrating the strong effect of biodiversity on ecosystem production even after accounting for abiotic forcing ( Duffy et al., 2017 ). In the marine realm, several studies have been devoted also to understand the effects that marine biodiversity has on production, biomass, or on the resilience to disturbances or invasions ( Stachowicz et al., 2007 ; van der Plas, 2019 ). Yet the majority of this experimental and field research has examined relationships in the benthos ( O'Connor and Byrnes, 2014 ; Duffy et al., 2017 ). Regarding the pelagos, recent studies have addressed BEF relationships within natural assemblages of plankton either in fresh or marine waters. A BEF relationship in the plankton was first described by Ptacnik et al. (2008) , who showed that resource-use efficiency (RUE), an ecological index that measures the proportion of supplied resources turned into new biomass ( Hodapp et al., 2019 ), scaled positively with phytoplankton taxonomic richness in multiple Fennoscandian lakes and in the Baltic Sea. Korhonen et al. (2011) also used data from boreal lakes to connect, in this case, productivity to diversity of various plankton groups showing linear, unimodal, or nonsignificant relationships between richness and biomass production depending on the spatial scale. Furthermore, Olli et al. (2014) , using long-term phytoplankton sampling, concluded that increased diversity enhanced RUE for primary producers across the brackish Baltic Sea. However, it is unclear if these BEF relationships apply to highly dynamic Eastern Boundary Upwelling Ecosystems (EBUEs). For instance, in the California Current System, an ecosystem model coupled to a circulation model showed a hump-shaped relationship between diversity and productivity with portions of the diversity-productivity scatter being dependent on geographic regions ( Goebel et al., 2013 ). Other authors did not find a relationship between phytoplankton species richness and ecosystem productivity (e.g., Cermeño et al., 2013 ).

Typically, the majority of BEF studies have used richness as the metric of biodiversity because it is easy to manipulate in experiments and to measure in the field. However, there is also evidence of the relevance of other components of biodiversity to understand pelagic processes. For instance, some authors have studied the effect(s) of evenness on planktonic ecosystem properties showing a strong negative effect on RUE in the phytoplankton of the Wadden Sea ( Hodapp et al., 2015 ) or on biomass and resource use in the phytoplankton of the Baltic Sea ( Lehtinen et al., 2017 ) highlighting the importance of the identity of dominant species. Besides taxonomic diversity, biodiversity can be assessed in terms of functional diversity, that is, accounting for the expression of multiple functional traits in the community, often concluding that functional diversity can be a better predictor of ecosystem properties than taxonomic diversity as shown for phytoplankton communities in Fennoscandian lakes ( Abonyi et al., 2018 ). Whether taxonomic or functional diversity of competing species affects ecosystem properties, it is also fundamental to incorporate trophic complexity in order to understand the effects of biodiversity across trophic levels ( Duffy et al., 2007 ). In experimental planktonic systems, Striebel et al. (2012) showed that phytoplankton diversity increased zooplankton productivity, while Filstrup et al. (2014) showed that the effect of phytoplankton evenness on RUE switched from negative at the producer level (phytoplankton) to positive at the consumer level (zooplankton) in US lakes.

Apart from average effects on ecosystem functioning, theory, experiments, and field studies predict that increasing diversity can reduce the variability of community biomass or other ecosystem properties in time and space through several mechanisms ( Loreau and de Mazancourt, 2013 ). This would occur because more diverse assemblages containing interacting species, which respond differently to the environment are more likely to buffer the effects of perturbations conferring stability to the community and maintaining the ecosystem properties in a dynamic environment ( Ives and Carpenter, 2007 ). Stability, however, is a complex and multifaceted concept with multiple components, such as variability, resistance, or resilience, which might be unrelated implying that the overall stability of an ecosystem might not be simply explained by one particular component ( Hillebrand et al., 2018 ), thus leading to different biodiversity-stability relationships ( Craven et al., 2018 ). Most analyses on biodiversity-stability relationships have been performed through experiments in terrestrial systems ( Tilman et al., 2014 ), whereas fewer studies dealt with natural ecosystems, and the majority have focused on plants (e.g., García-Palacios et al., 2018 ; but see Cusson et al., 2015 ). In the case of natural plankton assemblages, Ptacnik et al. (2008) showed that higher levels of phytoplankton taxonomic richness implied less variability of both resource use and community composition, and Shurin et al. (2007) documented a positive relationship between zooplankton diversity and community stability in temperate lakes. Despite all these research efforts, the importance of phytoplankton diversity on stability in EBUEs, so as the shape of BEF relationships between trophic levels, and the performance of functional diversity vs. taxonomic diversity have been yet poorly addressed (but see Cermeño et al., 2013 ; Goebel et al., 2013 ; Vallina et al., 2017 ).

Planktonic communities are highly dynamic with assemblages changing rapidly in response to circulation and fertilization patterns and other physical and environmental forcing. This is even more evident in coastal upwelling systems where planktonic assemblages might fluctuate at short-time scales with different assemblages characterizing the various phases of an upwelling cycle ( Marañón, 2015 ). At larger spatial and temporal scales, the structure of phytoplankton and zooplankton assemblages is affected by seasonal changes, with species' abundance responding to light conditions, temperature, nutrient inputs, or the presence of particular producers and consumers ( Wiltshire et al., 2015 ). Therefore, planktonic biodiversity is affected by the physical environment, water hydrography, and biotic variables ( Sarker et al., 2018 ). However, BEF studies have focused less on the abiotic and biotic context that might exert comparable effects to changes in species richness in mediating ecosystem properties ( Godbold, 2012 ; van der Plas, 2019 ). Thus, accounting for the environmental context in BEF relationships under natural (or experimental) conditions is crucial to understand and interpret the effects that changing biodiversity has on ecosystem properties ( García et al., 2018 ), stability ( García-Palacios et al., 2018 ), or community performance ( Schabhüttl et al., 2013 ).

