Functional diversity of aquatic ciliates

Available online at www.sciencedirect.com

ScienceDirect European Journal of Protistology 61 (2017) 331–358

Functional diversity of aquatic ciliates Thomas Weisse University of Innsbruck, Research Institute for Limnology, Mondsee, Mondseestrasse 9, 5310 Mondsee, Austria Available online 13 April 2017

Abstract This paper first reviews the concept of functional diversity in general terms and then applies it to free-living aquatic ciliates. Ciliates are extremely versatile organisms and display an enormous functional diversity as key elements of pelagic food webs, acting as predators of bacteria, algae, other protists and even some metazoans. Planktonic ciliates are important food for zooplankton, and mixotrophic and functionally autotrophic species may significantly contribute to primary production in the ocean and in lakes. The co-occurrence of many ciliate species in seemingly homogenous environments indicates a wide range of their ecological niches. Variation in space and time may foster co-occurrence and prevent violating the competitive exclusion principle among ciliates using the same resources. Considering that many ciliates may be dormant and/or rare in many habitats, ciliate species diversity must be higher than can be deduced from simple sampling techniques; molecular methods of identification clearly point to this hidden diversity. From a functional point of view, the question is how much of this diversity represents redundancy. A key challenge for future research is to link the ecophysiological performance of naturally co-occurring ciliates to their functional genes. To this end, more experimental research is needed with with functionally different species. © 2017 The Author. Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Functional redundancy; Functional response; Functional trait; Ecological networks; Metagenomics; Numerical response

Introduction

Abbreviations: α, initial slope of the functional response; CAP, canonical analysis of principal coordinates; CWM, community weighted mean; e, conversion efficiency; d, mortality rate; EE, evolutionary ecology; FD, functional diversity; FDiv, functional divergence; FDQ , Rao’s quadratic entropy; FDvar , functional logarithmic variance an index of functional divergence; FE, functional ecology; FEve, functional evenness; FR, functional richness; FT, functional trait; FTD, full trait distribution model; Imax , maximum prey ingestion rate; k, half saturation constant; LOD, lorical oral diameter; M, body or cell mass; ω, prey selectivity of a predator; OTU, Operational Taxonomic Unit; PCA, principal component analysis; PCoA, principal coordinates analysis; , edibility of prey; R, metabolic rate; Rc , range of a functional trait; RDA, redundancy analysis; rmax , maximum specific growth rate; SD, standard deviation; SF, niche space; V’, threshold food concentration. E-mail address: [email protected]

Ecology, as the scientific analysis of interactions among organisms and their environment, aims to understand the distribution and abundance of organisms in any ecosystem on Earth. Ecology can be divided in several subdisciplines; for instance, autecology investigates single species, while synecology is concerned with communities. Applied ecology considers the application of ecological principles to real-world questions such as environmental management and conservation biology. Theoretical ecology uses simple conceptual and advanced mathematical models and numerical simulations to study the performance of populations, communities, and ecosystems. The various fields and approaches of ecology evolved since the first natural history studies by Hippocrates and Aristotle; the term ‘ecology’ was termed by Ernst Haeckel in 1866 (Haeckel 1866). Contemporary

http://dx.doi.org/10.1016/j.ejop.2017.04.001 0932-4739/© 2017 The Author. Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

332

T. Weisse / European Journal of Protistology 61 (2017) 331–358

ecology follows three different approaches: (1) descriptive ecology, based upon natural history, describes the occurrence of species in their environment; (2) functional ecology (FE) was established approximately 30 years ago (Calow 1987) as the branch of ecology that investigates the functions that species have in the community or ecosystem in which they occur (reviewed for protists by Weisse et al. 2016). Functional ecology is closely related to (3) evolutionary ecology (EE). The major difference between FE and EE is that, in contrast to the former, the latter considers the historical context, i.e. tries to understand the organisms and their functions as products of evolution. This article deals with functional diversity of ciliates, i.e. a monophyletic group of single-celled eukaryotes. The significance of ciliates for biogeochemical processes in terrestrial and aquatic ecosystems, as model organisms for cell biology, biochemistry, molecular genetics, and ecology has been reviewed elsewhere (Hausmann and Bradbury 1996; Weisse et al. 2016). Although I will consider primarily free-living aquatic ciliates, the general principles discussed in the following apply to terrestrial species as well. Functional diversity (FD) is the concept inherent in FE, linking species’ properties to ecosystem processes via functional traits (Brocchieri 2016; Scheiner et al. 2016; and references therein). Resolving the extent to which the structure and dynamics of ecological communities depend on the traits of their component species has been identified as one of the most challenging questions of current ecology (Sutherland et al. 2013). Functional traits (FTs) are components of an organism’s phenotype that influence ecosystem level processes (Dıáz and Cabido 2001; Petchey and Gaston 2006). Note that, mainly for historic grounds and application of the trait concept by different disciplines, this definition commonly used in FE deviates from that used by other authors in different fields (reviewed by Violle et al. 2007). Violle et al. (2007) define a functional trait as “any trait which impacts fitness indirectly via its effects on growth, reproduction and survival”, i.e. their level of definition and application of FTs is the individual, not the ecosystem. I will use FTs mainly concerning their effect at the community and ecosystem level. Accordingly, it is crucial to identify those functional traits of ciliates that may affect ecosystem processes. This distinction in the trait concept is important in the context of functional redundancy (see Section Implications of functional diversity for ecosystem dynamics, below), i.e. when not all organismal traits that influence the performance of the individuals affect ecosystem functioning. Before addressing FD of ciliates in detail, I will briefly summarise the concept of FD research and the most common metrics to measure FD, and discuss some general issues such as diversity–productivity, diversity–stability, and diversity–invasibility relationships. There are several articles studying important aspects of ciliates’ FD such as those related to their role in food webs (e.g., Caron and Goldman 1988; Lischke et al. 2016; Sanders and Wickham 1993), but there is at present no theory relating the functional

composition of food webs to their dynamics and properties (Gravel et al. 2016). These authors reviewed for animals the application of trait-based concepts, which were originally developed for plants. Trait-based classification systems have also been developed for phytoplankton, based upon morphological and physiological FTs (Kruk et al. 2010; Litchman and Klausmeier 2008; Reynolds et al. 2002; Weithoff 2003). Functional diversity research of aquatic protists has gained considerable momentum over the past decade, when the significance of mixotrophic species for biogeochemical cycling in the ocean was detected (Flynn and Hansen 2013; Flynn and Mitra 2009; Mitra et al. 2014; Mitra et al. 2016; Ward and Follows 2016). Petchey and Gaston (2006) identified better consideration of microbial ecology in the general framework as the greatest research challenge for FD research; however, in spite of the increasing awareness of planktonic protists for global energy and nutrient cycling, systematic FD research of ciliates is still in its infancy. I will not review the literature on these ecological issues in detail; rather, the primary goal of this article is to identify open questions and to propose avenues for future research on the functional diversity of ciliates. This review is part of a series of papers in this special issue dealing with integrative biodiversity of ciliates. Integrative ecology is, together with integrative systematics and phenotypic plasticity, a major area of integrative biodiversity research of ciliates (Clamp and Lynn 2017). The inherently multidimensional nature of ecosystems calls for an integrative approach; we need to first identify and then measure the functionally most relevant variables, thereby advancing the integration of ciliate function and genetics.

Concepts and methods of functional diversity research Types of functional traits Functional traits are central for understanding how properties of organisms and species will affect ecosystem stability and dynamics. Since ciliates have a central role in planktonic food webs (Porter et al. 1979; Sherr and Sherr 1988; Weisse 2003, 2006; Weisse et al. 1990; Weisse and Sonntag 2016) I will use the food web as an example to group FTs according to their effect at the different levels ranging from pairwise interactions to the ecosystem level. The food web concept has the advantage, with some notable exceptions, that it considers processes at higher trophic levels (secondary consumers, top predators) that are commonly ignored in plant ecology (Lavorel et al. 2013). Secondly, understanding the structure and dynamics of food webs is central for predicting ecosystem functioning. The following section was guided by the recent review of Gravel et al. (2016) that was written from an animal ecologist’s perspective.


The FTs affecting (aquatic) food webs can be broadly classified into three major categories with several subcategories each (Gravel et al. 2016). (1) ‘Topological traits’ denote pairwise predator–prey interactions, i.e. which prey type will be ingested by a given predator. (a) ‘Foraging traits’ consider the prey type and are, therefore, closely related to trophic categories such as herbivores, carnivores, and omnivores. (b) ‘Vulnerability traits’ determine the sensitivity of a given prey to predation and the type of predator; they also include mechanisms to resist predation such as permanent morphological, chemical and reversible inducible defences (Tollrian and Harvell 1999). (2) ‘Consumption traits’ (Gravel et al. 2016) comprise the numerical and functional responses of the predator at a per capita level. Ingestion can be divided into a series of six mechanistic steps from searching for prey to digestion (Montagnes et al. 2008b), with the major steps of encounter (or searching) and processing (or handling). Each of these steps can be represented as a subcategory of consumption traits. In addition, assimilation and conversion efficiencies determine the fraction of the ingested prey that is converted into predator biomass, respectively used for producing offspring. The numerical and functional responses of aquatic protists have recently been reviewed, with emphasis on ciliates and dinoflagellates (Weisse et al. 2016). The distinction between topological and consumption traits is not always clear. The functional response of a predator is always based upon predator–prey interactions. I will use the term ‘consumption trait’ whenever quantification of this interaction as numerical and functional response is possible (see details below, Applying the functional diversity concept to ciliate research). (3) ‘Life history traits’ (Gravel et al. 2016) are not directly involved in trophic interactions but affect the population dynamics of prey and predator. Examples are fecundity (or, in asexually reproducing protists, specific growth rates), natural (i.e., predator-less) mortality, and the formation of resting stages. In contrast to the two former trait types, life history traits affect primarily the population level. The above three main categories represent all functional ‘effect traits’, determining the effect a species has on ecosystem functioning (Lavorel and Garnier 2002; Lavorel et al. 2013). Traits associated with the response of (plant) species to environmental factors such as resources and disturbances were termed ‘response traits’ by Lavorel and Garnier (2002). Examples for response traits are body size and sensitivity to ambient environmental factors. Body size (or cell size in single-celled organisms) is an important variable with several implications for food webs (Azam et al. 1983); details will be discussed in Section Applying the functional diversity concept to ciliate research.

333

It is clear that all organisms are characterized by more than one of the above traits. The originality of a species and its functional distinctiveness are important for quantifying how a given species contributes to the functional diversity of a community and to ecosystem functioning (Gravel et al. 2016; Pavoine et al. 2005). Functional distinctiveness (or uniqueness, see below) measures the average rarity of all the features belonging to a given species, relative to all other species in the community. A recent meta-analysis from highly diverse ecosystems concluded that most functionally distinct combinations of traits are supported predominantly by rare species, both in terms of local abundance and regional occupancy (Mouillot et al. 2013). This issue is related to the role of rare species that is currently controversially debated for ciliates and other microbes (reviewed by Dunthorn et al. 2014; Weisse 2014).

Measuring and weighting functional traits There are four levels involved in measuring FD (Petchey and Gaston 2006): • identifying and measuring important FTs • weighting them according to their relative functional importance • measuring trait diversity using appropriate statistics • measuring and predicting functional variation at the ecosystem level. The different functional traits described in the previous section are measured in different units. To compare the relative effect of each trait, FTs have to be standardized so that traits have a mean of zero and SD of 1, implying that each trait has the same weight in FD estimates and the units used to measure FTs have no influence (Legendre and Legendre 2012; Petchey and Gaston 2006; Villéger et al. 2008). Standardization of FTs requires that the absolute range of a trait is known for the communities under study. If this is not the case, the range may be derived from values reported in the literature (Mason et al. 2005). The trait of a given species, respectively each organism, can then be allocated to an arbitrarily chosen category (ranging, for instance, from 0 to 100, Mason et al. 2005). If all FTs to be considered have been standardized, the functional structure of a community can be characterized by the multivariate distribution of traits with their respective statistics (mean, variance, etc.) of each species and the covariance among them (Gravel et al. 2016). The functional distance between two species is defined as their distance in trait space (Fig. 1). The significance of one or several traits of a given species can be weighted by its relative abundance or biomass in the community. The closer a species is to the overall weighted mean (centroid, Coux et al. 2016), the more important it is for the community functioning (Fig. 1), i.e. the distance of a species from the centroid is a measure of its functional originality. Dominant species (A in Fig. 1) are functionally usually less distinct (i.e., have

334


Fig. 1. The concept of functional distinctiveness and originality illustrated with a hypothetical community composed of six species (modified from Coux et al. 2016). The open triangle denotes the unweighted mean, the closed triangle the mean (centroid) weighted for species abundances. The diameter of the circles is proportional to the abundance of the species. The axes can be determined by principal coordinates analysis (PCoA), principal component analysis (PCA) or other multivariate statistics to reduce the multidimensional traits to two dimensions. The dominant species A is close to the centroid, i.e. it is more important for the community functioning than the other species, but it is functionally less distinct (functional original) than the rare species B and C. These species are distinct but, since they are close to each other, have a lower functional uniqueness than species A.

lower functional originality, because they are closer to the centroid) than rare species (B, C in Fig. 1). The position of the species in Fig. 1 relative to the centroid determines its functional originality but does not allow inferences about its functional uniqueness (Coux et al. 2016). Functional uniqueness is measured by the Euclidean distance of a species to its nearest neighbour, i.e. species B and C in Fig. 1 have a lower uniqueness than species A. Euclidean distance is the straightline distance between two points in a Euclidean plane such as in Fig. 1 or in a three-dimensional Euclidean space, and the distance is calculated by the Pythagorean formula. Note that the term ‘uniqueness’ defined by Coux et al. (2016) deviates from its colloquial use because it implies a comparison with other species; functionally similar species are all but unique. In contrast to functional originality, functional uniqueness does not depend on the species’ relative abundances. In a three-dimensional ordination, the centre of gravity represents the community mean and the size of the space around it is a measure of FD (Weithoff 2003). Coux et al. (2016) also noted that functional uniqueness is conceptually the opposite of functional redundancy (see Section Implications of functional diversity for ecosystem dynamics, below).

Community-aggregated or community weighted mean (CWM) values of functional traits are used to compare the functional distance between communities; in both cases, trait values are weighted according to the relative abundance of species (Garnier et al. 2004; Májeková et al. 2016). The CWM is probably the most widely used single-trait metric in FD research (Muscarella and Uriarte 2016 and references therein). For any combination of FTs, FD represents the dispersion of the traits around their means (Coux et al. 2016; Gravel et al. 2016). However, this implies that the value of each (standardized) trait and its biological variation are equally important, an assumption that is probably not realistic; the question is, how to weight the amount of variation of FTs objectively (Petchey and Gaston 2006). Májeková et al. (2016) provided a practical framework (the R package “traitor”, available at https://github.com/larsito/traitor) to assess how missing trait data may affect FD estimates. By gradually removing data, beginning with the least abundant species and their associated trait information, the effect of first rare and subsequently more common species on FD indices can be calculated. Májeková et al. (2016) also noted that sensitivity of the indices to missing trait information depends on the skewness of the data (trait values related to size or weight are often positively skewed); the more normal the distribution is, the less sensitive are most FD indices to missing trait information. Data transformation, reducing the skewness, may significantly improve the accuracy of several FD indices (Májeková et al. 2016). Standardization is relatively straightforward for some scalable FTs such as body size (which can easily be grouped from the smallest to the largest) and some traits related to predator–prey interactions (e.g., consumption rate of a given prey by a given predator) but more difficult for categorical variables and binary variables that, in principle, can take on exactly two values (e.g., the presence or absence of cyst formation). The ability to encyst is an important life history trait enabling survival of many ciliate species under temporarily adverse environmental conditions. Care must be taken to avoid placing too much emphasis on those easy-to-measure FTs. For instance, if more morphological than physiological and life-history traits are considered, equal weighting would give morphology more influence than the other variables (Petchey and Gaston 2006). To compensate, more weight may be given to important physiological traits (e.g., Roscher et al. 2004), although such weighting seems arbitrary. Similarly, the use of wide categories and collective terms (‘nutrients’, ‘food’) has little meaning for FD analyses. If a ciliate species is omnivorous and its feeding spectrum is known, different scores can be used to differentiate between various food sources (for further details, see Sections Implications of functional diversity for ecosystem dynamics and Applying the functional diversity concept to ciliate research, below). Motility may be considered a binary category (sessile vs. motile species), but, among the motile species, it is


a continuous variable, depending on intrinsic and several ambient factors. Furthermore, sessile species usually have a motile stage to facilitate dispersal, and some sessile ciliates such as many peritrichs may be symphorionts, living as quasi-planktonic organisms attached on algae (e.g., diatoms, filamentous cyanobacteria), rotifers, microcrustaceans and other pelagic metazoans (Foissner et al. 1999; Lynn 2008). Similar to further discrimination between ‘herbivorous’ or ‘omnivorous’ species, different scores can be used to differentiate between the various degrees and forms of motility. Cyst formation may be overlooked if only a small percentage of a ciliate population encysts (Müller et al. 2002; Müller and Wünsch 1999). An example for a species reaching almost complete encystment is the tide-pool ciliate Strombidium oculatum that exhibits an endogenous circatidal behaviour (Montagnes et al. 2002); at low tide, the ciliate is freeswimming in pools, and shortly before flushing of the pools by the incoming tide, >90% of the population encyst on a substrate. The observations were conducted with two populations in the Irish Sea; sequence analyses of the ITS region and the SSU rDNA gene from several wild populations collected from tide pools in the North Atlantic Ocean later revealed that S. oculatum comprises several morphologically almost identical cryptic species with apparently similar behaviour (Katz et al. 2005; McManus et al. 2010). In spite of this taxonomic ambiguity, the example illustrates that, even if the ability to form cysts is a binary variable for a given species (i.e., yes or no), it can be considered a continuous variable at the population level, ranging from zero to almost 100% of the population undergoing encystment. Functional diversity is analyzed by multivariate statistics, such as cluster analysis and various ordination techniques (briefly summarized by Chen (2015) and Weithoff (2003); for a detailed account, consult Legendre and Legendre (2012) and Zuur et al. (2007)). Villéger et al. (2008) provided a general practical framework for FD analysis in the ecosystem context, which I briefly summarize and compliment with a few notes: • Each species j in the community under study has T traits of standardized values (xj 1 , xj 2 , . . ., xjT ,) which represent coordinates in the multidimensional functional trait space. The values should be listed in a matrix with selected functional traits for each species (functional matrix). • If not all traits are quantitative and continuous, the functional matrix is transformed into a distance matrix. • The functional trait matrix and the species distance matrix are complimented by an ‘abundance pattern’ matrix, listing the relative abundance (or biomass) of each species in each community under study. • Ordination techniques are used to reduce the multidimensional functional diversity to two or three axes. If all FTs are quantitative and continuous, principal component analysis (PCA) can transform a number of possibly correlated variables (FTs) into a smaller number of uncorrelated variables (principal components), so that similarities of the