In this study, we analyzed whether the phytoplankton diversity effects on ecosystem function follows theoretical and experimental expectations in real communities occurring in EBUEs for which knowledge is limited. In doing so, we used long-term time series of phytoplankton and zooplankton community data in conjunction with meteorological and hydrographic data to examine BEF relationships in a highly dynamic coastal upwelling ecosystem. In particular, we (i) evaluated the effect of two components of phytoplankton taxonomic diversity (richness and evenness) on phytoplankton and zooplankton rates of productivity to the amount of available resources (i.e., RUE), (ii) tested whether biodiversity influences planktonic RUE variability, (iii) quantified the importance of biodiversity relative to environmental conditions in driving planktonic RUE dynamics, and (iv) evaluated the explanatory power of phytoplankton taxonomic diversity vs. functional diversity in explaining planktonic RUE.

Materials and Methods

Study area and plankton sampling.

Galicia is at the northern boundary of the Iberia/Canary current EBUE ( Figure 1 ). Coastal winds at these latitudes (42° to 44° N) are seasonal; northerly winds prevail from March–April to September–October, promoting coastal upwelling, and downwelling-favorable southerly winds predominate the rest of the year. However, more than 70% of the variability in coastal winds occurs in periods of <1 month, so that the upwelling season appears as a succession of wind-stress events separated by wind-calm episodes, with a wide variety of frequencies ranging from 3 to 15 days ( Álvarez-Salgado et al., 2002 ).

Figure 1 . Map of study area showing the location of the plankton and hydrographic sampling station (black dot), and the location of the grid cell where the upwelling index was calculated (black triangle).

Phytoplankton identification and count data were obtained from the time series project RADIALES conducted by the Instituto Español de Oceanografía off A Coruña (NW Spain, Figure 1 ) ( Bode et al., 2009 ). Specifically, water samples were collected monthly with 5 L of Niskin bottles or a rosette sampler from 0-, 5-, 10-, 20-, 30-, 40-, and 70-m depths at station E2CO (water depth, 80 m; 43°25′30″N, 08°26′20″W; Figure 1 ) from January 1989 to December 2016 ( n = 313 days sampled), though sampling at 7 m ended in 1993, and from 2010 onward, samples were taken just from 3 depths between the surface and 40 m including the depth of the surface chlorophyll maximum. For each depth, samples were collected for the determination of phytoplankton abundance and inorganic nutrients and chlorophyll a concentration following the methods described in Casas et al. (1997) . Phytoplankton samples of volume 50–100 ml were preserved in Lugol's solution and kept in the dark until analysis. Depending on phytoplankton concentration, 10–25 ml of samples was allowed to settle in the Utermöhl chamber for up to 24 h. Samples were counted following the technique described by Utermöhl ( Lund et al., 1958 ) using a Nikon Diaphot TMD microscope until May 1997 and a Nikon Eclipse TE300 microscope until the end of the time series. A magnification of 100× was used for large forms, 250× for intermediate forms, and 400× for small forms. The entire slide was examined at 100× to account for large species, while only transects or smaller areas were examined at higher magnification. At least 250 cells were counted for each sample. Whenever possible, organisms were classified at the species or genus level. Species nomenclature followed the World Register of Marine Species ( http://www.marinespecies.org ). The samples were identified by two experts (M. Varela until 2010, and J. Lorenzo from 2010 to 2016). The individual biomass (in pg C) for each identified taxa was estimated from cell biovolume (in μm 3 ) after measuring the dimensions of 30–100 cells in samples distributed over all seasons and applying conversion equations from the literature (see details in Huete-Ortega et al., 2010 ). Biomass (in pg C L −1 ) of a given species in a given day and sampled depth was then calculated as the abundance (in cells L −1 ) times the cell biomass. For the purposes of the present work, we used the taxa that were systematically identified at the species level in at least 10 samples along the time series. This resulted in a set of 73 taxa, 18 of which started to be identified in 2008 ( Supplementary Table 1 ). The total biomass (obtained as the sum of the biomass of all counted species in each day and sampled depth) was significantly correlated with chlorophyll a concentration ( Supplementary Figure 1 ), and the biomass was dominated by diatoms ( Supplementary Figure 2 ). The original dataset is available at PANGAEA ( https://doi.org/10.1594/PANGAEA.908815 ) ( Bode, 2019 ). Additionally, we collated a series of morphological (cell size and ability to form chains or colonies), physiological (silica requirement, trophic strategy, and pigment composition), and behavioral (ability to swim) traits for each phytoplankton species ( Supplementary Table 1 ) using our own compilation and data from the literature ( Klais et al., 2017 ). This group of traits is of relevance for reproduction, resource acquisition, and survival ( Litchman and Klausmeier, 2008 ). Together, they affect phytoplankton fitness and are involved in several functions such as light use, nutrient uptake, or predator avoidance. Cell size can be further considered as a key trait that affects metabolism, growth rate, and community structure among others ( Marañón, 2015 ).

Zooplankton was sampled at the same station and dates as phytoplankton by means of double oblique tows from the surface to 5 m of the bottom using a 50-cm diameter Juda—Bogorov plankton net with 250-μm (until 1997) or 200-μm (from 1997 onward) mesh size. The net was equipped with a General Oceanic Flowmeter for the calculation of water filtered and a depth recorder. Samples were preserved in 2–4% sodium borate-buffered formaldehyde. Subsamples were taken to estimate total zooplankton abundances (in ind × m −3 ) by direct examination using a stereo microscope, and biomass (in μg DW × L −1 ) by weighting dried aliquots (50°C, 48 h). Further details can be found in Bode et al. (2012) . The original dataset is available at PANGAEA ( https://doi.org/10.1594/PANGAEA.908815 ) ( Bode, 2019 ).

Hydrographic Sampling and Analysis

Concurrently with the plankton samples, vertical profiles of temperature and salinity were measured with a CTD probe (Seabird SBE-25). The salinity of the CTD was checked against the salinity of bottom water samples measured with an induction salinometer Autosal 8400A calibrated with Standard Seawater ( Casas et al., 1997 ). Salinity was expressed in the practical salinity scale ( UNESCO, 1986 ). Nitrate concentration (NO 3 in μmol L −1 ) was determined by segmented flow analysis according to the standard procedures of Grasshoff et al. (1983) using a Technicon AA-II (1989–2006), a Bran–Luebbe AA3 (2007–2012), and a Seal Analytics QuAAttro 39 (2013–2016). Chlorophyll a concentration (Chl a in mg m −3 ) was measured by fluorimetric analysis of acetone extracts of phytoplankton collected on 0.8-μm pore-size membrane filters (until 1992) or GF/F filters (from 1993 onward). Specific calibrations were performed to ensure the continuity of the chlorophyll series when changing from the filter fluorometer method ( Parsons et al., 1984 ) to the spectrofluorimetric technique ( Neveux and Panouse, 1987 ) after 2001. Nutrient and chlorophyll data series are available at: https://doi.org/10.1594/PANGAEA.885413 ( Bode et al., 2018 ).

Physical Forcing

Daily upwelling index (UI in m 3 s −1 km −1 ) data, a rough estimate of the volume of water upwelled per km of coastline for the period 1989 to 2016 were downloaded from: http://www.indicedeafloramiento.ieo.es/index_UI_en.html . UI was estimated from geostrophic winds calculated from the surface atmospheric pressure fields supplied every 6 h by the US Navy Operational Global Atmospheric Prediction System (NOGAPS) model maintained by the Fleet Numerical Meteorological and Oceanography Center ( http://www.usno.navy.mil/FNMOC/ ) in a 1° × 1° grid centered at 44°N 9°W ( Figure 1 ). This cell is representative for the physical forcing that determines the impact of coastal upwelling in the region ( Bode et al., 2015 ). We used the meridional wind component, thus the UI represents the volume of water upwelled along the West–East direction with positive values of UI indicating upwelling-favorable conditions. Conversely, negative values indicate downwelling-favorable conditions. Further numerical details can be found in González-Nuevo et al. (2014) . In this study, values of UI were averaged over 15 days prior to each sampling date.

Data Analyses

Resource-use efficiency and biodiversity estimations.

Phytoplankton resource-use efficiency (RUE pp ) was calculated sensu Ptacnik et al. (2008) in terms of phytoplankton carbon biomass (pg C L −1 ) per unit of nitrate concentration (μmol L −1 ), i.e., RUE pp = phytoplankton biomass/ NO 3 - concentration. Other components such as ammonia are important for the dissolved inorganic nitrogen (DIN) pool in this region; however, this variable was not measured with the same periodicity. Nonetheless, nitrate is the main limiting nutrient for the primary production in this region ( Álvarez-Salgado et al., 1997 ). Zooplankton resource-use efficiency (RUE zp ) was calculated sensu Filstrup et al. (2014) in terms of zooplankton biomass (μg L −1 ) per unit phytoplankton carbon biomass (pg C L −1 ), i.e., RUE zp = zooplankton biomass/phytoplankton biomass. Both ratios were natural log-transformed for later modeling.

Taxonomic diversity (TD) was expressed as species richness (S) and as evenness (J) ( Pielou, 1966 ) using phytoplankton biomass as follows:

J = H H m a x where H max = ln S and H = - ∑ i = 1 S B i B t o t × l n B i B t o t where B i is the biomass of a species i , and B tot is the total biomass. Additionally, we calculated various uncorrelated multitrait-based functional diversity (FD) metrics, which capture the various aspects of functional diversity ( Mouchet et al., 2010 ). In particular, FD was expressed as functional group richness (FGR) and functional dispersion (FDis) following Laliberté and Legendre (2010) , and functional evenness (FEve) following Villéger et al. (2008) . To calculate the FD metrics, the abundance matrix was based on species biomass, and the functional trait matrix contained a quantitative variable (cell biomass expressed in log units) and other six binary variables describing other morphological, physiological, and behavioral traits (see Supplementary Table 1 ). FGR is a dendrogram-based indicator of functional groups computed from an a posteriori classification of species based on their functional traits for which the Ward method was used to create the dendrogram of the species that was cut at nine functional groups. FDis is a distance-based metric that measures the distance to the centroid of the assemblage in the trait space, and FEve is a metric that combines the evenness of species spacing in trait space and the evenness of species relative abundances. FDis and FEve metrics were weighted by the biomass of the species, and FEve was not defined for samples with fewer than three taxa ( n = 6 cases). Finally, single-trait-based indices, that is, community-weighted mean (CWM) traits were also calculated to examine phytoplankton single-trait contributions to RUE. CWMs were calculated for each trait weighted by species biomass using methods implemented by Laliberté and Legendre (2010) .