335

data can be visualized in a two-dimensional (biplot) or three-dimensional plot. If not all traits are quantitative and continuous (e.g., if binary variables are included), Principal Coordinates Analysis (PCoA) is commonly used to explore and visualize dissimilarities of the data (Fig. 1). The species distance matrix is thus transformed into a species-coordinates matrix. The first two or three axes generated by ordination techniques ideally explain a large amount of the variation in the data set (e.g., Legendre and Legendre 2012; Weithoff 2003). • These matrices can then be used to calculate the FD indices described further below and to visualize correlations between various indices such as, e.g. the relationship between species diversity and FD (Weithoff 2003). • The values of the FD indices can be linked to matrices of environmental variables and ecosystem properties (e.g., productivity, nutrient cycling, resilience, susceptibility to invasion) to analyze response traits and effect traits. Functions for various matrix operations are available in the statistical language R (R Core Team 2014).

Characterizing and measuring functional diversity Thus far, I have addressed mainly the first two steps of measuring FD, i.e. (1) identifying and measuring important FTs and (2) weighting them according to their relative functional importance. I now turn to the third level, measuring trait diversity using appropriate statistics. In analogy to measuring species diversity, Mason et al. (2005) proposed to measure FD in terms of richness, evenness and divergence. While functional richness (FR) measures how much niche space is filled (‘convex hull’, i.e. the overall area of trait space occupied by species in a community; Boersma et al. 2016 and references therein), functional evenness (FEve) and functional divergence (FDiv) describe how this space is filled (Schleuter et al. 2010). The niche space is defined by the range of environmental conditions for any given variable (e.g., temperature, food, pH); in theory, if all variables that may affect a species’ performance are considered, the combination of many variables in a n-dimensional hypervolume characterizes the niche of a species. The niche space is thus an abstract environmental space in which population dynamics is gauged (Holt 2009). The response of a given species to an environmental variable can be assessed by the species reaction norm (response curve), which often follows a normal distribution (Fig. 2A and B). The minimum and maximum value of the variable where the species can thrive determines its tolerance, the peak value its optimum for the variable under consideration. For instance, if water temperature in a small, eutrophic temperate water body varies seasonally between 0 ◦ C and 30 ◦ C and pH ranges from 7 to 10, this defines the temperature × pH niche space for the microorganisms dwelling in this pond (Fig. 2C). This niche space is further expanded, i.e. receives additional dimensions,

336


Fig. 2. Sketch of reaction norms of five hypothetical species for two environmental variables (A, B) and niche space (C). The axes of the niche space can be calculated by principal component analysis or other ordination techniques to include more than the two variables shown in A and B. The reaction norm denotes the range of tolerance of a species, indicating the minimum (left x-axis intercept) and maximum (right x-axis intercept) values of the variable tolerated, and the optimum, where the species’ biomass peaks. The rectangle represents the range of environmental conditions (niche space). The circles and ellipsoids represent the niches of individual species that may partially overlap. The niche space is (i) not completely filled and (ii) unevenly filled.

if more variables such as resource supply and predation are considered. Analogous to species richness, FR is independent of species abundance, i.e. it only considers if a species is present or absent under a set of environmental conditions (Mason et al. 2005). Species are not the only currency of FD estimates; for instance, if individuals or size classes, not species, are selected, the estimate of FD becomes insensitive to species richness. This is important for many protists with uncertain species delimitation, including cryptic species (Caron et al. 2012; de Vargas et al. 1999; Fenchel 2005; Nanney et al. 1998). A typical example of allocating organisms to nontaxonomic categories is the body size spectrum for a pelagic community (Gaedke 1992; Sheldon et al. 1972; Straile 1998). Here, instars or juveniles and adults of the same metazoan species fall into several size categories, i.e. the chosen number of size categories may exceed species number. This may also apply to some polymorphic ciliates such as the predatory species Tetrahymena vorax (reviewed by Gronlien et al. 2011) and Lembadion bullinum (Kopp and Tollrian 2003; Kuhlmann 1993), but, since most pelagic ciliates fall into the size range of 10–50 ␮m, the opposite is true for most planktonic ciliate communities. The proportion of niche space filled for the trait can then be calculated (Mason et al. 2005) as SFci FRci = (1) Rc

Fig. 3. Graphical illustration of functional evenness. The same functional trait (e.g., body or cell size) is shown for two hypothetical communities (A, C) composed of five species each in response to two environmental variables. Community A fills the available niche space regularly, i.e. it has a high functional evenness. Community C fills the available niche space irregularly, i. e. its functional evenness is lower. The arrows in D point to under-utilized niche space.

with FRci = the functional richness of functional trait c in community i, SFci = the niche space filled by the species within the community, and Rc = the absolute range of the trait. Taking the above example of temperature ranging from 0–30 ◦ C, we may allocate the species into categories of 0.25 ◦ C width each, yielding a total of 120 categories. If species occur in each of these categories, FRci is 1. In reality, due to seasonal variation of temperature and species composition, FRci will almost always be lower than 1 in any given water body. Therefore, although functional richness has no upper limit because it quantifies an absolute volume filled in a multidimensional space (Villéger et al. 2008), the standardized FRci index is constrained between 0 and 1, meeting one of the most important criteria of FD indices (reviewed by Villéger et al. 2008, their Table 1). Functional evenness (FEve) indicates whether mean species traits are distributed regularly within the occupied trait space, i.e., with equal distances between nearest neighbours and equal abundances (Schleuter et al. 2010). A high FEve indicates a regular distribution (Fig. 3B), a low FEve points to the existence of clusters of species and/or abundances (Fig. 3D). Functional divergence (FDiv) analyzes the degree of divergence in the abundance distribution of species’ functional


traits. Functional divergence is high if the most abundant species occur close to the extreme values of the functional trait range and low when the most abundant species have FTs that are close to the centre of the functional trait range. High FDiv is likely in habitats were specialist species prevail and may be indicative of disruptive selection (Coutinho et al. 2016). Functional divergence has been measured in several different ways; as taxonomic distinctness, genetic variation, or as functional complexity (reviewed by Mason et al. 2005). Functional divergence can be calculated as the abundanceweighted functional variance using the mean character value for each of the species in a community. However, the respective metric must be unaffected by the number of species and the measurement units used (Mason et al. 2003). Note that the functional divergence index (FDvar ) presented by Mason and colleagues, which is based on an abundance-weighted sum of squares, was considered a metric for functional diversity in their original publication (Mason et al. 2003) but, more restrictively, denoted a metric for functional divergence in their subsequent work (Mason et al. 2005). Because Mason et al. (2003) log-transformed the trait values before calculating the variance, Schleuter et al. (2010) termed the FDvar index functional logarithmic variance; arctangent transformation and a scaling fator of 5 are used in order to restrict the FDvar index between 0 and 1. There are at least three other one-dimensional indices available to calculate FDiv (reviewed by Schleuter et al. 2010). The most common multivariate index of FDiv is Rao’s quadratic entropy (RaoQ or FDQ ; Rao 1982a,b; see also Botta-Dukát 2005). Similar to Simpson’s species diversity index (Simpson 1949), this index calculates the abundanceweighted variance of the dissimilarities between all species pairs (e.g., Májeková et al. 2016; Schleuter et al. 2010). There are many more indices available to compute FR, FEve and FDiv (e.g., Mason et al. 2005; Mouchet et al. 2010; Ricotta 2005; Schleuter et al. 2010; Villéger et al. 2008). If the main criteria for functional diversity indices are that they are able to deal with several FTs, take into account abundances, and measure all the facets of functional diversity, none of the indices meets all the criteria; instead, the use of three or more complementary indices should come close to meeting these criteria (Mason et al. 2005; Villéger et al. 2008). The details of the assumptions and calculations of the FD indices are beyond the scope of this article. The statistical language R (R Core Team 2014; see also Borcard et al. 2011) offers several freely available scripts such as the ‘FD package’ (Laliberté et al. 2014), the package ‘vegan’ (Oksanen et al. 2017), and the ‘traitor package’ (Májeková et al. 2016) to calculate these indices. A short overview on the most common FD metrics was presented by Chen (2015), and more information for computing functional diversity is given in the user manual of the free software ‘FDiversity’ (Casanoves et al. 2008, 2011; https://sites.google.com/site/functionaldiversity/downloads), which is linked to R. For further details on FD indices, the

337

interested reader is referred to the review by Schleuter et al. (2010), who listed three FR indices, two FEve indices, five FDiv indices, and proposed three more indices.

Implications of functional diversity for ecosystem dynamics The values of FEve and FDiv (measured on an arbitrary scale ranging from 0 to 1) have important corollaries for ecosystem dynamics. High FEve (Fig. 3B) implies that the available niche space has been used widely, reducing the chances for new species to invade; in contrast, low FEve indicates that some niche space is preferred and other underutilized, tending to decrease productivity and to increase the chances of invasion (Fig. 3D). Similarly, high FDiv indicates a high degree of niche differentiation, and thus low resource competition (Mason et al. 2005). However, the cooccurrence of many ciliate and other microbial species in homogenous environments such as the pelagic open ocean with seemingly nearly identical ecological niches (e.g., with respect to food requirements and response to salinity and temperature) may indicate functional redundancy. Functional redundancy or ecological equivalence implies that several or even many functionally similar species provide biological buffering capacity, allowing relatively stable community functions (e.g., primary production, carbon mineralization) in spite of taxonomic changes (Allison and Martiny 2008; Caron and Countway 2009; Rousk et al. 2009). In line with this notion, a metatranscriptome (mRNA) analysis of 21 European mainland freshwater lakes provided evidence that the stable relative importance of different metabolic pathways at the ecosystem level is maintained through functional redundancy of distinct taxa (Grossmann et al. 2016). In contrast to this postulate, oceanic bacteria exhibited low levels of functional redundancy (Fuhrman et al. 2006), connoting the presence of well defined, probably narrow niches for the bacteria (Fuhrman 2009). Similarly, from their microcosm experiments conducted in several freshwater ecosystems, Delgado-Baquerizo et al. (2016) found a lack of functional redundancy in the relationship between bacterial diversity and ecosystem functioning. More investigations with bacteria and eukaryotes are needed to experimentally examine the relationship between taxonomic diversity and the stability, resilience and predictability of ecosystems (Caron and Countway 2009). Obviously, the extent of functional redundancy depends on the widths of the functional categories being used. In broad categories such as ‘bacterivores’ there is certainly functional redundancy among marine ciliates and metazooplankters. However, at a finer functional scale the functional redundancy of pelagic ciliates is still an open question. Dolan and co-workers grouped tintinnids into 4 ␮m wide size classes of their lorical oral diameter (LOD) in several oceanic regions (Dolan and Torréton 2006; Dolan et al. 2009, 2013); they found high species diversity in many size classes

338


and concluded that this reflects the fact that accompanying each dominant (‘core’) species are many ecologically similar (‘occasional’) species, indicating that species diversity may greatly exceed ecological diversity in the plankton. The core species were distributed unevenly between the LOD size classes and did not show any obvious similarities in overall size and shape (Dolan et al. 2009, 2013). Similar conclusions have been reached for periphytic and biofilm-dwelling ciliates in coastal waters of the Yellow Sea (Xu et al. 2014; Zhao et al. 2016; Zhong et al. 2014). However, Zhang et al. (2017) recently reported that a common ciliate of the genus Strombidium was an as yet undescribed cryptic species with a peculiar nutritional mode; this ciliate, which occurs in coastal waters off Hong Kong at abundances ranging from 50 to 25,700 cells L−1 , feeds on progametes of the giant dinoflagellate Noctiluca scintillans. Most likely, this topological trait is shared, if at all, by few other ciliate species. Accordingly, this Strombidium sp. may have an overproportionate impact on FD estimates, especially at its peak abundance, but also if it is rare. Therefore, the prevailing opinion that functionally redundant species belong to the rare ciliate species (Caron and Countway 2009; Dolan et al. 2009; Weisse 2014) needs to be tested. If the assumption holds, variation in space and time between dominant and rare species may foster their co-occurrence and prevent violating the competitive exclusion principle (Hardin 1960) among ciliates using the same resources. Mainly because of its importance for agriculture, the relationship between FD and productivity has for a long time received wide attention (reviewed by Tilman 1999, 2001). Two models predict that primary productivity increases with FD of plants: (1) the sampling effect, because there is a greater chance that a more productive species is present at higher than at lower diversity, and (2) the niche differentiation effect, because a more diverse species community would better cover habitat heterogeneity. Tilman (2001) discussed empirical and experimental evidence supporting these two models, which both predict more complete utilization of limiting resources at higher FD. Similarly to the diversity-productivity relationship, it was proposed more than 50 years ago that greater diversity causes greater stability of ecosystems (Elton 1958; see also Richardson and Pyˇsek 2007 and the reviews by Tilman 1999, 2001). Tilman (2001) concluded that there is strong evidence that communities with greater FD are more stable. Greater ecosystem stability implies lower susceptibility to perturbation and more resistance to invasion. With the rapid accumulation of new information arising from molecular analyses (see Section New insights from molecular data, below), a major question is how much of the observed enormous genetic variability is ecologically and thus evolutionarily, neutral. If the vast majority of the measured variability is functionally neutral, there is no benefit for community FD and resistance to perturbation. Similarly, ecosystem resilience to disturbance would be unaffected. An inherent difficulty is that it is virtually impossible to pre-

dict that what appears to be neutral now may not become functional in the future, if the environmental conditions change (Weisse 2014). Likewise, the presumed ecological equivalence and functional redundancy of many (marine) microbes including ciliates discussed above is an abstract concept that cannot be tested rigorously because we can never fully account for the n-dimensionality of ecological functions. However, if the categories (traits) are unequivocally defined a priori, it is possible to measure the effect of the absence or presence of target species on the community and ecosystem level. For instance, it has recently been demonstrated that mixotrophy increases trophic transfer efficiency and vertical carbon flux in a model of the ocean’s food web (Ward and Follows 2016). Accordingly, although constitutive mixotrophs have the innate ability to photosynthesize (Mitra et al. 2016), similar to photoautotrophs (“algae”), these guilds are not identical but complementary to each other; in other words, obligate mixotrophs (which always photosynthesize) are not functionally equivalent to photoautotrophs. Boersma et al. (2016) provided a graphical framework for visualizing ecological phenomena in trait space to guide the selection, application, and interpretation of quantitative FD methods. These authors compared an undisturbed to a disturbed community and illustrated, for instance, that disturbance may lead to considerable functional turnover (i.e. little overlap in trait space between undisturbed and disturbed communities). Similarly, the Convergence/Divergence Hypothesis, which is the most prominent in the FD literature, can be tested using the appropriate metric (FR, Eq. (1), above) and visualized in the respective plot. In principle, the plots, which are similar to Fig. 1 if species are replaced by communities, can be used to compare any communities.