Statistical Analyses

The values of RUE pp calculated for a day i and depth j were modeled using generalized least square (GLS) models that were formulated as follows:

where α is an intercept, and ns n is a natural cubic spline describing the effect of the day of the year (DoY), i.e., the seasonality, the time trend, i.e., the interannual long-term pattern (days, i.e., consecutive days from 1989 to 2016), the depth (D), the water temperature (WT), the coastal upwelling index (UI), the phytoplankton richness (S), and the phytoplankton evenness (J). All splines had 2 degrees of freedom (df) with the exception of DoY that used 4 df. Finally, ϵ i,j is a vector of errors assumed to have mean 0 and variance σ 2 . To evaluate the variability in the response variable, i.e., a nonconstant variance, the variance in RUE pp (σ 2 ) was modeled as an exponential function of the environmental conditions (i.e., WT or UI) or the taxonomic diversity metrics (i.e., S or J). For instance, for WT: Var(ϵ i,j ) = σ 2 × exp (2 × δ × WT i,j ), where δ is an unknown parameter to be estimated that describes the estimated change in variance with water temperature. Model fitting improvement and comparison between the possible variance covariates were evaluated using the Akaike information criterion (AIC) and likelihood ratio tests ( Pinheiro and Bates, 2000 ). Covariability among predictors was evaluated using variance inflation factors (VIFs).

The values of RUE zp calculated for a day i were also modeled using GLS models that were formulated as follows:

In this case, to match the zooplankton oblique tows, WT was averaged over the water column, and phytoplankton carbon biomass (necessary to calculate RUE zp , see above), S and J were estimated based on the species biomass averaged over the water column. The rest of the parameters and variance model are as described for Equation (1).

To enable comparison of the phytoplankton and zooplankton BEF relationships using taxonomic and functional diversity, RUE pp and RUE zp calculated for a day i were related to phytoplankton S, J, and FD metrics estimated also for a day i , therefore, based on the species biomasses averaged over the water column as explained above for the RUE zp model. To quantify the bivariate relationships, we used reduced major axis (RMA) regression, and we did not include other covariates in these models because biodiversity was the best predictor as observed in the more detailed models (see Results below).

Finally, complementary analyses were further performed. In particular, we confronted the patterns found using a measure of standing stock to quantify biomass production with other alternative forms of calculating RUE pp , that is, calculated sensu Ptacnik et al. (2008) in terms of chlorophyll a (mg m −3 ) per unit of nitrate (μmol L −1 ), and sensu Lehtinen et al. (2017) in terms of primary production (mg C m −3 h −1 ) per unit of nitrate (μmol L −1 ). Primary production was obtained from Bode et al. (2019) . These models were fitted to data covering the whole period, though restricting the phytoplankton species to the 55 taxa that were consistently identified along all three decades ( Supplementary Table 1 ).

All treatment of data and analyses were performed with the software R (version 4.0.2, R Core Team, 2020 ) and using the packages “nlme 3.1-149” ( Pinheiro and Bates, 2000 ), “relaimpo 2.2-3” ( Grömping, 2006 ), “FD 1.0-12” ( Laliberté and Legendre, 2010 ), and “lmodel2 1.7-3” ( Legendre and Legendre, 1998 ).

Phytoplankton Resource-Use Efficiency

The model fitted to phytoplankton resource-use efficiency data (Equation 1, Supplementary Table 2 ) revealed that RUE pp showed a seasonal cycle peaking in late March to early April ( Figure 2A ) and a nonlinear long-term trend over the study period ( Figure 2B ). RUE pp decreased linearly with sampling depth ( Figure 2C ) and was positively related to water temperature ( Figure 2D ) and upwelling index ( Figure 2E ). Furthermore, RUE pp was related to taxonomic diversity scaling positively with phytoplankton richness ( Figure 2F ) and negatively with phytoplankton evenness ( Figure 2G ). Studying the importance of predictors for RUE pp based on the proportional marginal variance decomposition, taxonomic diversity, and richness (61.5%) in particular, was the best predictor in explaining RUE pp dynamics, whereas the environmental factors (WT and UI) played a secondary role ( Supplementary Table 2 ). Including a variance model in the GLS as an exponential function of covariates resulted in better fittings ( Supplementary Table 3 ). More specifically, the spread of RUE pp decreased with evenness that was the most optimal variance covariate. In particular, an increase in 0.2 units of phytoplankton evenness reduced RUE pp variability by 21.4% ( Supplementary Table 3 ). Finally, the RUE pp model did not show any relevant remaining patterns in the residuals ( Supplementary Figure 3 ).

Figure 2 . Partial-effects plots from the RUE pp GLS model depicted in Equation (1). Shown are the seasonal (A) and long-term (B) changes in RUE pp , the trend with depth (C) , and the relationships with water temperature (D) , upwelling index (E) , phytoplankton richness (F) , and phytoplankton evenness (G) . Bands indicate 95% confidence intervals and the rugs along the x-axes display the distribution of the data. See ANOVA table in Supplementary Table 2 and model selection of variance covariates in Supplementary Table 3 . Silhouettes were obtained from http://www.phylopic.org .