Applying the functional diversity concept to ciliate research Application of the functional diversity concept outlined in the foregoing to ciliates is still in its infancy. For instance, in evolutionary terms, sex (conjugation) may be the most important life history parameter of ciliates, producing more ecologically fit exconjugants in the long term, but little is known of its significance for most species in the field (Dunthorn and Katz 2010; Weisse 2014; Weisse and Sonntag 2016). Accordingly, to my knowledge, the question if asexual reproduction is regularly intermitted by conjugation has been ignored thus far in FD analyses of ciliates. Laboratory microcosm experiments using protists as model organisms are promising tools to investigate general concepts in population biology, community ecology and evolutionary biology (reviewed by Altermatt et al. 2015; Montagnes et al. 2012); however, they have only sporadically been applied to investigate functional ecology of ciliates. One notable exception is the study of ciliates found in the waterfilled leaves of the purple pitcher plant (Sarracenia purpurea)


(Miller et al. 2014); key FTs of several species/strains of the ciliate Colpoda were subjected to experimental evolution (TerHorst 2010, 2011). In his competition experiments with Colpoda spp. and Pseudocyrtolophosis alpestris, another colpodid ciliate, TerHorst (2011) measured six FTs: cell size, cell speed, peak population density, population growth rate, habitat use, and cyst production. The main effects of interspecific competition were that Colpoda populations had smaller cell sizes, produced fewer cysts and had higher population growth rates relative to populations grown in monoculture (i.e., only with intraspecific competition). Surprisingly, when predatory mosquito larvae (Wyeomyia smithii) were added to this experimental system to study simultaneous effects of predation and competition, trait values of Colpoda spp. were similar to those in the monoculture treatment, indicating that direct effects of predation were offset by strong indirect effects on the evolution of traits (TerHorst 2010). In their review on functional diversity of soils protists, Coûteaux and Darbyshire (1998) focused on species richness and feeding guilds. However, these authors noted that other characteristics should be included for FD estimates of soil protists, i.e. morphotypes (size, body and shape) and ‘physiotypes’ (food preference, microhabitat type, life cycle, ability to encyst, and r- and K-life strategies). At the end of their review, Coûteaux and Darbyshire (1998) briefly discuss the response of ciliates and the other soil protist taxa to nutrient availability. Zhao et al. (2016) analyzed the functional diversity of biofilm-dwelling ciliates in coastal waters of the Yellow Sea (China), combining body size spectra of ciliates with their feeding classification. These authors reported seasonal variation in body size patterns of ciliates at four sampling stations that were related to changes in environmental variables such as temperature, pH, nutrients, and dissolved oxygen. Similarly, Xu et al. (2016), analyzing the same data set, correlated ciliate body-size diversity with spatial changes of environmental variables, especially nutrients. These authors conducted canonical analysis of principal coordinates (CAP) on Spearman rank correlation of body size classes and taxonomic distinctness (ranging from species to phylum level) and Euclidean distance matrices of abiotic variables. Xu et al. (2016) concluded that the ecological parameters based on body-size spectrum may be used as potential bioindicators of water quality status. However, although FD may be correlated to each of these environmental variables, they cannot be treated equally as factors affecting FD. As I will discuss in detail below, we can quantify the effect of scalable factors such as temperature, pH and oxygen on the functional and numerical response of ciliates. However, response to collective factors such as ‘food’ and ‘nutrients’ (or substrates, in general terms) is, in most cases, only indirect. These terms usually include various substrates of varying nutritional quality (see Section Numerical and functional responses of aquatic ciliates, below). For some tintinnid ciliates with agglomerated lorica in the genera Acanthostomella, Stenosomella, Codonella and Tintinnopsis, the seeming link to food

339

may also indicate a relationship to material (diatom frustules, coccoliths from coccolithophorid algae) used for constructing the ciliate lorica (Agatha et al. 2013; Dolan 2013). Similarly, nutrient requirements of mixotrophic species may be highly specific, depending, inter alia, on light availability, supply with adequate heterotrophic food, stoichiometry and the nutritional history of the species (reviewed by Weisse et al. 2016). Lumping ciliates together in (too) broad resource-use categories (algivorous, bactivorous, predatory, omnivorous, detritivorous, mixotrophic) may explain why Van Wichelen et al. (2013) could not detect differences in functional composition (and ␣-diversity, calculated from species richness) between 66 turbid and clear-water shallow lakes in northwestern Europe. These authors assigned ciliate species to functional groups based on taxonomy, size, food preference, and preferred habitat. Principal component analysis (PCA), based on the functional group composition, and redundancy analysis (RDA) yielded no differences in ␣-diversity and functional composition between the ciliate communities of the two different lake types. Analogous to species diversity, the extent of functional differences among the species in a community (=functional alpha diversity), and their functional turnover (i.e. functional beta diversity) have been calculated by several authors for testate amoebae (Jassey et al. 2016 and references therein). Jassey et al. (2016) investigated testate amoeba FD in relation to increasing frost intensity across a continental gradient. These authors constructed a functional space for each amoeba species by first creating a functional distance matrix for each pair of species and then computing a PCoA on it. The first four axes of the PCoA yielded synthetic functional traits summarizing the functional space of testate amoeba, and species coordinates in the four-dimensional space defined by the PCoA were used to calculate functional metrics according to Villéger et al. (2008). Jassey et al. (2016) also performed RDA, using the vegan package in R (Oksanen et al. 2017), to relate climatic variables to testate amoeba species composition and CWM traits composition. This study by Jassey et al. (2016) revealed that peatland testate amoeba communities diverge among sites with different winter climates, and that this is reflected through contrasting functions (i.e., species replacement occurred mostly between species that are not functionally redundant). With the exception of Van Wichelen et al. (2013), such an attempt of FD analysis is still lacking for soil and aquatic ciliates. However, several authors used PCA and canonical ordination methods (canonical correspondence analysis, CCA, and RDA) to compare ciliate species data matrices from different habitats and to determine the main environmental factors responsible for the variation in ciliate community structure (Galbraith and Burns 2010; Kammerlander et al. 2016; Li et al. 2016). Although these studies confirmed the general notion that ciliate dynamics is driven antagonistically by bottom-up forces (food supply) and top-down forces (predation), they showed that the relative strength of these drivers

340


Table 1. Resource use (trophic) guilds of free-living ciliates. Phagotrophs are shown with grey background, mixotrophs with light green and photoautotrophs with dark green background (For interpretation of the references to colour in this table legend, the reader is referred to the web version of this article).

References: (1) Kidder et al. (1940); (2) Lilly (1942); (3) Beaver and Crisman (1989); (4) Foissner et al. (1999); (5) Jürgens and Massana (2008); (6) Lynn (2008); (7) Pérez-Uz (1996); (8) Sanders et al. (1989); (9) Sherr et al. (1989); (10) Sherr and Sherr (1987); (11) Fenchel (1980); (12) Montagnes and Lessard (1999); (13) Müller and Schlegel (1999); (14) Müller et al. (1991); (15) Ohman and Snyder (1991); (16) Weisse (2003); (17) Fenchel (1987); (18) Miyake and Harumoto (1996); (19) Faust and Gulledge (1996); (20) Posch and Arndt (1996); (21) Scherwass et al. (2005); (22) Bourland and Strüder-Kypke (2010); (23) Strüder-Kypke et al. (2001); (24) Weisse and Sonntag (2016); (25) Matthes et al. (1988); (26) Esteban et al. (2010); (27) Stoecker (1991); (28) Stoecker et al. (2009); (29) Garcia-Cuetos et al. (2012); (30) Lindholm (1985); (31) Stoecker et al. (1989).

may not only change with trophic state (Beaver and Crisman 1989; Jürgens et al. 1999; Weisse 2003) but may also vary within relatively homogenous environments such as shallow eutrophic lakes (Li et al. 2016) or alpine lakes (Kammerlander et al. 2016). Abundance, species diversity and, although not directly measured, FD of ciliates were higher in a turbid glacial lake than in an alpine clear water lake, and the abundance of the dominating ciliate taxa in the turbid lake was mainly controlled by the presence of predatory zooplankton; in the clear water lake, ultraviolet radiation was a major factor affecting the occurrence of ciliates (Kammerlander et al. 2016). The majority of studies on functional diversity of ciliates in aquatic ecosystems dealt with their trophic role. Facultative and obligate ectoparasitic and endoparasitic species can cause diseases of natural and cultivated marine and freshwater fishes and invertebrates, but obligate parasites are relatively rare among ciliates (summarized by Lynn 2008) and are, therefore, largely ignored in the following. Apart from species-specific studies on, e.g., symbionts (Görtz 1996), most empirical and experimental ecological work with ciliates investigated their functions in aquatic food webs (see Section Application to food web analyses).

Central to such analyses is the classification of a ciliate according to its feeding or resource-use type. The broad functional classification by Pratt and Cairns (Pratt and Cairns 1985) into six groups (photosynthetic autotrophs, bacterivores/detritivores, saprotrophs, algivores, non-selective omnivores and predators) has been refined in the meantime (Table 1). Mitra et al. (2016) in their recent review argued that the fundamental division between functional protist groups must be between non-phagotrophic phototrophs, non-photosynthetic phagotrophs, and (constitutive and nonconstitutive) mixotrophs, with some combination of the two primary modes of nutrition. This distinction classifies extant protist species from a functional ecological point of view and neglects the fact that all protists appear to be osmotrophic to some degree (Flynn and Hansen 2013; Flynn et al. 2013; Mitra et al. 2016). Osmotrophy seems to be a relict because phagotrophy developed from an ancestral osmotrophic or saprotrophic way of life (e.g., Raven 1997; Mitra et al. 2016). However, for some extant ciliate species osmotrophy is the primary form of nutrition, constituting a specialialized case of the heterotrophic guild. Note that Table 1 comprises only freeliving ciliates; therefore, astome ciliates (subclass Astomatia Schewiakoff, 1896), which are also heterotrophic osmotrophs


341

Table 2. Important functional traits of free-living ciliates. Category

Parameters

Meaning/comments

Trait category

Numerical response

Maximum specific growth rate Threshold food concentration A constant (where r = rmax /2) Maximum prey ingestion rate Half saturation constant Conversion efficiency Predator mortality rate Prey type and size range Direct and inducible

Life history/response Consumption/response Consumption/response Consumption/response Consumption/response Consumption/response Life history/response Topological/foraging Topological/vulnerability

Dormant stage in life cycle

Life history/response

Depending on intrinsic and on several ambient variables


Motility

rmax V’ k2 Imax k e d ω (selectivity) Φ (edibility); cell size, swimming behaviour (‘jumps’), toxicysts (extrusomes), cell projections En-/excystments, frequency of cysts in population Length, width, diameter, circularity (deviation from sphere) Swimming speed and constancy


Conjugation Sensitivity to abiotic parameters

Timing, frequency, duration Temperature, salinity, pH, . . .

Depending on intrinsic and on several ambient factors Sexual recombination

Functional response Predator–prey dynamics (Independent Response Model) Prey size/resource use spectrum Defense mechanisms

Cyst formation Cell size and shape

but live as endosymbionts in the digestive tracts of annelids and other invertebrates (Lynn 2008), are not listed. I have classified free-living ciliates in Table 1 primarily based upon their heterotrophic, respectively symbiotic way of life. Phagotrophs comprise both heterotrophs that do not need a partner and commensals and parasites that both live in a symbiontic relationship. Except for two truly photautotrophic Mesodinium species (Garcia-Cuetos et al. 2012), symbiotic ciliates are also phagotrophs. On the one hand, the general classification shown in Table 1 is, in many cases, too strict; e.g., many, if not most, ‘herbivorous’ ciliates are omnivores, ingesting more than one category (functional guild) of prey. On the other hand, treating omnivores as a single (sub)category may be misleading. Omnivory is an important FT of many planktonic ciliates because it comprises versatile species that are able to switch between various food resources; however, in most cases, omnivorous ciliates will ingest either their preferred prey (if they are selective feeders) or the most abundant edible prey at a given time. Primarily in temporarily (seasonally) or spatially fluctuating environments (e.g., estuaries), ciliates may use their ability to ingest different food items. Therefore, for FD analysis of the ciliate assemblage at a specific site and time, omnivory, similar to herbivory and carnivory, should not be treated as a single ecological category but split into more specific subcategories (e.g., bacterivores, flagellate feeders, centric diatom feeders) with different coefficients of selectivity (ω; details presented in Tables 2 and 3 and discussed in the next section). Treating ciliate omnivory as a single category may be useful when comparing FD of two contrasting sites over a longer time (e.g., annual average). This is because we can expect a

Life history/response (?) Life history/response

ciliate community comprising many omnivorous species to be more resilient to temporarily adverse environmental conditions (low food supply) than another community that is composed mainly of feeding specialists. Mixotrophic species belong, at the same time or seasonally alternating, to several trophic levels. Mixotrophy is a mutualistic form of symbiosis, but this represents only one end of a dynamic continuum of biotic interactions ranging from positive (mutualistic) to (nearly) neutral (commensalistic) and antagonistic (parasitism, predation) effects on the host (Dziallas et al. 2012). Endosymbiosis in planktonic ciliates typically include a green algal partner of Chlorella-like algae (which are polyphyletic, comprising >10 species belonging to several genera; Pröschold et al. 2011) in freshwater (e.g., Askenasia chlorelligera, Coleps spetai and C. hirtus viridis, Euplotes spp., Frontonia spp., Paramecium busaria, Stentor spp.) and mainly dinoflagellates (genus Symbiodinium) in the sea (Esteban et al. 2010; Pröschold et al. 2011; Reisser 1986; Mitra et al. 2016 and references therein). Based upon their literature review, Mitra et al. (2016) estimated that about one third of the ciliate cells inhabiting the marine photic zone uses chloroplasts (‘kleptoplastids’) derived from several to many prey types for their photosynthetic activity. Note that, in a strict sense, chloroplast retention does not represent a symbiosis (“living together”), since the algae providing the chloroplasts are dead. Dziallas et al. (2012) pointed out that, in many cases, the effects of symbiosis on ciliate hosts and their endosymbionts are unclear.

342


Table 3. Presumed morphological and physiological parameter correlations with cell size of free-living ciliates. The shaded area refers to parameters derived from the Independent Response Model (reviewed by Weisse et al. 2016) or a Full Trait Distribution Model (Coutinho et al. 2016).

Positive

Neutral

Imax

Negative

Remarks

rmax (initial slope of FR)

V’ (threshold food concentration) ???

d (mortality rate) e (conversion efficiency)

k

Cell size should be positively related to the trophic state (i.e., availability of edible prey) of the habitat

k2 (prey selectivity)

(edibility)

Stress resistance

Determines sensitivity to fluctuations of abiotic variables

Swimming speed

In absolute terms Conjugation Cyst formation Colony formation

Numerical and functional responses of aquatic ciliates Prey ingestion and digestion by ciliates represent continuous consumption traits that can be measured by functional response curves (reviewed by Weisse et al. 2016). Similarly, the growth response of a predator to prey availability can be characterized by the numerical response. Using nonlinear curve fitting, analogous to Michaelis–Menten enzyme kinetics and to the relationship between photosynthesis and irradiance (P–I curve) of algae, functional and numerical response curves can be parameterized, yielding useful variables such as threshold prey concentrations V (i.e., the minimum prey level needed to sustain a predator population), and maximum ingestion (Imax ) and growth rates (rmax ) of the predator (Fig. 4). There are two other types of functional responses (Holling 1959), but the type shown in Fig. 4B is by far the most common for ciliates and most other predators. Numerical and functional response data will yield at least five FTs for ciliates (Table 2). While most of these are consumption (and foraging) traits, maximum specific growth rate falls into the category of life history traits. If both functional and numerical responses are measured independently from each other for a given ciliate species, the Independent Response Model (Fenton et al. 2010; Weisse et al. 2016) can be applied to calculate further variables such as conversion efficiency and per capita predator mortality (Li and Montagnes 2015). As discussed for continuous FTs in the general section above, all these variables can be standardized. The Independent Response Model is based upon the classical Rosenzweig–MacArthur predator–prey model

Mainly restricted to peritrichs

(Rosenzweig and MacArthur 1963). The ‘full trait distribution’ (FTD) model used by Coutinho et al. (2016) is another modification of the Rosenzweig–MacArthur model, simulating a continuous set of predator and prey species differing in their selectivity for prey uptake, ω, and edibility (susceptibility to predators), . The grazing rate g of a predator on a prey is a function of these two parameters. Up to now, ω and have been estimated only for a few ciliate species from results obtained in the laboratory under specific conditions (Müller and Schlegel 1999; Skogstad et al. 1987; Strom and Morello 1998; Verity 1991; Verity and Villareal 1986; Weisse and Frahm 2002; Wickham 1995; Wickham and Gilbert 1991; Wickham and Gugenberger 2008). However, the maximum specific (or intrinsic) growth rate, denoted rmax , μmax or r in the literature, is usually positively related to their vulnerability to grazing, i.e. rmax becomes higher the more edible a prey species is (Coutinho et al. 2016). Numerical and functional response data are available for ∼30 marine and ∼10 freshwater ciliate species. Marine ciliates of the subclass Choreotrichia Small and Lynn 1985 have been investigated in the most detail. In his recent review, Montagnes (2013) listed numerical and functional response data of 8–10 loricate choreotrichs of the order Tintinnida Kofoid and Campbell, 1929 from laboratory experiments conducted at 15–21 ◦ C; the uncertain species number reflects the fact that not all tintinnids studied in laboratory experiments have been identified to the species level. Maximum specific growth rates (rmax ) of tintinnids range from 0.36 d−1 (Amphorides quadrilineata; Jakobsen et al. 2001) to 1.8 d−1 (Tintinnopsis acuminata; Verity 1985). The threshold prey concentration (V ) ranges from 200 ng C d−1 ), and a similar wide range may occur within one species, depending on the nutritional quality of the prey offered in the experiments; Imax of Strombidinopsis jeokjo ranged from 2–350 ng C d−1 at comparable experimental temperature (Jeong et al. 1999; Jeong et al. 2004; Jeong et al. 2007; Jeong et al. 2011). Experimental investigations on another 8–10 marine ciliate species of the subclass Oligotrichia Bütschli, 1887/1889 focused on the genera Strombidium (Gismervik 2005; Montagnes 1996; Rose et al. 2013; Yang et al. 2015) and Parallelostrombidium (Montagnes 1996); the species Strombidium siculum investigated by Montagnes (1996) was later transferred to the new genus Parallelostrombidium (Agatha 2004). The parameters of the functional and numerical responses of the marine oligotrichs are similar to those reported above for the aloricate choreotrichs, with two notable exceptions. Not surprisingly, Rose et al. (2013) measured low rmax (0.21 d−1 ) for an Antarctic Strombidium sp. (at 0 ◦ C). The low rmax (0.33 d−1 ) obtained for Strombidium cf. sulcatum at 15 ◦ C (Yang et al. 2015) is probably not representative of this species because the ciliates were starved before the beginning of the experiments. Acclimatization to and standardization of the experimental conditions is important for obtaining unbiased results in numerical and functional response experiments (Montagnes 2013; Weisse et al. 2016). Studies on two prostomatid species (Balanion comatum and Tiarina fusus, Jakobsen and Hansen 1997; Jeong et al. 2002), one scuticociliate and one hypotrich (Parauronema sp. and Euplotes sp., Rose et al. 2013), and one heterotrich ciliate species (Condylostoma spatiosum, Li et al. 2011) compliment the investigations with marine ciliates. Maximum ingestion rates of T. fusus were lower than those for larger ciliates on the same potentially toxic dinoflagellate and raphidophyte prey (reviewed by Jeong et al. 2002); V (34–160 ␮g C L−1 ) of T. fusus was relatively high, and rmax (0.13–0.47 d−1 ) relatively low. The respective parameters of the other marine prostomatid investigated (Balanion comatum, Jakobsen and Hansen 1997) were similar to those of choreotrich and oligotrich marine ciliates and to those of the closely related freshwater species, B. planctonicum (Weisse et al. 2001). Therefore, more data with more palatable prey are needed for T. fusus.

344


Numerical and functional responses of freshwater ciliates were studied with 5–6 prostomatid species of the genera Urotricha (three species), Balanion and Coleps (probably two species) (Klaveness 1984; Madoni et al. 1990; Müller and Schlegel 1999; Stabell et al. 2002; Weisse et al. 2001). Further experiments were performed with the oligotrichs Meseres corlissi (Weisse 2004) and Halteria grandinella ˇ 2000), the choreotrich Rimostrom(Jürgens and Simek bidium lacustris (syn. Strobilidium lacustris; Müller and Schlegel 1999), the scuticociliate Histiobalantium bodamicum (Müller and Schlegel 1999), the colpodid Bromeliothrix metopoides (Weisse et al. 2013a,b), and the tetrahymenid Glaucomides bromelicola (Weisse et al. 2013a). Most of the freshwater data are comparable to the marine studies discussed above. However, some of the numerical and functional responses are indicative of a specific life style of the ciliates; for instance, from their analyses of in situ and in vitro growth rates of H. bodamicum (rmax in situ 0.40 d−1 , rmax in the laboratory 0.33 d−1 ), Müller and Weisse (1994) concluded that this scuticociliate is a ‘K-selected’ species and a superior competitor at relatively low algal food concentrations in lakes. At the other extreme along the spectrum of r/K selection is the cyst-forming colpodid ciliate Bromeliothrix metopoides from tank bromeliads (Weisse et al. 2013b); its extremely high food threshold (>1.4 mg C L−1 ) and rmax (4.71 d−1 ) characterize this species as a boom and bust ciliate that is specifically adapted to its peculiar habitat. The other species investigated from tank bromeliads, G. bromelicola, that cannot form resting cysts (Foissner 2013), reached similar high growth rates (rmax = 3.55 d−1 ), but had lower V (50–100 ␮g C L−1 ). Competition experiments with these two species revealed that stable coexistence of both species is possible in their natural habitat (Weisse et al. 2013a). The ciliates from tank bromeliads are not comparable to typical planktonic species. Except for the study on C. spatiosum (Li et al. 2011), all other experiments discussed in the foregoing were conducted with truly planktonic or meroplanktonic ciliates. More data are available for some aspects of the numerical and functional responses of aquatic ciliates, and in some cases, the experimental evidence was rapidly growing. For example, Taylor (1978) reported rmax and cell volumes for 11 bactivorous freshwater species, and Taylor and Shuter (1981), reviewing the then–existing literature, listed similar data for 35 ciliate species, including several non–bactivorous species from marine habitats. Wickham and Lynn (1990) provided more rmax data for seven species of colpodean ciliates. Müller and Geller’s meta-analysis (Müller and Geller 1993) already comprised rmax data for 102 ciliate strains/species; more than half of them (55) were bacterivorous species. The experimental results available suggest that there is no such thing as a species–specific numerical response curve. Firstly, the shape of the curve depends on the prey type, a topological trait (e.g., Lee et al. 2014; Weisse et al. 2013a). Secondly, covariation of consumption with response traits, i.e. with ambient variables such as temperature (Kimmance

et al. 2006; Li et al. 2011; Montagnes and Weisse 2000; Weisse and Montagnes 1998; Weisse et al. 2001; Weisse et al. 2002) and pH (Weisse et al. 2007, 2013a) also affects the experimental results. In particular, the interaction of temperature and food deserves more attention in the course of global warming (Montagnes et al. 2008a). Similarly, most of the experiments were conducted at temperatures ranging from 15–21 ◦ C, i.e. more experiments are needed with polar and tropical species. Thirdly, pronounced intraspecific variation (phenotypic plasticity) of numerical and functional responses is known from ciliates (Weisse 2006; Weisse et al. 2001) and the phylogenetically closely related dinoflagellates (Calbet et al. 2011; Calbet et al. 2013; Yang et al. 2013), and this plasticity does not only result from clonal differences and local adaptation (Gächter and Weisse 2006; Weisse et al. 2007). The same clone may yield different results when feeding on the same prey under different nutritional histories (Calbet et al. 2013; Li et al. 2013; reviewed by Weisse et al. 2016). The numerical response of two isolates of Balanion planctonicum is shown in Fig. 4A, together with the numerical response of Urotricha farcta, another prostomatid freshwater ciliate. All three isolates were fed the same cryptophyte food (Cryptomonas sp. strain 26.80 from the algal culture collection in Göttingen, Germany) and studied at the same experimental temperature (15 ◦ C), illustrating that intraspecific differences may be as large as interspecific differences (Pérez-Uz 1995; Weisse 2002; Weisse and Rammer 2006). Accordingly, the parameters of the functional and numerical responses of ciliates (and other taxa) should be expressed by a range for each species (e.g., threshold food concentration ranging from 10–20 ␮g C L−1 for palatable prey at temperature ranging from 15–20 ◦ C). This is also because the parameter estimates derived from numerical and functional response curves depend on several assumptions such as conversion from prey cell numbers to units of biomass (cell carbon), and the curve fitting does not always yield significant results. For example, the functional response experiment with B. planctonicum conducted in parallel to the numerical response experiment shown in Fig. 4A (the strain with the lowest rmax ) did not allow to estimate Imax because ingestion increased over a wide range of food levels, and the equation shown in Fig. 4B did not adequately predict an asymptote, i.e. Imax (Weisse et al. 2001). In spite of these caveats, some general conclusions can be drawn from the literature reported above; e.g., the low threshold concentration that is typical for tintinnids may imply a selective advantage for these loricate choreotrichs at low prey levels commonly encountered in the ocean (Montagnes 2013). The marine data set comprising ∼10 loricate and aloricate choreotrichs each, and a similar number of oligotrich species, may be sufficient to allow a meta-analysis to reveal if there are any significant differences among the major taxa. It is obvious that the marine data set is more representative for the ciliate community than the freshwater data set, where oligotrichs are clearly underrepresented, relative to their species richness and abundance; Foissner et al. (1991) classified the


freshwater ciliate plankton community as ‘Oligotrichetea’ (see also Foissner et al. 1999).

Application to food web analyses The kind of trophic classification summarized in Table 1 guided the analysis of pelagic carbon flow and nutrient cycling (Caron 1991; Caron and Goldman 1988; Gaedke and Straile 1994a; Legendre and Rivkin 2015; Weisse et al. 1990), demonstrated the central position of ciliates at the interface of the microbial food web and the classical grazer food chain (Adrian et al. 2001; Auer et al. 2004; Azam et al. 1983; Burns and Gilbert 1993; Calbet and Saiz 2005; Gifford 1991; Jack and Gilbert 1997; Karus et al. 2014; Porter et al. 1979; Sherr and Sherr 1994; Sherr and Sherr 2002; Weisse 2003, 2006; Weisse et al. 1990; Wickham 1995; Wickham and Gilbert 1991), and was used recently to illustrate cascading effects in planktonic food webs, emphasizing the significance of ciliates in lakes (Lischke et al. 2016) and in the ocean (Wollrab and Diehl 2015). To account for the trophic versatility typical of many ciliate species requires comprehensive food web analyses, measuring most of the trophic pathways with different methods at the same time (e.g. Brussaard et al. 1995; Weisse et al. 1990). Due to logistic and financial constraints, this is not practical in many cases, and the trophic roles of ciliates are often derived from assumptions based upon the literature. This is a risky approach, because, overall, there is a shortage of experimental studies, and generalizations or upscaling of empirical findings are based upon results obtained under specific laboratory conditions (the ‘upscaling problem’ was recently reviewed by Weisse et al. 2016). Moreover, intraspecific ecophysiological (trait) variation is commonly ignored in such broad analyses. However, although interspecific trait variation usually exceeds intraspecific trait variation the latter received increasing attention in multispecies predator–prey and competition models (Bolnick et al. 2011; Chesson 2000a,b; Klauschies et al. 2016). To account for intra– and interspecific trait variation, Coutinho et al. (2016) modified previous multispecies predator–prey models (Bauer et al. 2014; Tirok and Gaedke 2010) to their FTD model introduced in the foregoing section; the FTD model ignores details about species identities. Such full trait distribution models are extremely useful to investigate the response of communities to selection (Coutinho et al. 2016) but full parameterization of ω and (Table 2) is difficult if not impossible for complex communities. In their analysis of long-term ciliate biomass data from Lake Constance, Germany, Gaedke and Wickham (2004) used cluster analysis to identify nine groups of temporarily co-occurring ciliate morphotypes. These clusters exhibited a larger seasonality than found in the size distribution, suggesting that the cluster analysis captured some functional characteristics that were not (only) mediated by cell size. Follow-up studies using the same data set and additionally including phytoplankton investigated the

345

effects of irradiance, temperature, and vertical mixing on phytoplankton–ciliate dynamics (Tirok and Gaedke 2006; Tirok and Gaedke 2007a; Tirok and Gaedke 2007b) and demonstrated complex interactions and trade–offs in topological traits of prey and predators (i.e., prey edibility and predator food-selectivity) in the planktonic food web (Tirok et al. 2011; Tirok and Gaedke 2010). The FTD model (Countinho et al. 2016) also assumed parameter values originating from the long-term data set obtained in Lake Constance.

Natural mortality and sensitivity to predation Natural mortality in the absence of predation can be inferred from but not be equated with the threshold food concentration V listed in Table 2. This is because the shape of the numerical response at insufficient food levels below the threshold concentration differs between different species (Weisse 2006), i.e. it is an intrinsic (life history) trait of a species. In other words, different ciliate species display different sensitivities to starvation (Fenchel 1989; Hewett 1987; Jackson and Berger 1984, 1985; Jakobsen & Hansen 1997; Montagnes 1996). Most actively swimming, planktonic ciliates do not seem to reduce their metabolism significantly upon the onset of starvation and are able to survive only for 2–4 times their minimum generation time (Jakobsen & Hansen 1997; Tarangkoon and Hansen 2011; Weisse et al. 2001), corresponding to hours to days (Montagnes 1996). Exceptions are the phototrophic ciliate Mesodinium rubrum (syn. Myrionecta rubra), the closely related species M. pulex without phototrophic endosymbionts, and the freshwater prostomatid U. farcta that can all tolerate starvation for several weeks (Tarangkoon and Hansen 2011; Weisse et al. 2001). Similar long resistance to starvation is known from (benthic or meroplanktonic) ciliates such as the scuticociliate Uronema marinum and hypotrichs of the genus Euplotes that live close to the sediment water interface (Fenchel 1990; Jackson and Berger 1984, 1985; Turley et al. 1986). Remarkably, both M. pulex and U. farcta were isolated from littoral habitats (Tarangkoon and Hansen 2011; Weisse et al. 2001), are not restricted to a planktonic way of life and are frequently found in mud (Foissner et al. 1999). The empirical evidence thus supports Jackson’s and Berger’s conclusion (Jackson and Berger 1984, 1985) that a species’ survival time is related to its behaviour; some species living in shallow water bodies and sedentary ciliates such as many heterotrichs seem to have lower weight-specific respiratory rates and/or may be better able than actively swimming ciliates to reduce their metabolism upon starvation. Fenchel (1987) pointed out that protists are confronted with two opposing fitness requirements when they are exposed to starvation. One goal is staying alive, which is facilitated by reducing cellular metabolism. However, this implies that, as soon as food becomes available, the lag time, before the active metabolic state enabling cell division is resumed, increases.

346


The initial gain in fitness at the onset of starvation may thus be turned into a disadvantage when food shortage is over. Fenchel (1987) concluded that, if the periods of starvation in fluctuating environments are likely to be short, maintaining a relatively high physiological activity should be favoured. Closely related to the resistance to starvation is cyst formation (see above, Section Measuring and weighting functional traits), another life history trait representing an evolutionarily successful strategy to cope with adverse conditions including shortage of food (reviewed by Corliss and Esser 1974; Gutiérrez et al. 2001; Verni and Rosati 2011). It seems that most of the species that can survive starvation for longer periods do not form resting cysts, but more experimental evidence is needed to support this conclusion. Topological FTs (i.e., pairwise predator–prey interactions) for ciliates are the prey spectrum of a predator and the resource use of mixotrophic species (foraging traits), and the sensitivity to predation for virtually all ciliate species (vulnerability traits). Direct defence mechanisms in ciliates include possession of numerous toxicysts (i.e., extrusomes filled with a toxic agent) that can be immediately ejected and paralyze or kill an aggressor or prey (e.g., Buonanno et al. 2014; Mazanec and Trevarrow 1998; Rosati and Modeo 2003). Other efficient lethal toxins are found in coloured and colourless cortical granules of heterotrich ciliates such as Blepharisma japonicum (Miyake et al. 2003). Indirect defence mechanisms include the jumping behaviour of oligotrich and prostomatid ciliates (for review, see Weisse and Sonntag 2016) and inducible defence mechanisms (Kuhlmann et al. 1999) such as the prominent lateral and dorsal projections (‘wings’) of Euplotes octocarinatus produced in the presence of the predatory ciliate Lembadion bullinum (Kuhlmann and Heckmann 1985, 1994). It is a challenge for future FD research with ciliates to consider and weight this behavioural versatility in FT analyses.