Zooplankton Resource-Use Efficiency

The model fitted to zooplankton resource-use efficiency data (Equation 2, Supplementary Table 4 ) revealed that RUE zp showed a seasonal cycle peaking in late March to early April ( Figure 3A ) and a decreasing trend from 2003 onward ( Figure 3B ). RUE zp was positively related to water temperature ( Figure 3 C) and had a nonlinear relationship with the upwelling index ( Figure 3D ). Furthermore, RUE zp was related to phytoplankton taxonomic diversity scaling negatively with richness ( Figure 3E ) and positively with evenness ( Figure 3F ). As for the case of RUE pp , when studying the importance of predictors for RUE zp , taxonomic diversity, and richness (66.6%) in particular, was the best predictor, whereas the environmental factors played a secondary role ( Supplementary Table 4 ). Including a variance model in the GLS as an exponential function of covariates resulted in better fittings ( Supplementary Table 5 ). More specifically, the spread of RUE zp decreased with richness that was the most optimal variance covariate. In particular, an increase in 10 units of phytoplankton richness reduced RUE zp variability by 18.8% ( Supplementary Table 5 ). Finally, the RUE zp model did not show any relevant remaining patterns in the residuals ( Supplementary Figure 4 ).

Figure 3 . Partial-effects plots from the RUE zp GLS model depicted in Equation (2). Shown are the seasonal (A) and long-term (B) changes in RUE zp , and the relationships with water temperature (C) , upwelling index (D) , phytoplankton richness (E) , and phytoplankton evenness (F) . Bands indicate 95% confidence intervals, and the rugs along the x-axes display the distribution of the data. See ANOVA table in Supplementary Table 4 , and model selection of variance covariates in Supplementary Table 5 . Silhouettes were obtained from http://www.phylopic.org .

Taxonomic and Functional Diversity Effects on Resource-Use Efficiency

Multitrait-based functional diversity metrics were unrelated, though they showed a certain degree of association with taxonomic diversity, especially between FDis and J, and FGR and S ( Supplementary Figure 5 ). Figures 4 , 5 compare the effects of taxonomic and functional diversity of phytoplankton on RUE pp and RUE zp , respectively. First, both taxonomic ( Figure 4A ) and functional ( Figure 4C ) richness had positive effects on RUE pp , while the effects were negative on RUE zp ( Figures 5A,C ). Second, both taxonomic ( Figure 4B ) and functional ( Figure 4D ) evenness had negative effects on RUE pp , while the effects were positive on RUE zp ( Figures 5B,D ). Finally, the relationship with FDis showed the same trend as for FEve for phytoplankton RUE, where more functionally similar phytoplankton assemblages had higher RUE ( Figure 4E ), and for zooplankton RUE, where zooplankton preying upon more functionally dissimilar phytoplankton assemblages had higher RUE ( Figure 5E ). Regarding the explanatory power, TD metrics had higher explanatory power (i.e., greater R 2 ) compared to FD metrics. Within TD, richness was a better predictor (48 and 47% for RUE pp and RUE zp , respectively), whereas within FD, FEve was a better predictor (29 and 20% for RUE pp and RUE zp , respectively). All slopes were statistically significant ( p < 0.0001), and in all cases, elevated RUE pp values occurred when diatom biomass was higher, while,when RUE zp values were higher, phytoplankton biomass was less dominated by diatoms.

Figure 4 . Bivariate plots showing the effects of phytoplankton taxonomic (A,B) and functional (C–E) diversity on RUE pp . Dot size is scaled to diatom biomass, and lines show reduced major axis (RMA) fits. For each relationship, the slope (and statistical significance with parametric p -value < 0.0001***) and the explanatory power are shown.

Figure 5 . Bivariate plots showing the effects of phytoplankton taxonomic (A,B) and functional (C–E) diversity on RUE zp . Dot size is scaled to diatom biomass, and lines show RMA fits. For each relationship, the slope (and statistical significance with parametric p -value < 0.0001***) and the explanatory power are shown.

Finally, regarding the phytoplankton single-trait-based indices, three CWMs had important effects on plankton RUE: the ability to use biogenic silica, the ability to swim, and the ability to form chains or colonies ( Figure 6 , Supplementary Table 6 ). For RUE pp , the largest contributor was CWM chain ( Figure 6C ), while CWM motility ( Figure 6B ) and CWM silica ( Figure 6A ) contributed less to explain phytoplankton resource use. For RUE zp , all three CWMs contributed almost equally to zooplankton resource use ( Figures 6D–F ), though the contribution of CWM motility was slightly higher ( Supplementary Table 6 ). RUE pp increased with CWM silica and CWM chain , and decreased with CWM motility . However, the direction of the effects was the opposite for the case of RUE zp .

Figure 6 . Scatterplots showing the relationships between RUE pp (A–C) and RUE zp (D–F) with CWM silica (A,D) , CWM motility (B,E) , and CWM chain (C,F) . Lines show RMA fits, and corresponding numerical data are provided in Supplementary Table 6 .

Complementary Analyses

Models using alternative calculations of RUE pp , namely, with chlorophyll a ( Supplementary Figure 6 ) and primary production ( Supplementary Figure 7 ) and fitted to data covering the whole period though restricting the phytoplankton species to the 55 taxa that were consistently identified along all three decades, resulted roughly in the same patterns as described above using a measure of standing stock to quantify the phytoplankton biomass production.

Phytoplankton and Zooplankton Resource-Use Efficiency

The model for RUE pp showed that more phytoplankton species lead to higher biomass per unit resource. This positive relationship between phytoplankton RUE and richness concurs with earlier observations made in aquatic systems both in the field ( Ptacnik et al., 2008 ) and in experiments ( Striebel et al., 2009a ). At the same time, the model revealed also a negative relationship between evenness and RUE pp , which again agrees well with previous field observations in differing aquatic systems such as is the Wadden Sea, where phytoplankton evenness was the most important driver of productivity and RUE ( Hodapp et al., 2015 ), or in Midwestern US lakes, where phytoplankton RUE was inversely related to phytoplankton evenness ( Filstrup et al., 2014 ). On the other hand, the model for RUE zp showed that zooplankton communities produced the least biomass per unit of phytoplankton biomass when feeding upon species-rich phytoplankton communities but dominated by few species or group of species. Again, this result concurs with previous findings documenting a negative relationship between phytoplankton evenness and the production of zooplankton biomass per unit of phytoplankton biomass in lakes ( Filstrup et al., 2014 ). However, it differs from Filstrup et al. (2019) who found a nonsignificant role of phytoplankton richness in driving zooplankton resource use efficiency in lakes.

In general, two nonexclusive mechanisms have been identified to explain why biodiversity enhances ecosystem function: complementarity, by which more diverse communities use limiting resources more efficiently through niche partitioning or facilitation, and the selection effect, by which more diverse communities are more likely to include a few dominant species with specific traits that drive the ecosystem functioning ( O'Connor and Byrnes, 2014 ). Separating and quantifying these processes can be addressed experimentally ( Loreau and Hector, 2001 ); however, it is rather difficult to differentiate between these two mechanisms in the field. Nevertheless, the positive effect of richness found in our study can be likely explained by the niche complementarity mechanism. This interpretation would be based on the premise that a more diverse community may include more diverse traits such as light and nutrient utilization traits reflecting a better niche differentiation in wavelength utilization and resource use ( Striebel et al., 2009a , b ; Behl et al., 2011 ). This would be important during periods of nutrient enrichment (e.g., upwelling pulses) when more and more variable resources would facilitate the coexistence of a larger number of species. On the other hand, the negative effect of evenness would indicate that certain dominant species, or group of species, would exhibit specific traits that would give them certain physiological advantages allowing for a more efficient exploitation of resources, that is, a selection effect ( Filstrup et al., 2019 ).

The simultaneous action of complementarity and selection effects would probably be associated to the heterogeneous environment that typically characterizes EBUEs. Coastal upwelling systems are highly productive regions where most of the biomass is usually composed of chain-forming diatoms especially during blooms ( Sarthou et al., 2005 ). This fact has been shown also in our study zone where diatoms are the dominant group and assemblages change rapidly between upwelling/relaxation/downwelling phases ( Casas et al., 1997 ). Diatoms are fast growing species capable of maintaining high nutrient uptake rates for longer periods, and exploit typical upwelling-intermittent nutrient pulses more effectively than other taxa of the same size ( Marañón, 2015 ). Dominance primarily reflects the distribution of traits within a community and the identity of the dominant traits, thus this fact has been recognized as an important effect to explain the fate of ecosystem processes because evenness often responds more rapidly to altered environmental constraints than species richness leading to rapid responses in ecosystem functioning ( Hillebrand et al., 2008 ). Given these premises, we suggest that the mechanistic basis for the shift in the effect of phytoplankton richness and evenness would be dependent on the diatom biomass and, more specifically, on the functional traits that diatoms have. This is evidenced by the relationships found with the community-weighted mean traits highlighting that the predominance of traits that characterizes diatom species, specially the ability to form chains and colonies, increases phytoplankton resource use. Furthermore, diatoms show high growth rates relative to their cell volume. However, nutrient traits tend to be similar among taxa for a typical cell size. This indicates that diatoms appear to be adapted to high nutrient conditions as those found within upwelling regions ( Edwards et al., 2012 ). Indeed, phytoplankton species with greater growth rates and, in particular diatoms sampled in our region tend to respond more strongly to increased upwelling ( Otero et al., 2018 ). Concurring with our results, a population growth model fitted to phytoplankton species over an annual cycle pointed out the fundamental importance of the selection effect in driving marine primary productivity ( Cermeño et al., 2016 ).

This effect of diatom dominance would translate up in the food web. For the consumer level, we found lower RUE zp when phytoplankton biomass was dominated by diatoms, which could be likely explained by several factors including the lower impact that mesozooplankton compared to microzooplankton has on phytoplankton (e.g., Fileman and Burkill, 2001 ), the inhibition that diatom exudates can exert on zooplankton grazing (e.g., Malej and Harris, 1993 ), or the reduced impact that copepod grazing has on phytoplankton biomass and production during production peaks compared to upwelling transition periods when phytoplankton biomass is lower, and dinoflagellates are more abundant (e.g., Bode et al., 2003 ). Indeed, when there was a predominance of traits in the community that characterize dinoflagellate species (e.g., ability to swim), zooplankton resource use was higher. Likewise, in lakes, when zooplankton preys upon phytoplankton communities dominated by cyanobacteria, RUE zp decreases because cyanobacteria is a poor food source for zooplankton ( Filstrup et al., 2014 ).

A simultaneous detection of a positive and negative effect of richness and evenness, respectively, on ecosystem function as the one observed here, was shown also by others either in fresh ( Filstrup et al., 2019 ) or marine ( Napoleón et al., 2014 ) waters. However, there are contrasting results on the relative contribution of the two effects, that is, the differences between the strength of the relationships. Overall, our models indicated that taxonomic richness consistently had much stronger effects on both phytoplankton and zooplankton RUE. This result contrasts with what has been observed in lakes, where evenness explained more variance in phytoplankton and zooplankton RUE than richness leading the authors to suggest that it might be the distribution of taxa rather than their number, the main driver of ecosystem function in lakes ( Filstrup et al., 2019 ). Coastal upwelling ecosystems are highly heterogeneous and contain an enormous species richness rapidly changing with the environmental conditions. Such high number of species is probably necessary to maintain the overall productivity of the system and ensures the occurrence of highly productive species. This scenario would lead to both complementarity and selection effects operating simultaneously, with complementarity being stronger when functionally different species coexist (e.g., during the spring bloom) and take advantage of a heterogeneous resource supply, whereas selection effects would be important when a broad trait space coincides with a more homogeneous landscape ( Hodapp et al., 2016 ). Nonetheless, taken altogether, our results suggest that the opposing effects of primary producer biodiversity on phytoplankton and zooplankton resource use efficiency would be of similar nature in the ocean and freshwater environment. However, there appear to be differences on the sensitivity of ecosystem functioning to richness and evenness that could be ascribed to intrinsic differences among systems, such as changes in hydrographic and biogeochemical characteristics, or the identity of the dominant taxa, or to other underlying mechanisms demanding further research.