Cell size—a key trait of functional diversity I include cell size in life history traits (Table 2), although cell size depends both on consumption (the more and nutritionally better the food, the larger the cell) and predation; the feeding spectrum of predators is strongly dependent on cell size (Fenchel 1987; Hansen et al. 1994; Hewett 1980; Roberts et al. 2011). In photoautotrophic and mixoautotrophic protists, body size and shape are important for light harvesting and nutrient uptake (Grover 1989; Key et al. 2010; Reynolds 1984). Cell shape (body form) may also affect the susceptibility to predation and, in benthic and interstitial species, constrain the motility of ciliates. Accordingly, cell size and, to a lesser degree, shape are both response and effect traits, suggesting that cell size is a ‘master trait’. Major morphological and physiological features of a ciliate cell that are correlated with its size are listed in Table 3. The rationale appears obvious for some of these variables; for instance, larger ciliates can swim faster (in absolute terms), filter more

water, and can form more food vacuoles in a given period of time than smaller species, resulting in higher Imax of the former (Hansen et al. 1997). Similarly, edibility () of ciliates declines with their cell size (e.g., Fenchel 1987; Hewett 1980; Roberts et al. 2011) because fewer planktonic predators can ingest larger than smaller ciliate species. By implication, larger ciliates should be able to exploit a more diverse prey spectrum, resulting in a positive relation between cell size and ω. It seems also plausible to assume that larger species can sustain periods of starvation and other unfavourable environmental conditions better than smaller species, leading to an inverse relationship of cell size and mortality rate (d). This is because (i) the ability to store reserves increases with cell size and (ii) the specific metabolism decreases with cell size. Jackson and Berger (1984) interpreted long survival rates under starvation of the large ciliate Stentor coeruleus as an effect of its large quantity of reserves and low respiratory rate. However, the same authors reported that survivorship of another heterotrich species, Condylostoma vorticella, was similar to that of Euplotes patella despite the fact that the former species was at least four times the size of the latter. Therefore, different levels of cellular activity (mainly sedentary vs actively swimming) may offset the general relationship between cell size and mortality rate (see Section Natural mortality and sensitivity to predation, above). Surface area (A) of all organisms scales with their body mass (or cell volume), M, as A = M 2/3 ; similarly, the basal metabolic rate, R, of organisms is related to their mass, M, via the power function: R = a × Mb

(2)

where a is a constant and the exponent b is, both in animals and protists, not significantly different from 0.75 (Fenchel 1987; Hemmingsen 1960; Kleiber 1947). Scaling of surface area with body mass has found strong empirical support in organismic biology. However, the finding that weight specific metabolic rate decreases somewhat more slowly than suggested by Rubner’s “surface law” (Rubner 1883) and the exact nature of Kleiber’s mass exponent of 0.75 (Kleiber 1932, 1947) have been controversially debated for decades (e.g., Alcaraz 2016; Dodds et al. 2001; Glazier 2005; Heusner 1982; Schmidt-Nielsen 1984). From Eq. (2) it follows that specific metabolic rate, R/M, declines with body size or cell volume with an exponent of −0.25, if b = 0.75: a × M 0.75 R = M M

= a × M −0.25

(3)

Note that the constant a is not universal constant but it is several times higher in metazoa than in protozoa (Hemmingsen 1960; reviewed by Fenchel 1987). With respect to ciliates, Eq. (3) has important implications for at least three more variables reported in Table 3. First, specific growth rate is closely related to metabolism, and rmax also decreases with the ∼0.25 power of cell volume


(Fenchel 1987; Fenchel and Findlay 1983; Hansen et al. 1997). Secondly, maximum clearance rate (i.e., the rate at which organisms process water), which is equivalent to α, the initial slope of the functional response (Fenchel 1987; Weisse et al. 2016), also declines with the same exponent with cell size (Fenchel 1986; Hansen et al. 1997). Analogous to the Michaelis–Menten equation for enzyme kinetics, α is an indication of the affinity between prey and predator, yielding a direct measure of an organism’s competitive ability for scarce resources (Fenchel 1987; Weisse et al. 2016). In other words, larger ciliates (and other protists) need more food to sustain their populations than smaller species. If Imax scale positively and α negatively with cell size, the half saturation constant k of the functional response should increase with cell size. It is intuitive to assume that the threshold food concentration, V’, of the numerical response also increases with cell size, but the empirical data available for ciliates suggest otherwise (Weisse 2006; Weisse et al. 2013b). A possible explanation is the fact that ciliates and some other protists developed several adaptive responses to survive food deplete conditions (Weisse et al. 2016) that may mask the presumed positive relationship of V’ to cell size. More data are needed to clarify this issue. Conversion efficiency (e) seems to be invariant with cell size in ciliates, other protists and metazooplankton, with an average of ∼0.33 (reviewed by Hansen et al. 1997; Straile 1997; Weisse et al. 2016). Similarly, there are no theoretical foundations to assume that the constant k2 of the numerical response curves scale with body size. In conclusion, mainly because Imax , ω, and possibly V’ scale positively and α negatively with cell size (Table 3), average ciliate cell size should be positively related to the trophic state of the habitat. This is also because protist cell size varies with the nutritional status of the organisms, depending on resource (food) quantity and quality (Calbet et al. 2013; Fenchel 1989; Jackson and Berger 1985; Kimmance et al. 2006; Weisse et al. 2002). To a lesser extent, cell size is affected by abiotic factors such as temperature and pH; for a ciliate species, these variables may change its cell size (biovolume) by a factor of 2–4 (Weisse and Montagnes 1998; Weisse and Stadler 2006; Weisse et al. 2002). Because it integrates response and effect traits, the body size spectrum is an important ecosystem property of (planktonic) ecosystems (Gaedke 1992; Gaedke and Straile 1994b; Sheldon et al. 1972; Steele 1974).

Emergent questions New insights from molecular data The potential number of parameters and organisms interacting in each (aquatic) environment is extremely high. This is especially true for ciliates, which, together with dinoflagellates, are genetically, and functionally the most diverse component of eukaryotic organisms in many ecosystems.

347

The application of molecular techniques for the detection and identification of aquatic protists has fundamentally changed our perception of ciliate diversity and, thus, our understanding of their functional ecology over the past two decades (Caron et al. 2017; de Vargas et al. 2015; Epstein and Lopez-Garcia 2008; Keeling et al. 2014; Massana et al. 2009; Zubkov and Tarran 2008). Analyses from 18S ribosomal DNA sequences and metagenomics data from the Tara Oceans expedition reported an enormous amount of as yet unknown taxonomic and, presumably, functional protist diversity (de Vargas et al. 2015; Gimmler et al. 2016; Sunagawa et al. 2015). The vast majority of this diversity represents as yet uncultured taxa, and many or even the majority of the genes sequenced show no similarity to sequences currently available in public databases (Caron et al. 2017; Gimmler et al. 2016), i.e. the genetic makeup cannot be linked to form. This is a major problem because protist species demarcation also requires morphological description of the new taxa (Warren et al. 2017). The Marine Microbial Eukaryotic Transcriptome Sequencing Project (Keeling et al. 2014) that focuses on cultured species is an attempt to overcome this obstacle, providing a reference data set for novel taxa. As more and more metatranscriptomes become available and the diversity of RNA transcripts and the pathways they denote become the standard for elucidating ecological functions, a detailed functional analysis of diversity without worrying about species definitions and their boundaries (Boenigk et al. 2012) may become possible in the near future. This is particularly true for FD indices that are independent from species richness. Mason et al. (2003) already postulated that a FD index should be unaffected by the number of species, because the number of taxonomic species (which is defined in arbitrary categories) itself is not relevant to functional diversity. If a taxonomic unit (a species or Operational Taxonomic Unit, OTU) is split into two with the same trait values and the same total abundance, FD measured in terms of FDiv and FR will be unaffected. However, the FEve index does not satisfy this criterion, since the regularity of trait values will be changed (Villéger et al. 2008) Molecular analyses of small protists in freshwater began slightly later than in the oceans but has revealed a similar large diversity of mostly uncultured species, many of which seem to be ciliates and some other alveolates (reviewed by Simon et al. 2015). Metabarcode analyses from the Tara Oceans expedition (de Vargas et al. 2015) clearly support the notion expressed above that endosymbiotic associations are important among oceanic ciliates (recently reviewed by Caron et al. 2017; Stoecker et al. 2017). However, the presence of molecules at the DNA, RNA or protein level cannot be equated with cellular abundance. In particular, the highly variable 18S rDNA copy number in many alveolates is a major reason for biased protist abundance data obtained from high-throughput sequencing (Dunthorn et al. 2014; Medinger et al. 2010; Stoeck et al. 2014). It is also clear that, if new associations and functions are conjectured, sequencing alone cannot reveal the details of

348


the microbial interactions. Therefore, more functional studies on ecologically relevant model ciliates under realistic environmental conditions are urgently needed (Weisse et al. 2016). All (aquatic) organisms are embedded in ecological interactions in which each species is influenced by multiple other species, by abiotic factors, and also by its own (nutritional) history. Statistical analyses of co-occurrence and sequence similarity networks (Faust and Raes 2012; Forster et al. 2012; Forster et al. 2015; Fuhrman 2009; Posch et al. 2015; Steele et al. 2011; Worden et al. 2015) are increasingly being used to deduce hypotheses about structure and function of marine microbes, including ciliates, from the enormous amount of information gained by high-throughput sequencing (HTS) and various ‘omics’ approaches (metatranscriptome and proteome analyses). Recent sequence similarity network analysis of ciliate data obtained from HTS demonstrated that the extensive novel diversity of environmental ciliates and the tremendous richness of their interactions differ in relation to geographic location and habitat (Forster et al. 2015). These authors also pointed out that our ability to analyze and interpret the data obtained from HTS lags far behind the rapidly increasing generation of new data. For a short overview on methods for the analysis of (microbial) networks, the reader is referred to Fuhrman et al. (2015). The question of how species traits influence network structure has been identified recently as one of the 100 fundamental ecological questions (Sutherland et al. 2013). In more general terms, one of the grand challenges in the life sciences is to link genes and their expression levels to ecosystem structure and stability (DeLong et al. 2006; Guidi et al. 2016; Wymore et al. 2011). First attempts to infer metabolism and ecological roles from transcriptome analyses have been published for the heterotrophic choreotrich Strombidinopsis sp. and the mixotrophic oligotrich Strombidium rassoulzadegani (Santoferrara et al. 2014), and this field will rapidly progress and may revolutionize the understanding of functional diversity and ecological roles of ciliates in near future (Santoferrara and McManus 2017). Based upon their analysis of terrestrial plant communities, Wymore et al. (2011) presented four postulates of community genetics for testing the hypothesis of a genetic effect on the community and ecosystem level:

(1) the demonstration of a target species’ impact on the community and ecosystem; (2) the demonstration of key traits that are genetically based and heritable; (3) the demonstration of quantifiable effects of genotypic variation (different genotypes) on the communities and ecosystem processes in which they live; and (4) the manipulation of target gene(s) or their expression to experimentally evaluate a community and ecosystem effect.

Conclusions and avenues for future ciliate research Review articles notoriously end with a plea for more research, and the present paper is no exception. I identify six key issues for future research on functional ecology of ciliates: (1) more experimental investigations with functionally different ciliates from contrasting environments are needed to provide a solid database for the functional traits listed in Tables 2 and 3. With the scarce and/or taxonomically biased empirical evidence that is currently available it is, for instance, impossible to infer if the functional diversity of marine and freshwater species is different. A comparison with trait-based community ecology of phytoplankton (Litchman and Klausmeier 2008) illustrates how much ciliate research lags behind; in their recent analysis of light utilization traits of marine phytoplankton, Edwards et al. (2015) compiled data from 308 growth-irradiance experiments performed on 119 species. Similarly, the data base is impressive concerning the interaction of light and temperature for phytoplankton growth (Edwards et al. 2016). Accordingly, growth-irradiance curves and nutrient uptake-irradiance curves of algae, which are the equivalent of numerical and functional response curves for heterotrophs, can be parameterized for functional groups of phytoplankton with more certainty than it is currently possible for the corresponding curves of ciliates. For freshwater ciliates, only a handful of studies investigated the interaction of prey uptake and growth rates (cited above). In Urotricha farcta, temperature affected the threshold food level (V ), the initial slope of the numerical response curve, and the maximum specific growth rate (rmax ), similar to temperature × food interactions known from Daphnia and rotifers (Weisse et al. 2002 and references therein). In consequence, considering temperature × food interaction substantially alters the outputs of resource-consumer population and food web models (Kimmance et al. 2006; Montagnes et al. 2008a). Regarding the significance of ciliates for global biogeochemical cycling, (2) more experimental studies with ecologically relevant (i.e., not only functionally different but also functionally important) species on the interaction of FTs with abiotic parameters are urgently needed in order to better parameterize models of carbon flow in the course of global warming. It is obvious that in both cases (1 and 2), the species under investigation need to be sequenced to improve the available genetic data set and the linkage between genes and their function. (3) Analysis of covariation between FTs received relatively little attention thus far. For instance, it has already been shown for ciliates that topological traits (e.g., prey type) interact with consumption traits (e.g., ingestion rates) and life history traits (cell size, fecundity, cyst formation, mortality) (e.g., Montagnes 2013; Weisse et al. 2013a), but a formal analysis of this covariation is still pending. In food webs, increasing functional diversity at one trophic level could provide niches for above layers, but it is not known if traits


responsible for trophic interactions are independent of the ones determining resource use (Gravel et al. 2016). From their analysis of functional trait composition of food webs, Gravel et al. (2016) identified several open questions for future research. One obvious challenge is (4) developing theory relating functional composition, network structure and ecosystem functioning. This also implies understanding the role of a species in a network from its FTs, a postulate that is particularly applicable to the functioning of mixotrophic ciliates in oceanic gyres. At present, we have only a vague idea about the role of those species in their respective networks and do not know how many of them may be functionally redundant at the ecosystem level. Since ciliates have short generation times and many species can be reared in the laboratory with relative ease, ecologists and modellers are encouraged to make more use of ciliates for tackling these issues of general relevance. (5) More emphasis should be placed on isolating species of the ‘rare ciliate biosphere’ (Dunthorn et al. 2014; Weisse 2014) that are prominent offshore in the ocean and, probably, in large oligotrophic lakes. However, we have to be aware that investigating ciliates under simplified laboratory conditions (e.g., with one prey and one predator species each in response to one or a few abiotic factors) reduces not only the complexity (=n-dimensionality) of the situation in the field, but may also lead to unwarranted loss of properties that only emerge in the multispecies or ecosystem system context. (6) Network and metagenome/transcriptome analyses will increasingly offer insights to decipher what many yet uncultured species do in their natural realm.

Acknowledgements This review is based upon a plenary talk presented at the 5th Workshop of the International Research Coordination Network for Biodiversity of Ciliates in Guam, supported by the National Science Foundation of the USA (grant to John Clamp) and the National Natural Science Foundation of China (grant to Weibo Song); I thank the funding agencies for travel support and Chris Lobban and his team for organizing the meeting. I also appreciate comments by Denis Lynn, Bettina Sonntag, and two anonymous reviewers on previous versions of the manuscript.

References Adrian, R., Wickham, A.A., Butler, N.M., 2001. Trophic interactions between zooplankton and the microbial community in contrasting food webs: the epilimnion and the deep chlorophyll maximum of a mesotrophic lake. Aquat. Microb. Ecol. 24, 83–97. Agatha, S., 2004. Evolution of ciliary patterns in the Oligotrichida (Ciliophora, Spirotricha) and its taxonomic implications. Zoology 107, 153–168.

349

Agatha, S., Laval-Peuto, M., Simon, P., 2013. The tintinnid lorica. In: Dolan, J.R., Montagnes, D.J.S., Agatha, S., Coats, D.W., Stoecker, D.K. (Eds.), The Biology and Ecology of Tintinnid Ciliates: Models for Marine Plankton. John Wiley & Sons, Ltd., Chichester, UK, pp. 17–41. Alcaraz, M., 2016. Marine zooplankton and the metabolic theory of ecology: is it a predictive tool? J. Plankton Res. 38, 762–770. Allison, S.D., Martiny, J.B., 2008. Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. U. S. A. 105, 11512–11519. Altermatt, F., Fronhofer, E.A., Garnier, A., Giometto, A., Hammes, F., Klecka, J., Legrand, D., Mächler, E., Massie, T.M., Pennekamp, F., 2015. Big answers from small worlds: a user’s guide for protist microcosms as a model system in ecology and evolution. Methods Ecol. Evol. 6, 218–231. Auer, B., Elzer, U., Arndt, H., 2004. Comparison of pelagic food webs in lakes along a trophic gradient and with seasonal aspects: influence of resource and predation. J. Plankton Res. 26, 697–709. Azam, F., Fenchel, T., Field, J., Gray, J., Meyer-Reil, L., Thingstad, F., 1983. The ecological role of water column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–263. Bauer, B., Vos, M., Klauschies, T., Gaedke, U., 2014. Diversity, functional similarity, and top-down control drive synchronization and the reliability of ecosystem function. Am. Nat. 183, 394–409. Beaver, J.R., Crisman, T.L., 1989. The role of ciliated protozoa in pelagic freshwater ecosystems. Microb. Ecol. 17, 111–136. Boenigk, J., Ereshefsky, M., Hoef-Emden, K., Mallet, J., Bass, D., 2012. Concepts in protistology: species definitions and boundaries. Eur. J. Protistol. 48, 96–102. Boersma, K.S., Dee, L.E., Miller, S.J., Bogan, M.T., Lytle, D.A., Gitelman, A.I., 2016. Linking multidimensional functional diversity to quantitative methods: a graphical hypothesisevaluation framework. Ecology 97, 583–593. Bolnick, D.I., Amarasekare, P., Araújo, M.S., Bürger, R., Levine, J.M., Novak, M., Rudolf, V.H.W., Schreiber, S.J., Urban, M.C., Vasseur, D., 2011. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192. Borcard, D., Gillet, F., Legendre, P., 2011. Numerical Ecology with R. Springer, New York. Botta-Dukát, Z., 2005. Rao’s quadratic entropy as a measure of functional diversity based on multiple traits. J. Veg. Sci. 16, 533–540. Bourland, W.A., Strüder-Kypke, M.C., 2010. Agolohymena aspidocauda nov. gen., nov. spec., a histophagous freshwater tetrahymenid ciliate in the family Deltopylidae (Ciliophora, Hymenostomatia), from Idaho (northwest USA): morphology, ontogenesis and molecular phylogeny. Eur. J. Protistol. 46, 221–242. Brocchieri, L., 2016. Functional and phylogenetic diversity. J. Phylogenet. Evol. Biol. 4, e122, http://dx.doi.org/10.4172/2329-9002.1000e122. Brussaard, C., Riegman, R., Noordeloos, A., Cadée, G., Witte, H., Kop, A., Nieuwland, G., Van Duyl, F., Bak, R., 1995. Effects of grazing, sedimentation and phytoplankton cell lysis on the structure of a coastal pelagic food web. Mar. Ecol. Prog. Ser. 123, 259–271. Buonanno, F., Anesi, A., Guella, G., Kumar, S., Bharti, D., La Terza, A., Quassinti, L., Bramucci, M., Ortenzi, C., 2014. Chemical

350


offense by means of toxicysts in the freshwater ciliate, Coleps hirtus. J. Eukaryot. Microbiol. 61, 293–304. Burns, C.W., Gilbert, J.J., 1993. Predation on ciliates by freshwater calanid copepods: rates of predation and relative vulnerabilities of prey. Freshw. Biol. 30, 377–393. Calbet, A., Saiz, E., 2005. The ciliate-copepod link in marine ecosystems. Aquat. Microb. Ecol. 38, 157–167. Calbet, A., Bertos, M., Fuentes-Grünewald, C., Alacid, E., Figueroa, R., Renom, B., Garcés, E., 2011. Intraspecific variability in Karlodinium veneficum: growth rates, mixotrophy, and lipid composition. Harmful Algae 10, 654–667. Calbet, A., Isari, S., Martínez, R.A., Saiz, E., Garrido, S., Peters, J., Borrat, R.M., Alcaraz, M., 2013. Adaptations to feast and famine in different strains of the marine heterotrophic dinoflagellates Gyrodinium dominans and Oxyrrhis marina. Mar. Ecol. Prog. Ser. 483, 67–84. Calow, P., 1987. Towards a definition of functional ecology. Funct. Ecol. 1, 57–61. Caron, D.A., 1991. Evolving role of protozoa in aquatic nutrient cycles. In: Reid, P.C., Turley, C.M., Burkill, P.H. (Eds.), Protozoa and Their Role in Marine Processes. , pp. 387–415. Caron, D.A., Countway, P.D., 2009. Hypotheses on the role of the protistan rare biosphere in a changing world. Aquat. Microb. Ecol. 57, 227–238. Caron, D.A., Goldman, J.C., 1988. Dynamics of protistan carbon and nutrient cycling. J. Protozool. 35, 247–249. Caron, D.A., Countway, P.D., Jones, A.C., Kim, D.Y., Schnetzer, A., 2012. Marine protistan diversity. Ann. Rev. Mar. Sci. 4, 467–493. Caron, D.A., Alexander, H., Allen, A.E., Archibald, J.M., Armbrust, E.V., Bachy, C., Bell, C.J., Bharti, A., Dyhrman, S.T., Guida, S.M., Heidelberg, K.B., Kaye, J.Z., Metzner, J., Smith, S.R., Worden, A.Z., 2017. Probing the evolution, ecology and physiology of marine protists using transcriptomics. Nat. Rev. Microbiol. 15, 6–20. Casanoves, F., Di Rienzo, J.A., Pla, L., 2008. User Manual FDiversity: Statistical Software for the Analysis of Functional Diversity, First Edition, Argentina, www.fdiversity.nucleodiversus.org. Casanoves, F., Pla, L., Di Rienzo, J.A., Díaz, S., 2011. FDiversity: a software package for the integrated analysis of functional diversity. Methods Ecol. Evol. 2, 233–237. Chen, B., Liu, H., Lau, M.T., 2010. Grazing and growth responses of a marine oligotrichous ciliate fed with two nanoplankton: does food quality matter for micrograzers? Aquat. Ecol. 44, 113–119. Chen, Y., 2015. Biodiversity and Biogeographic Patterns in AsiaPacific Region I: Statistical Methods and Case Studies. Bentham Science Publishers Ltd., Sharjah, U.A.E. Chesson, P., 2000a. General theory of competitive coexistence in spatially-varying environments. Theor. Pop. Biol. 58, 211–237. Chesson, P., 2000b. Mechanisms of maintenance of species diversity. Ann. Rev. Ecol. Syst. 31, 343–366. Clamp, J.C., Lynn, D.H., 2017. Investigating the biodiversity of Ciliates in the ‘Age of Integration’. Eur. J. Protistol. 61, 314–322, http://dx.doi.org/10.1016/j.ejop.2017.01.004. Corliss, J.O., Esser, S.C., 1974. Comments on the role of the cyst in the life cycle and survival of free-living protozoa. Trans. Am. Microsc. Soc. 93, 578–593. Coûteaux, M.-M., Darbyshire, J.F., 1998. Functional diversity amongst soil protozoa. Appl. Soil Ecol. 10, 229–237.

Coutinho, R.M., Klauschies, T., Gaedke, U., 2016. Bimodal trait distributions with large variances question the reliability of traitbased aggregate models. Theor. Ecol., 1–20. Coux, C., Rader, R., Bartomeus, I., Tylianakis, J.M., 2016. Linking species functional roles to their network roles. Ecol. Lett. 19, 762–770, http://dx.doi.org/10.1111/ele.12612. de Vargas, C., Norris, R., Zaninetti, L., Gibb, S.W., Pawlowski, J., 1999. Molecular evidence of cryptic speciation in planktonic foraminifers and their relation to oceanic provinces. Proc. Nat. Acad. Sci. 96, 2864–2868. de Vargas, C., Audic, S., Henry, N., Decelle, J., Mahé, F., Logares, R., Lara, E., Berney, C., Le Bescot, N., Probert, I., Carmichael, M., Poulain, J., Romac, S., Colin, S., Aury, J.-M., Bittner, L., Chaffron, S., Dunthorn, M., Engelen, S., Flegontova, O., Guidi, L., Horák, A., Jaillon, O., Lima-Mendez, G., Lukeˇs, J., Malviya, S., Morard, R., Mulot, M., Scalco, E., Siano, R., Vincent, F., Zingone, A., Dimier, C., Picheral, M., Searson, S., KandelsLewis, S., Coordinators, T.O., Acinas, S.G., Bork, P., Bowler, C., Gorsky, G., Grimsley, N., Hingamp, P., Iudicone, D., Not, F., Ogata, H., Pesant, S., Raes, J., Sieracki, M.E., Speich, S., Stemmann, L., Sunagawa, S., Weissenbach, J., Wincker, P., Karsenti, E., 2015. Eukaryotic plankton diversity in the sunlit ocean. Science 348. Delgado-Baquerizo, M., Giaramida, L., Reich, P.B., Khachane, A.N., Hamonts, K., Edwards, C., Lawton, L.A., Singh, B.K., 2016. Lack of functional redundancy in the relationship between microbial diversity and ecosystem functioning. J. Ecol. 104, 936–946. DeLong, E.F., Preston, C.M., Mincer, T., Rich, V., Hallam, S.J., Frigaard, N.-U., Martinez, A., Sullivan, M.B., Edwards, R., Brito, B.R., 2006. Community genomics among stratified microbial assemblages in the ocean’s interior. Science 311, 496–503. Dıáz, S., Cabido, M., 2001. Vive la difference: plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655. Dodds, P.S., Rothman, D.H., Weitz, J.S., 2001. Re-examination of the 3/4-law of metabolism. J. Theor. Biol. 209, 9–27. Dolan, J.R., 2013. Introduction to tintinnids. In: Dolan, J.R., Montagnes, D.J.S., Agatha, S., Coats, D.W., Stoecker, D.K. (Eds.), The Biology and Ecology of Tintinnid Ciliates: Models for Marine Plankton. John Wiley & Sons Ltd., Chichester, UK, pp. 1–16. Dolan, J.R., Landry, M.R., Ritchie, M.E., 2013. The species-rich assemblages of tintinnids (marine planktonic protists) are structured by mouth size. ISME J. 7, 1237–1243. Dolan, J.T., Jacquet, S., Torréton, J.-P., 2006. Comparing taxonomic and morphological biodiversity of tintinnids (planktonic ciliates) of New Caledonia. Limnol. Oceanogr. 51, 950–958. Dolan, J.R., Ritchie, M.E., Tunin-Ley, A., Pizay, M.-D., 2009. Dynamics of core and occasional species in the marine plankton: tintinnid ciliates in the north-west Mediterranean Sea. J. Biogeogr. 36, 887–895. Dunthorn, M., Katz, L.A., 2010. Secretive ciliates and putative asexuality in microbial eukaryotes. Trends Microbiol. 18, 183–188. Dunthorn, M., Stoeck, T., Clamp, J., Warren, A., Mahé, F., 2014. Ciliates and the rare biosphere: a review. J. Eukaryot. Microb. 61, 404–409. Dziallas, C., Allgaier, M., Monaghan, M.T., Grossart, H.-P., 2012. Act together-implications of symbioses in aquatic ciliates. Front. Microbiol. 3, 1–17.


Edwards, K.F., Thomas, M.K., Klausmeier, C.A., Litchman, E., 2015. Light and growth in marine phytoplankton: allometric taxonomic, and environmental variation. Limnol. Oceanogr. 60, 540–552. Edwards, K.F., Thomas, M.K., Klausmeier, C.A., Litchman, E., 2016. Phytoplankton growth and the interaction of light and temperature: a synthesis at the species and community level. Limnol. Oceanogr. 61, 1232–1244. Elton, C.S., 1958. The Ecology of Invasions by Plants and Animals. Methuen, London, pp. 18. Epstein, S., Lopez-Garcia, P., 2008. Missing protists: a molecular prospective. Biodivers. Conserv. 17, 261–276. Esteban, G.F., Fenchel, T., Finlay, B.F., 2010. Mixotrophy in ciliates. Protist 161, 621–641. Faust, K., Raes, J., 2012. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550. Faust, M.A., Gulledge, R.A., 1996. Associations of microalgae and meiofauna in floating detritus at a mangrove island Twin Cays, Belize. J. Exp. Mar. Biol. Ecol. 197, 159–175. Fenchel, T., 1980. Suspension feeding in ciliated protozoa: functional response and particle size selection. Microb. Ecol. 6, 1–11. Fenchel, T., 1986. Protozoan filter feeding. Prog. Protistol. 1, 65–113. Fenchel, T., 1987. Ecology of Protozoa. The Biology of Free-Living Phagotrophic Protists. Science Tech./Springer, Madison, WI. Fenchel, T., 1989. Adaptations to a feast and famine existence in protozoa. In: Wieser, W., Gnaiger, E. (Eds.), Energy Transformations in Cells and Organisms. Georg Thieme, Stuttgart, Germany, pp. 290–295. Fenchel, T., 1990. Adaptive significance of polymorphic life cycles in Protozoa: responses to starvation and refeeding in two species of marine ciliates. J. Exp. Mar. Biol. Ecol. 136, 159–177. Fenchel, T., 2005. Cosmopolitan microbes and their ‘cryptic’ species. Aquat. Microb. Ecol. 41, 49–54. Fenchel, T., Findlay, B.J., 1983. Respiration rates in heterotrophic, free-living protozoa. Microb. Ecol. 9, 99–122. Fenton, A., Spencer, M., Montagnes, D.J.S., 2010. Parameterising variable assimilation efficiency in predator–prey models. Oikos 119, 1000–1010. Flynn, K.J., Mitra, A., 2009. Building the perfect beast: modelling mixotrophic plankton. J. Plankton Res. 31, 965–992. Flynn, K.J., Hansen, P.J., 2013. Cutting the canopy to defeat the selfish gene; conflicting selection pressures for the integration of phototrophy in mixotrophic protists. Protist 164, 811–823. Flynn, K.J., Stoecker, D.K., Mitra, A., Raven, J.A., Glibert, P.M., Hansen, P.J., Granéli, E., Burkholder, J.M., 2013. Misuse of the phytoplankton–zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types. J. Plankton Res. 35, 3–11. Foissner, W., 2013. Description of Glaucomides bromelicola n. gen. n. spec. (Ciliophora, Tetrahymenida), a macrostome forming inhabitant of bromeliads (Bromeliaceae), including redescriptions of Glaucoma scintillans and G. reniformis. J. Eukaryot. Microb. 60, 137–157. Foissner, W., Berger, H., Schaumburg, J., 1999. Identification and Ecology of Limnetic Plankton Ciliates. Informationsberichte Bayer. Landesamt für Wasserwirtschaft 3/99, Munich. Foissner, W., Blatterer, H., Berger, H., Kohmann, F., 1991. Taxonomische und ökologische Revision der Ciliaten des Saprobiensystems—Band I: Cyrtophorida, Oligotrichida,

351

Hypotrichia, Colpodea. Informationsberichte Bayer. Landesamt für Wasserwirtschaft 1/91, Munich. Forster, D., Behnke, A., Stoeck, T., 2012. Meta-analyses of environmental sequence data identify anoxia and salinity as parameters shaping ciliate communities. Syst. Biodivers. 10, 277–288. Forster, D., Bittner, L., Karkar, S., Dunthorn, M., Romac, S., Audic, S., Lopez, P., Stoeck, T., Bapteste, E., 2015. Testing ecological theories with sequence similarity networks: marine ciliates exhibit similar geographic dispersal patterns as multicellular organisms. BMC Biol. 13, 1–16. Fuhrman, J.A., 2009. Microbial community structure and its functional implications. Nature 459, 193–199. Fuhrman, J.A., Cram, J.A., Needham, D.M., 2015. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Micro. 13, 133–146. Fuhrman, J.A., Hewson, I., Schwalbach, M.S., Steele, J.A., Brown, M.V., Naeem, S., 2006. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc. Natl. Acad. Sci. U. S. A. 103, 13104–13109. Gächter, E., Weisse, T., 2006. Local adaptation among geographically distant clones of the cosmopolitan freshwater ciliate Meseres corlissi. I. Temperature response. Aquat. Microb. Ecol. 45, 291–300. Gaedke, U., 1992. The size distribution of plankton biomass in a large lake and its seasonal variability. Limnol. Oceanogr. 37, 1202–1220. Gaedke, U., Straile, D., 1994a. Seasonal changes of the quantitative importance of protozoans in a large lake: an ecosystem approach using mass-balanced carbon flow diagrams. Mar. Microb. Food Webs 8, 163–188. Gaedke, U., Straile, D., 1994b. Seasonal changes of trophic transfer efficiencies in a plankton food web derived from biomass size distributions and network analysis. Ecol. Model. 75/76, 435–445. Gaedke, U., Wickham, S.A., 2004. Ciliate dynamics in response to changing biotic and abiotic conditions in a large, deep lake (Lake Constance). Aquat. Microb. Ecol. 34, 247–261. Galbraith, L.M., Burns, C.W., 2010. Drivers of ciliate and phytoplankton community structure across a range of water bodies in southern New Zealand. J. Plankton Res. 32, 327–339. Garcia-Cuetos, L., Moestrup, Ø., Hansen, P.J., 2012. Studies on the genus Mesodinium II. Ultrastructural and molecular investigations of five marine species help clarifying the taxonomy. J. Eukaryot. Microbiol. 59, 374–400. Garnier, E., Cortez, J., Billès, G., Navas, M.-L., Roumet, C., Debussche, M., Laurent, G., Blanchard, A., Aubry, D., Bellmann, A., 2004. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637. Gifford, D.J., 1991. The protozoan-metazoan link in pelagic ecosystems. J. Protozool. 38, 81–86. Gimmler, A., Korn, R., De Vargas, C., Audic, S., Stoeck, T., 2016. The Tara Oceans voyage reveals global diversity and distribution patterns of marine planktonic ciliates. Sci. Rep. 6, 33555, http://dx.doi.org/10.1038/srep33555. Gismervik, I., 2005. Numerical and functional responses of choreoand oligotrich planktonic ciliates. Aquat. Microb. Ecol. 40, 163–1173. Glazier, D.S., 2005. Beyond the ‘3/4-power law’: variation in the intra-and interspecific scaling of metabolic rate in animals. Biol. Rev. 80, 611–662.

352


Görtz, H.-D., 1996. Symbiosis in ciliates. In: Hausmann, K., Bradbury, P.C. (Eds.), Ciliates: Cells as Organisms. Gustav Fischer, Stuttgart, pp. 441–462. Gravel, D., Albouy, C., Thuiller, W., 2016. The meaning of functional trait composition of food webs for ecosystem functioning. Philos. Trans. R. Soc. B 371, 20150268. Gronlien, H.K., Hagen, B., Sand, O., 2011. Microstome—macrostome transformation in the polymorphic ciliate Tetrahymena vorax leads to mechanosensitivity associated with prey-capture behaviour. J. Exp. Biol. 214, 2258–2266. Grossmann, L., Beisser, D., Bock, C., Chatzinotas, A., Jensen, M., Preisfeld, A., Psenner, R., Rahmann, S., Wodniok, S., Boenigk, J., 2016. Trade-off between taxon diversity and functional diversity in European lake ecosystems. Mol. Ecol. 25, 5876–5888. Grover, J.P., 1989. Influence of cell shape and size on algal competitive ability. J. Phycol. 25, 402–405. Guidi, L., Chaffron, S., Bittner, L., Eveillard, D., Larhlimi, A., Roux, S., Darzi, Y., Audic, S., Berline, L., Brum, J.R., 2016. Plankton networks driving carbon export in the oligotrophic ocean. Nature 53, 465–470. Gutiérrez, J.C., Callejas, S., Borniquel, S., Benítez, L., MartínGonzález, A., 2001. Ciliate cryptobiosis: a microbial strategy against environmental starvation. Int. Microbiol. 4, 151–157. Haeckel, E.H.P.A., 1866. Generelle Morphologie der Organismen. Allgemeine Grundzüge der organischen Formen-Wissenschaft, mechanisch begründet durch die von Charles Darwin reformirte Descendenz-Theorie. Zweiter Band: Allgemeine Entwicklungsgeschichte der Organismen. Georg Reimer, Berlin, Germany. Hansen, B., Bjornsen, P.K., Hansen, P.J., 1994. The size ratio between planktonic predators and their prey. Limnol. Oceanogr. 39, 395–403. Hansen, P.J., Bjørnsen, P.K., Hansen, B.W., 1997. Zooplankton grazing and growth: scaling within the 2-2,000-␮m body size range. Limnol. Oceanogr. 42, 687–704. Hardin, G., 1960. The competitive exclusion principle. Science 131, 1292–1297. Hausmann, K., Bradbury, P.C., 1996. Ciliates: Cells as Organisms. G. Fischer, Stuttgart, p. 485. Hemmingsen, A.M., 1960. Energy metabolism as related to body size and respiratory surfaces: and its evolution. Rep. Stenogr. Mem. Hosp. (Copenhagen) 9, 1–110. Heusner, A.A., 1982. Energy metabolism and body size I. Is the 0.75 mass exponent of Kleiber’s equation a statistical artifact? Respir. Physiol. 48, 1–12. Hewett, S.W., 1980. The effect of prey size on the functional and numerical responses of a protozoan predator to its prey. Ecology, 1075–1081. Hewett, S.W., 1987. Prey size and survivorship in Didinium nasutum (Ciliophora: Gymnostomatida). Trans. Am. Microsc. Soc., 134–138. Holling, C.S., 1959. The components of predation as revealed by a study of small-mammal predation of the European pine sawfly. Can. Entomol. 91, 293–320. Holt, R.D., 2009. Bringing the Hutchinsonian niche into the 21st century: ecological and evolutionary perspectives. Proc. Natl. Acad. Sci. U. S. A. 106, 19659–19665. Jack, J.D., Gilbert, J.J., 1997. Effects of metazoan predators on ciliates in freshwater plankton communities. J. Eukaryot. Microbiol. 44, 194–199.