Environmental Effects on Resource-Use Efficiency

Apart from the importance of the environmental conditions in determining the overall composition of the community outlined above, we detected direct abiotic effects on RUE and time-varying patterns. In particular, our statistical models showed an annual peak located in April for both phytoplankton and zooplankton RUE coinciding with the spring bloom ( Casas et al., 1997 ). Moreover, there were somehow an interannual inverse pattern between RUE pp and RUE zp that would point to changes in the plankton community along years. In this regard, the studied system displayed phase shifts in plankton community structure around the turn of the 21st century following changes in climate and local hydrography ( Bode et al., 2020 ). The models also revealed the influence of water temperature and upwelling intensity on RUE, albeit less pronounced than the effects of biodiversity. These effects were weak for RUE zp and especially apparent for RUE pp . Resource use efficiency had greater values when upwelling intensity was high and waters were warmer. This combination of high upwelling during the 15 days previous to the sampling date and high water temperature during the sampling date indicates the typical succession of intense upwelling followed by wind relaxation. The positive effect of upwelling contributes to fertilize the system, which promotes a proliferation of phytoplankton biomass and a more efficient use of resources during the subsequent upwelling relaxation (e.g., Huete-Ortega et al., 2010 ). The effect of temperature could also be explained because this variable favors cell growth. Besides that, RUE pp decreased with depth likely associated to the decrease in phytoplankton biomass in deeper and less illuminated waters. Therefore, the abiotic environment was found to play a role in explaining part of the variability of RUE; however, its effect was minor compared to those of biodiversity. Recent meta-analyses showed that the effects of abiotic and biotic drivers in mediating ecosystem properties can overlap or be even stronger than the effects of species loss in controlled experiments ( Godbold, 2012 ). However, Ptacnik et al. (2008) showed in the field that the relative importance of abiotic drivers vs. that of richness was minor in explaining phytoplankton RUE dynamics, thus agreeing with our results. Moreover, Hodapp et al. (2015) identified also a weaker effect of temperature and light compared to evenness on phytoplankton productivity.

Biodiversity Effects on Resource-Use Efficiency Variability

Our statistical models also showed that phytoplankton and zooplankton RUE were both less variable at higher levels of diversity, evenness in the case of RUE pp , and richness in the case of RUE zp . These results indicate that RUE pp and RUE zp would be stabilized when phytoplankton biomass is more evenly distributed, and when zooplankton preys upon richer phytoplankton communities, respectively. In general, theoretical, experimental, and field studies conclude that temporal stability of ecosystem processes would be greater at higher diversity, usually in the form of species numbers, and that those processes would be simultaneously enhanced also at higher levels of diversity ( Tilman et al., 2014 ). Our variance models do not explicitly account for the temporal scale, but we can interpret the fact that biodiversity increases RUE stability as a consequence of that; first, more diverse assemblages are more likely to include different species maintaining the ecosystem function under different environmental conditions (i.e., the insurance hypothesis), and second, more diverse assemblages may show increased asynchrony in species' abundances (i.e., the portfolio effect) favoring the stability of RUE ( Thibaut and Connolly, 2013 ). Furthermore, contrary to expectations, our results showed that the highest levels of RUE were not associated with the lowest levels of variability. This would be in line with recent synthesis of multiple experiments, which concluded that, while biodiversity of primary producers simultaneously increases both the production and stability of biomass in terrestrial and aquatic ecosystems, these effects are independent, suggesting that an ecosystem process and its variability would not necessarily reach the highest levels simultaneously with biodiversity ( Cardinale et al., 2013 ).

Taxonomic vs. Functional Diversity Effects on Resource-Use Efficiency

Our analyses included the evaluation of the performance of three FD metrics resulting in the same patterns as their TD counterparts. This is in line with other works that pointed out the validity and potential of using phytoplankton functional traits to explain community assembly and ecosystem processes both in fresh (e.g., Leruste et al., 2018 ) and marine (e.g., Breton et al., 2017 ) waters. However, our results showed that FD metrics were slightly poorer predictors of RUE than TD metrics. This observation differs from what has been previously shown by other comparative studies undertaken in fresh ( Abonyi et al., 2018 ) and marine ( Ye et al., 2019 ) waters where FD usually outperforms TD in predicting ecosystem functioning. This discrepancy could be explained in part because the set of traits included in our analysis is insufficient and does not fully capture the fine variability along the species-specific ecological niches. For instance, quantitative light utilization traits and nutrient utilization traits are determinant for species-specific metabolism; thus, both functions, efficient use of light and nitrogen acquisition, might not be well represented reducing the percentage of variance of RUE explained by FD. Functional traits are typically sourced from published laboratory experiments, usually not available for all studied species (as would be the case here) but for the most common species, thus preventing a thorough examination of FD effects on RUE for this particular upwelling ecosystem. Apart from the comparison in explanatory power between TD and FD, it is worth mentioning that, in contrast to TD, FD evenness outperformed FD richness, which was a poor predictor, suggesting that FEve captures better the distribution of species across the functional trait space and its effect on RUE. Furthermore, the observed effects of FDis highlight that an increase in functional similarity of phytoplankton increases phytoplankton production per unit of nitrate, whereas at the same time, zooplankton biomass per phytoplankton biomass was higher when phytoplankton assemblages were more functionally dissimilar. This would reinforce the hypotheses that a selection effect would occur when trait dissimilarity was low in diatom-dominated assemblages inducing greater phytoplankton RUE and lower zooplankton RUE. In line with this, Cadotte (2017) using experimental plant assemblages found opposing relationships between selection (negative) and complementarity (positive) effects with functional dispersion.

Study Limitations

Our study has some limitations. On the one hand, we have not accounted for potential cell size changes through time that might bias biomass calculations as size is expected to decrease when temperature increases ( Morán et al., 2010 ). On the other hand, and more importantly, we acknowledge that we have worked with an incomplete number of species. It is known that conventional sampling methods may underestimate phytoplankton diversity ( Cermeño et al., 2014 ); however, we used a very specific assemblage that made the dataset more homogeneous along the time series. This assemblage was indeed representative as shown by the relationship between chlorophyll a and phytoplankton carbon, and covered the most abundant species occurring in this region in each recognized upwelling/relaxation/downwelling phase ( Casas et al., 1997 ). Furthermore, this assemblage was able to explain the variability in phytoplankton and zooplankton RUE calculated in terms of chlorophyll a . Additionally, our definition of RUE was based on the quantification of biomass production using a measure of standing stock thus not accounting for gross production; nonetheless, using a direct measure of primary production (secondary production was not available), we also found comparable patterns in the dynamics of RUE pp . These complementary analyses suggest that the patterns found are robust. Therefore, we believe that, given the consistency of the sampling methods along the three decades, and the comparable results obtained using different RUE calculations, the facts outlined above could have some influence on the effect size of our models, for instance, by minimizing the effect of rare species, though not on the direction of the effects. Finally, we assumed here a single resource limitation (nitrate); thus, further investigations are needed to explore the importance of colimitation or multiple resource limitation ( Hodapp et al., 2019 ) following the dynamics of upwelling.

Conclusions

We found that taxonomic and functional diversity enhance planktonic resource use efficiency in a coastal upwelling system. The sign of this relationship differed with the components of diversity considered (richness vs. evenness) and had opposite patterns across trophic levels. These effects can be likely explained through a simultaneous complementarity effect, by which more diverse assemblages with more diverse functional traits use resources more efficiently, and a selection effect through the dominance of diatoms growing rapidly and exploiting typical upwelling-intermittent nutrient pulses more effectively than other taxa of similar cell size. Additionally, resource use efficiency was less variable at higher levels of diversity; however, greater stability was not associated with the highest levels of RUE. Our results contribute to the understanding of phytoplankton diversity effects on ecosystem processes underpinning the differing effects of two contrasting biodiversity metrics and how these relationships vary across adjacent trophic levels in a coastal upwelling area. Incorporation of this approach to the observation, experimentation, and modeling of plankton ecology in EBUEs will allow gaining understanding on the important processes occurring in these productive coastal ecosystems and how these processes are mediated by biodiversity.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.1594/PANGAEA.908815 ; https://doi.org/10.1594/PANGAEA.885413 .

Author Contributions

JO designed the project. AB provided the data. JO analyzed the data with assists from AB and XÁ-S. JO wrote the manuscript with comments from co-authors. All authors contributed to the article and approved the submitted version.

This work is part of the time series project RADIALES conducted and funded by the Instituto Español de Oceanografía ( http://www.seriestemporales-ieo.net ) with additional support from project MarRisk (Interreg POCTEP Spain-Portugal) grant number 0262 MARRISK 1 E, from grants Contrato-Programa GAIN-IEO, and grant number IN607A2018/2 of the Axencia Galega de Innovación (GAIN, Xunta de Galicia, Spain). JO was supported by a Junta para la Ampliación de Estudios Fellowship (JAE-Doc programme 2011) from the CSIC and ESF.

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.

Acknowledgments

This article is dedicated to the memory of Dr. Manuel Varela, respected friend and colleague, who passed away on October 24, 2019. We would like to acknowledge the dedication of a large number of technicians, crew members, and scientists who contributed to the observational time series project RADIALES. We are particularly indebted to M. Varela (phytoplankton) and M. T. Álvarez-Ossorio (zooplankton) for maintaining the plankton series for more than 20 years. J. Lorenzo continued the phytoplankton counts since 2011, and E. Rey and M. A. Louro ensured the continuity of the zooplankton series. Nutrient data were provided by N. González and R. Carballo (1989–2012) and by M. Álvarez and M. Castaño (2013–2016).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2020.592255/full#supplementary-material

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Keywords: coastal upwelling, stability, functional diversity, taxonomic diversity, zooplankton, phytoplankton, nutrients, resource use efficiency

Citation: Otero J, Álvarez-Salgado XA and Bode A (2020) Phytoplankton Diversity Effect on Ecosystem Functioning in a Coastal Upwelling System. Front. Mar. Sci. 7:592255. doi: 10.3389/fmars.2020.592255

Received: 06 August 2020; Accepted: 20 October 2020; Published: 26 November 2020.

Reviewed by:

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*Correspondence: Jaime Otero, jotero@iim.csic.es

† Present address: Jaime Otero, Centro Oceanográfico de Vigo, Instituto Español de Oceanografía, Vigo, Spain

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