Jackson, K.M., Berger, J., 1984. Survival of ciliate protozoa under starvation conditions and at low bacterial levels. Microb. Ecol. 10, 47–59. Jackson, K.M., Berger, J., 1985. Survivorship curves of ciliate protozoa under starvation conditions and at low bacterial levels. Protistologica 21, 17–24. Jakobsen, H.H., Hansen, P.J., 1997. Prey size selection: grazing and growth response of the small heterotrophic dinoflagellate Gymnodinium sp. and the ciliate Balanion comatum—a comparative study. Mar. Ecol. Prog. Ser. 158, 75–86. Jakobsen, H.H., Hyatt, C., Buskey, E.J., 2001. Growth and grazing on the ’Texas brown tide’alga Aureoumbra lagunensis by the tintinnid Amphorides quadrilineata. Aquat. Microb. Ecol. 23, 245–252. Jassey, V.E.J., Lamentowicz, M., Bragazza, L., Hofsommer, M.L., Mills, R.T.E., Buttler, A., Signarbieux, C., Robroek, B.J.M., 2016. Loss of testate amoeba functional diversity with increasing frost intensity across a continental gradient reduces microbial activity in peatlands. Eur. J. Protistol. 55, 190–202. Jeong, H.J., Shim, J.H., Lee, C.W., Kim, J.S., Koh, S.M., 1999. Growth and grazing rates of the marine planktonic ciliate Strombidinopsis sp. on red-tide and toxic dinoflagellates. J. Eukaryot. Microbiol. 46, 69–76. Jeong, H.J., Yoon, J.Y., Kim, J.S., Yoo, Y., Seong, K.A., 2002. Growth and grazing rates of the prostomatid ciliate Tiarina fusus on red-tide and toxic algae. Aquat. Microb. Ecol. 28, 289–297. Jeong, H.J., Kim, J.S., Song, J.Y., Kim, J.H., Kim, T.H., Kim, S.K., Kang, N.S., 2007. Feeding by protists and copepods on the heterotrophic dinoflagellates Pfiesteria piscicida, Stoeckeria algicida, and Luciella masanensis. Mar. Ecol. Prog. Ser. 349, 199–211. Jeong, H.J., Yoo, Y., Kim, J.S., Kang, N.S., Kim, T.H., Kim, J.H., 2004. Feeding by the marine planktonic ciliate Strombidinopsis jeokjo on common heterotrophic dinoflagellates. Aquat. Microb. Ecol. 36, 181–187. Jeong, H.J., Lim, A.S., Yoo, Y.D., Lee, M.J., Lee, K.H., Jang, T.Y., Lee, K., 2014. Feeding by Heterotrophic Dinoflagellates and Ciliates on the free-living Dinoflagellate Symbiodinium sp. (Clade E). J. Eukaryot. Microbiol. 61, 27–41. Jeong, H.J., Kim, T.H., Du Yoo, Y., Yoon, E.Y., Kim, J.S., Seong, K.A., Kim, K.Y., Park, J.Y., 2011. Grazing impact of heterotrophic dinoflagellates and ciliates on common red-tide euglenophyte Eutreptiella gymnastica in Masan Bay, Korea. Harmful Algae 10, 576–588. Jürgens, K., Massana, R., 2008. Protist grazing on marine bacterioplankon. In: Kirchman, D.L. (Ed.), Microbial Ecology of the Oceans. , 2nd ed. Wiley-Liss, New York, pp. 383–442. ˇ Jürgens, K., Simek, K., 2000. Functional response and particle size selection of Halteria cf. grandinella: a common freshwater oligotrichous ciliate. Aquat. Microb. Ecol. 22, 57–68. Jürgens, K., Skibbe, O., Jeppesen, E., 1999. Impact of metazooplankton on the composition and population dynamics of planktonic ciliates in a shallow hypertrophic lake. Aquat. Microb. Ecol. 17, 61–75. Kamiyama, T., Tsujino, M., Matsuyama, Y., Uchida, T., 2005. Growth and grazing rates of the tintinnid ciliate Favella taraikaensis on the toxic dinoflagellate Alexandrium tamarense. Mar. Biol. 147, 989–997. Kammerlander, B., Koinig, K.A., Rott, E., Sommaruga, R., Tartarotti, B., Trattner, F., Sonntag, B., 2016. Ciliate community structure and interactions within the planktonic food web in


two alpine lakes of contrasting transparency. Freshw. Biol. 61, 1950–1965. Karus, K., Paaver, T., Agasild, H., Zingel, P., 2014. The effects of predation by planktivorous juvenile fish on the microbial food web. Eur. J. Protistol. 50, 109–121. Katz, L., McManus, G., Snoeyenbos-West, O., Pirog, K., Griffin, A., Foissner, W., 2005. Reframing the microbial ‘Everything is Everywhere’ debate: evidence for high gene flow and diversity in ciliate morphospecies. Aquat. Microb. Ecol. 41, 55–65. Keeling, P.J., Burki, F., Wilcox, H.M., Allam, B., Allen, E.E., Amaral-Zettler, L.A., Armbrust, E.V., Archibald, J.M., Bharti, A.K., Bell, C.J., Beszteri, B., Bidle, K.D., Cameron, C.T., Campbell, L., Caron, D.A., Cattolico, R.A., Collier, J.L., Coyne, K., Davy, S.K., Deschamps, P., Dyhrman, S.T., Edvardsen, B., Gates, R.D., Gobler, C.J., Greenwood, S.J., Guida, S.M., Jacobi, J.L., Jakobsen, K.S., James, E.R., Jenkins, B., John, U., Johnson, M.D., Juhl, A.R., Kamp, A., Katz, L.A., Kiene, R., Kudryavtsev, A., Leander, B.S., Lin, S., Lovejoy, C., Lynn, D., Marchetti, A., McManus, G., Nedelcu, A.M., Menden-Deuer, S., Miceli, C., Mock, T., Montresor, M., Moran, M.A., Murray, S., Nadathur, G., Nagai, S., Ngam, P.B., Palenik, B., Pawlowski, J., Petroni, G., Piganeau, G., Posewitz, M.C., Rengefors, K., Romano, G., Rumpho, M.E., Rynearson, T., Schilling, K.B., Schroeder, D.C., Simpson, A.G.B., Slamovits, C.H., Smith, D.R., Smith, G.J., Smith, S.R., Sosik, H.M., Stief, P., Theriot, E., Twary, S.N., Umale, P.E., Vaulot, D., Wawrik, B., Wheeler, G.L., Wilson, W.H., Xu, Y., Zingone, A., Worden, A.Z., 2014. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP). Illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 12, e1001889. Key, T., McCarthy, A., Campbell, D.A., Six, C., Roy, S., Finkel, Z.V., 2010. Cell size trade-offs govern light exploitation strategies in marine phytoplankton. Environ. Microbiol. 12, 95–104. Kidder, G., Lilly, D., Claff, C., 1940. Growth studies on ciliates: IV. The influence of food on the structure and growth of Glaucoma vorax sp. nov. Biol. Bull. 78, 9–23. Kimmance, S.A., Atkinson, D., Montagnes, D.J.S., 2006. Do temperature–food interactions matter? Responses of production and its components in the model heterotrophic flagellate Oxyrrhis marina. Aquat. Microb. Ecol. 42, 63–73. Klauschies, T., Vasseur, D.A., Gaedke, U., 2016. Trait adaptation promotes species coexistence in diverse predator and prey communities. Ecol. Evol. 6, 4141–4159. Klaveness, D., 1984. Studies on the morphology, food selection and growth of two planktonic freshwater strains of Coleps sp. Protistologica 20, 335–349. Kleiber, M., 1932. Body size and metabolism. Hilgardia 6, 315–353. Kleiber, M., 1947. Body size and metabolic rate. Physiol. Rev. 27, 511–541. Kopp, M., Tollrian, R., 2003. Trophic size polyphenism in Lembadion bullinum: costs and benefits of an inducible offense. Ecology 84, 641–651. Kruk, C., Huszar, V.L., Peeters, E.T., Bonilla, S., Costa, L., LÜRLING, M., Reynolds, C.S., Scheffer, M., 2010. A morphological classification capturing functional variation in phytoplankton. Freshwat. Biol. 55, 614–627. Kuhlmann, H.-W., 1993. Giants in Lembadion bullinum (Ciliophora, Hymenostomata)—general morphology and inducing conditions. Arch. Protistenkd. 143, 325–336.

353

Kuhlmann, H.-W., Heckmann, K., 1985. Interspecific morphogens regulating prey-predator relationships in protozoa. Science 227, 1347–1349. Kuhlmann, H.-W., Heckmann, K., 1994. Predation risk of typical ovoid and ‘winged’ morphs of Euplotes (Protozoa, Ciliophora). Hydrobiologia 284, 219–229. Kuhlmann, H.-W., Kusch, J., Heckmann, K., 1999. Predatorinduced defenses in ciliated protozoa. In: Tollrian, R., Harvell, C.D. (Eds.), The Ecology and Evolution of Inducible Defenses. , 2nd ed. Princeton University Press, Princeton, New Jersey, pp. 142–159. Laliberté, E., Legendre, P., Shipley, B., 2014. FD: Measuring Functional Diversity from Multiple Traits, and Other Tools for Functional Ecology. R Package Version 1.0–12. Lavorel, S., Garnier, E., 2002. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Funct. Ecol. 16, 545–556. Lavorel, S., Storkey, J., Bardgett, R.D., de Bello, F., Berg, M.P., Le Roux, X., Moretti, M., Mulder, C., Pakeman, R.J., Díaz, S., Harrington, R., 2013. A novel framework for linking functional diversity of plants with other trophic levels for the quantification of ecosystem services. J. Veg. Sci. 24, 942–948. Lee, K.H., Jeong, H.J., Jang, T.Y., Lim, A.S., Kang, N.S., Kim, J.H., Kim, K.Y., Park, K.-T., Lee, K., 2014. Feeding by the newly described mixotrophic dinoflagellate Gymnodinium smaydae: feeding mechanism, prey species, and effect of prey concentration. J. Exp. Mar. Biol. Ecol. 459, 114–125. Legendre, P., Legendre, L.F., 2012. Numerical Ecology, Third English Edition. Elsevier, Amsterdam. Legendre, L., Rivkin, R.B., 2015. Flows of biogenic carbon within marine pelagic food webs: roles of microbial competition switches. Mar. Ecol. Prog. Ser. 521, 19–30. Li, C., Xu, K., Lei, Y., 2011. Growth and grazing responses to temperature and prey concentration of Condylostoma spatiosum a large benthic ciliate, feeding on Oxyrrhis marina. Aquat. Microb. Ecol. 64, 97–104. Li, J., Montagnes, D.J.S., 2015. Restructuring fundamental predator–prey models by recognising prey-dependent conversion efficiency and mortality rates. Protist 166, 211–223. Li, J., Fenton, A., Kettley, L., Roberts, P., Montagnes, D.J.S., 2013. Reconsidering the importance of the past in predator–prey models: both numerical and functional responses depend on delayed prey densities. Proc. R. Soc. B 280 (1768), http://dx.doi.org/10.1098/rspb.2013.1389. Li, J., Chen, F., Liu, Z., Zhao, X., Yang, K., Lu, W., Cui, K., 2016. Bottom-up versus top-down effects on ciliate community composition in four eutrophic lakes (China). Eur. J. Protistol. 53, 20–30. Lilly, D.M., 1942. Nutritional and supplementary factors in the growth of carnivorous ciliates. Physiol. Zool. 15, 146–167. Lindholm, T., 1985. Mesodinium rubrum—a unique photosynthetic ciliate. Adv. Aquat. Microbiol. 3, 1–48. Lischke, B., Weithoff, G., Wickham, S.A., Attermeyer, K., Grossart, H.-P., Scharnweber, K., HIlt, S., Gaedke, U., 2016. Large biomass of small feeders: ciliates may dominate herbivory in eutrophic lakes. J. Plankton Res. 38, 2–15. Litchman, E., Klausmeier, C.A., 2008. Trait-based community ecology of phytoplankton. Annu. Rev. Ecol. Evol. Syst. 39, 615–639. Lynn, D.H., 2008. The Ciliated Protozoa–Characterization, Classification, and Guide to the Literature, 3rd ed. Springer, Dordrecht.

354


Madoni, P., Berman, T., Hadas, O., Pinkas, R., 1990. Food selection and growth of the planktonic ciliate Coleps hirtus isolated from a monomictic subtropical lake. J. Plankton Res. 12, 735–741. Májeková, M., Paal, T., Plowman, N.S., Bryndová, M., Kasari, L., Norberg, A., Weiss, M., Bishop, T.R., Luke, S.H., Sam, K., Le Bagousse-Pinguet, Y., Lepˇs, J., Götzenberger, L., de Bello, F., 2016. Evaluating functional diversity: missing trait data and the importance of species abundance structure and data transformation. PLoS One 11, e0149270. Mason, N.W., MacGillivray, K., Steel, J.B., Wilson, J.B., 2003. An index of functional diversity. J. Veg. Sci. 14, 571–578. Mason, N.W., Mouillot, D., Lee, W.G., Wilson, J.B., 2005. Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos 111, 112–118. Massana, R., Unrein, F., Rodríguez-Martínez, R., Forn, I., Lefort, T., Pinhassi, J., Not, F., 2009. Grazing rates and functional diversity of uncultured heterotrophic flagellates. ISME J. 3, 588–596. Matthes, D., Guhl, W., Haider, G., 1988. Suctoria und Urceolariidae (Peritricha). G. Fischer, Stuttgart. Mazanec, A.I., Trevarrow, B., 1998. Coleps, scourge of the baby zebrafish. Zebrafish Sci. Monit. 5, 1. McManus, G.B., Xu, D., Costas, B.A., Katz, L.A., 2010. Genetic identities of cryptic species in the Strombidium stylifer/apolatum/oculatum cluster, including a description of Strombidium rassoulzadegani n. sp. J. Eukaryot. Microbiol. 57, 369–378. Medinger, R., Nolte, V., Pandey, R.V., Jost, S., Ottenwälder, B., Schlötterer, C., Boenigk, J., 2010. Diversity in a hidden world: potential and limitation of next-generation sequencing for surveys of molecular diversity of eukaryotic microorganisms. Mol. Ecol. 19, 32–40. Miller, T.E., Moran, E.R., terHorst, C.P., 2014. Rethinking niche evolution: experiments with natural communities of protozoa in pitcher plants. Am. Nat. 184, 277–283. Mitra, A., Flynn, K.J., Burkholder, J.M., Berge, T., Calbet, A., Raven, J.A., Granéli, E., Glibert, P.M., Hansen, P.J., Stoecker, D.K., Thingstad, F., Tillmann, U., Våge, S., Wilken, S., Zubkov, M.V., 2014. The role of mixotrophic protists in the biological carbon pump. Biogeosciences 11, 995–1005. Mitra, A., Flynn, K.J., Tillmann, U., Raven, J.A., Caron, D., Stoecker, D.K., Not, F., Hansen, P.J., Hallegraeff, G., Sanders, R., Wilken, S., McManus, G., Johnson, M., Pitta, P., Våge, S., Berge, T., Calbet, A., Thingstad, F., Jeong, H.J., Burkholder, J., Glibert, P.M., Granéli, E., Lundgren, V., 2016. Defining planktonic protist functional groups on mechanisms for energy and nutrient acquisition: incorporation of diverse mixotrophic strategies. Protist 167, 106–120. Miyake, A., Harumoto, T., 1996. Defensive function of trichocysts in paramecium against the predatory ciliate Monodinium balbiani. Eur. J. Protistol. 32, 128–133. Miyake, A., Buonanno, F., Saltalamacchia, P., Masaki, M.E., Iio, H., 2003. Chemical defense by means of extrusive cortical granules in the heterotrich ciliate Climacostomum virens. Eur. J. Protistol. 39, 25–36. Montagnes, D.J.S., 1996. Growth responses of planktonic ciliates in the genera Strobilidium and Strombidium. Mar. Ecol. Prog. Ser. 130, 241–254. Montagnes, D.J.S., 2013. Ecophysiology and behavior of tintinnids. In: Dolan, J.R., Montagnes, D.J.S., Agatha, S., Coats, D.W., Stoecker, D.K. (Eds.), The Biology and Ecology of Tintinnid Cil-

iates: Models for Marine Plankton. Wiley-Blackwell, Chichester, UK, pp. 85–121. Montagnes, D.J.S., Lessard, E.J., 1999. Population dynamics of the marine planktonic ciliate Strombidinopsis multiauris: its potential to control phytoplankton blooms. Aquat. Microb. Ecol. 20, 167–181. Montagnes, D.J.S., Weisse, T., 2000. Fluctuating temperatures affect growth and production rates of planktonic ciliates. Aquat. Microb. Ecol. 21, 97–102. Montagnes, D.J.S., Berger, J.D., Taylor, F.J.R., 1996. Growth rate of the marine planktonic ciliate Strombidinopsis cheshiri Snyder and Ohman as a function of food concentration and interclonal variability. J. Exp. Mar. Biol. Ecol. 206, 121–132. Montagnes, D.J.S., Roberts, E., Lukes, J., Lowe, C.D., 2012. The rise of model protozoa. Trends Microbiol. 20, 184–191. Montagnes, D.J.S., Wilson, D., Brooks, S.J., Lowe, C., Campey, M., 2002. Cyclical behaviour of the tide-pool ciliate Strombidium oculatum. Aquat. Microb. Ecol. 28, 55–68. Montagnes, D.J.S., Morgan, G., Bissinger, J.E., Atkinson, D., Weisse, T., 2008a. Short-term temperature change may impact freshwater carbon flux: a microbial perspective. Glob. Change Biol. 14, 2810–2822. Montagnes, D.J.S., Barbosa, A.B., Boenigk, J., Davidson, K., Jürˇ gens, K., Macek, M., Parry, J., Roberts, E.C., Simek, K., 2008b. Selective feeding behaviour of free-living protists: avenues for continued study. Aquat. Microb. Ecol. 53, 83–98. Mouchet, M., Villéger, S., Mason, N., Mouillot, D., 2010. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules. Funct. Ecol. 24, 867–876. Mouillot, D., Bellwood, D.R., Baraloto, C., Chave, J., Galzin, R., Harmelin-Vivien, M., Kulbicki, M., Lavergne, S., Lavorel, S., Mouquet, N., 2013. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biol. 11, e1001569. Müller, H., Geller, W., 1993. Maximum growth rates of aquatic ciliated protozoa: the dependence on body size and temperature reconsidered. Arch. Hydrobiol. 126, 315–327. Müller, H., Schlegel, A., 1999. Responses of three freshwater planktonic ciliates with different feeding modes to cryptophyte and diatom prey. Aquat. Microb. Ecol. 17, 49–60. Müller, H., Weisse, T., 1994. Laboratory and field observations on the scuticociliate Histiobalantium from the pelagic zone of Lake Constance, FRG. J. Plankton Res. 16, 391–401. Müller, H., Wünsch, C., 1999. Seasonal dynamics of cyst formation of pelagic strombidiid ciliates in a deep prealpine lake. Aquat. Microb. Ecol. 17, 37–47. Müller, H., Stadler, P., Weisse, T., 2002. Seasonal dynamics of cyst formation of strombidiid ciliates in alpine LakeMondsee, Austria. Aquat. Microb. Ecol. 29, 181–188. Müller, H., Schöne, A., Pinto-Coelho, R.M., Schweizer, A., Weisse, T., 1991. Seasonal succession of ciliates in Lake Constance. Microb. Ecol. 21, 119–138. Muscarella, R., Uriarte, M., 2016. Do community-weighted mean functional traits reflect optimal strategies? Proc. R. Soc. B 283 (1827), http://dx.doi.org/10.1098/rspb.2015.2434. Nanney, D.L., Park, C., Preparata, R., Simon, E.M., 1998. Comparison of sequence differences in a variable 23S rRNA domain among sets of cryptic species of ciliated protozoa. J. Euk. Microbiol. 45, 91–100.


Ohman, M.D., Snyder, R.A., 1991. Growth kinetics of the omnivorous oligotrich ciliate Strombidium sp. Limnol. Oceanogr. 36, 922–935. Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., 2017. Vegan: Community Ecology Package. R package version 2. 4-2, https://CRAN.R-project.org/package=vegan. Pavoine, S., Ollier, S., Dufour, A.B., 2005. Is the originality of a species measurable? Ecol. Lett. 8, 579–586. Pérez-Uz, B., 1995. Growth rate variability in geographically divers clones of Uronema (Ciliophora: Scuticociliatida). FEMS Microb. Ecol. 16, 193–204. Pérez-Uz, B., 1996. Bacterial preferences and growth kinetic variation in Uronema marinum and Uronema nigricans (Ciliophora: Scuticociliatida). Microb. Ecol. 31, 189–198. Petchey, O.L., Gaston, K.J., 2006. Functional diversity: back to basics and looking forward. Ecol. Lett. 9, 741–758. Porter, K.G., Pace, M.L., Battey, F.J., 1979. Ciliate protozoans as links in freshwater planktonic food chains. Nature 277, 563–565. Posch, T., Arndt, H., 1996. Uptake of sub-micrometre-and micrometre-sized detrital particles by bacterivorous and omnivorous ciliates. Aquat. Microb. Ecol. 10, 45–53. Posch, T., Eugster, B., Pomati, F., Pernthaler, J., Pitsch, G., Eckert, E.M., 2015. Network of interactions between ciliates and phytoplankton during spring. Front. Microbiol. 6, article 1289. Pratt, J.R., Cairns Jr., J., 1985. Functional groups in the protozoa: roles in differing ecosystems. J. Protozool. 32, 415–423. Pröschold, T., Darienko, T., Silva, D.C., Reisser, W., Krienitz, L., 2011. The systematics of Zoochlorella revisited employing an integrative approach. Environ. Microbiol. 13, 350–364. Rao, C.R., 1982a. Diversity and dissimilarity coefficients: a unified approach. Theor. Popul. Biol. 21, 24–43. Rao, C.R., 1982b. Diversity: its measurement, decomposition, apportionment and analysis. Sankhy¯a: Indian J. Stat. Ser. A, 1–22. Raven, J., 1997. Phagotrophy in phototrophs. Limnol. Oceanogr. 42, 198–205. R Core Team, 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. Reisser, W., 1986. Endosymbiotic associations of freshwater protozoa and algae. Prog. Protistol. 1, 195–214. Reynolds, C.S., 1984. The Ecology of Freshwater Phytoplankton. University Press Cambridge. Reynolds, C.S., Huszar, V., Kruk, C., Naselli-Flores, L., Melo, S., 2002. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428. Richardson, D.M., Pyˇsek, P., 2007. Elton, CS 1958: the ecology of invasions by animals and plants. London: Methuen. Prog. Phys. Geog. 31, 659–666. Ricotta, C., 2005. A note on functional diversity measures. Basic Appl. Ecol. 6, 479–486. Roberts, E.C., Wootton, E.C., Davidson, K., Jeong, H.J., Lowe, C.D., Montagnes, D.J.S., 2011. Feeding in the dinoflagellate Oxyrrhis marina: linking behaviour with mechanisms. J. Plankton Res. 33, 603–614. Rosati, G., Modeo, L., 2003. Extrusomes in ciliates: diversification, distribution, and phylogenetic implications. J. Eukaryot. Microbiol. 30, 383–402.

355

Roscher, C., Schumacher, J., Baade, J., Wilcke, W., Gleixner, G., Weisser, W.W., Schmid, B., Schulze, E.-D., 2004. The role of biodiversity for element cycling and trophic interactions: an experimental approach in a grassland community. Basic Appl. Ecol. 5, 107–121. Rose, J.M., Fitzpatrick, E., Wang, A., Gast, R.J., Caron, D.A., 2013. Low temperature constrains growth rates but not short-term ingestion rates of Antarctic ciliates. Polar Biology 36, 645–659. Rosenzweig, M.L., MacArthur, R.H., 1963. Graphical representation and stability conditions of predator–prey interactions. Am. Nat., 209–223. Rousk, J., Brookes, P.C., Bååth, E., 2009. Contrasting soil pH effects on fungal and bacterial growth suggest functional redundancy in carbon mineralization. Appl. Environ. Microbiol. 75, 1589–1596. Rubner, M., 1883. Über den Einfluß der Körpergröße auf Stoff- und Kraftwechsel. Z. Biol. 19, 536–562. Sanders, R.W., Wickham, S.A., 1993. Planktonic protozoa and metazoa: predation, food quality and population control. Mar. Microb. Food Webs 7, 197–223. Sanders, R.W., Porter, K.G., Bennett, S.J., DeBiase, A.E., 1989. Seasonal patterns of bacterivory by flagellates, cilliates, rotifers, and cladocerans in a freshwater plankton community. Limnol. Oceanogr. 34, 673–687. Santoferrara, L.F., McManus, G., 2017. Integrating biodiversity dimensions in choreotrichs and oligotrichs in marine plankton. Eur. J. Protistol. 61, 323–330, http://dx.doi.org/10.1016/j.ejop.2017.04.004. Santoferrara, L.F., Guida, S., Zhang, H., McManus, G.B., 2014. De novo transcriptomes of a mixotrophic and a heterotrophic ciliate from marine plankton. PLoS One 9, e101418. Scheiner, S.M., Kosman, E., Presley, S.J., Willig, M.R., 2016. Decomposing functional diversity. Methods Ecol. Evol., http://dx.doi.org/10.1111/2041-210x.12696. Scherwass, A., Fischer, Y., Arndt, H., 2005. Detritus as a potential food source for protozoans: utilization of fine particulate plant detritus by a heterotrophic flagellate, Chilomonas paramecium, and a ciliate, Tetrahymena pyriformis. Aquat. Ecol. 39, 439–445. Schleuter, D., Daufresne, M., Massol, F., Argillier, C., 2010. A user’s guide to functional diversity indices. Ecol. Monogr. 80, 469–484. Schmidt-Nielsen, K., 1984. Scaling: Why Is Animal Size So Important? Cambridge Univ. Press, New York. Sheldon, R., Prakash, A., Sutcliffe, W., 1972. The size distribution of particles in the ocean. Limnol. Oceanogr. 17, 327–340. Sherr, E.B., Sherr, B.F., 1987. High rates of consumption of bacteria by pelagic ciliates. Nature 325, 710–711. Sherr, E.B., Sherr, B.F., 1988. Role of microbes in pelagic food webs: a revised concept. Limnol. Oceanogr. 33, 1225–1227. Sherr, E.B., Sherr, B.F., 1994. Bacterivory and herbivory: key roles of phagotrophic protists in pelagic food webs. Microb. Ecol. 28, 223–235. Sherr, E.B., Sherr, B.F., 2002. Significance of predation by protists in aquatic microbial food webs. Antonie Van Leeuwenhoek 81, 293–308. Sherr, E.B., Rassoulzadegan, F., Sherr, B.F., 1989. Bacterivory by pelagic choreotrichous ciliates in coastal waters of the NW Mediterranean Sea. Mar. Ecol. Prog. Ser. 55, 235–240. Simon, M., Jardillier, L., Deschamps, P., Moreira, D., Restoux, G., Bertolino, P., López-García, P., 2015. Complex communities of small protists and unexpected occurrence of typical marine lin-

356


eages in shallow freshwater systems. Environ. Microbiol. 17, 3610–3627. Simpson, E.H., 1949. Measurement of diversity. Nature 163, 688. Skogstad, A., Granskog, L., Klaveness, D., 1987. Growth of freshwater ciliates offered planktonic algae as food. J. Plankton Res. 9, 503–512. Stabell, T., Andersen, T., Klaveness, D., 2002. Ecological significance of endosymbionts in a mixotrophic ciliate—an experimental test of a simple model of growth coordination between host and symbiont. J. Plankton Res. 24, 889–899. Steele, J.A., Countway, P.D., Xia, L., Vigil, P.D., Beman, J.M., Kim, D.Y., Chow, C.-E.T., Sachdeva, R., Jones, A.C., Schwalbach, M.S., Rose, J.M., Hewson, I., Patel, A., Sun, F., Caron, D.A., Fuhrman, J.A., 2011. Marine bacterial, archaeal and protistan association networks reveal ecological linkages. ISME J. 5, 1414–1425. Steele, J.H., 1974. The Structure of Marine Ecosystems. Harvard Univ. Press, Cambridge, MA. Stoeck, T., Breiner, H.-W., Filker, S., Ostermaier, V., Kammerlander, B., Sonntag, B., 2014. A morphogenetic survey on ciliate plankton from a mountain lake pinpoints the necessity of lineage-specific barcode markers in microbial ecology. Environ. Microbiol. 16, 430–444. Stoecker, D.K., 1991. Mixotrophy in marine planktonic ciliates: physiological and ecological aspects of plastid retention by oligotrichs. In: Reid, P.C., Turley, C.M., Burkill, P.H. (Eds.), Protozoa and Their Role in Marine Processes. Springer-Verlag, Berlin, pp. 161–179. Stoecker, D.K., Taniguchi, A., Michaels, A.E., 1989. Abundance of autotrophic, mixotrophic and heterotrophic planktonic ciliates in shelf and slope waters. Mar. Ecol. Prog. Ser. 50, 241–254. Stoecker, D.K., Hansen, P.J., Caron, D.A., Mitra, A., 2017. Mixotrophy in the marine plankton. Ann. Rev. Mar. Sci. 9, 311–335. Stoecker, D.K., Johnson, M.D., de Vargas, C., Not, F., 2009. Acquired phototrophy in aquatic protists. Aquat. Microb. Ecol. 57, 279–310. Straile, D., 1997. Gross growth efficiencies of protozoan and metazoan zooplankton and their dependence on food concentration predator–prey weight ratio, and taxonomic group. Limnol. Oceanogr. 42, 1375–1385. Straile, D., 1998. Biomass allocation and carbon flow in the pelagic food web of Lake Constance. Arch. Hydrobiol. Spec. Issues Advanc. Limnol. 53, 545–563. Strom, S.L., Morello, T.A., 1998. Comparative growth rates and yields of ciliates and heterotrophic dinoflagellates. J. Plankt. Res. 20, 571–584. Strüder-Kypke, M.C., Wright, A.-D.G., Jerome, C.A., Lynn, D.H., 2001. Parallel evolution of histophagy in ciliates of the genus Tetrahymena. BMC Evol. Biol. 1, 5. Sunagawa, S., Coelho, L.P., Chaffron, S., Kultima, J.R., Labadie, K., Salazar, G., Djahanschiri, B., Zeller, G., Mende, D.R., Alberti, A., Cornejo-Castillo, F.M., Costea, P.I., Cruaud, C., D’Ovidio, F., Engelen, S., Ferrera, I., Gasol, J.M., Guidi, L., Hildebrand, F., Kokoszka, F., Lepoivre, C., Lima-Mendez, G., Poulain, J., Poulos, B.T., Royo-Llonch, M., Sarmento, H., Vieira-Silva, S., Dimier, C., Picheral, M., Searson, S., Kandels-Lewis, S., Boss, E., Follows, M., Karp-Boss, L., Krzic, U., Reynaud, E.G., Sardet, C., Sieracki, M., Velayoudon, D., Bowler, C., De Vargas, C., Gorsky, G., Grimsley, N., Hingamp, P., Iudicone, D., Jaillon, O., Not, F., Ogata, H., Pesant, S., Speich, S., Stemmann, L., Sullivan, M.B., Weissenbach, J., Wincker, P., Karsenti, E., Raes,

J., Acinas, S.G., Bork, P., 2015. Structure and function of the global ocean microbiome. Science 348. Sutherland, W.J., Freckleton, R.P., Godfray, H.C.J., Beissinger, S.R., Benton, T., Cameron, D.D., Carmel, Y., Coomes, D.A., Coulson, T., Emmerson, M.C., 2013. Identification of 100 fundamental ecological questions. J. Ecol. 101, 58–67. Tarangkoon, W., Hansen, P.J., 2011. Prey selection, ingestion and growth responses of the common marine ciliate Mesodinium pulex in the light and in the dark. Aquat. Microb. Ecol. 62, 25–38. Taylor, W.D., 1978. Maximum growth rate, size and commonness in a community of bactivorous ciliates. Oecologia 36, 263–272. Taylor, W.D., Shuter, B.J., 1981. Body size, genome size, and intrinsic rate of increase in ciliated protozoa. Am. Nat. 118, 160–172. TerHorst, C., 2011. Experimental evolution of protozoan traits in response to interspecific competition. J. Evol. Biol. 24, 36–46. TerHorst, C.P., 2010. Evolution in response to direct and indirect ecological effects in pitcher plant inquiline communities. Am. Nat. 176, 675–685. Tilman, D., 1999. The ecological consequences of changes in biodiversity: a search for general principles. Ecology 80, 1455–1474. Tilman, D., 2001. Functional diversity. Encycl. Biodivers. 3, 109–120. Tirok, K., Gaedke, U., 2006. Spring weather determines the relative importance of ciliates, rotifers and crustaceans for the initiation of the clear-water phase in a large, deep lake. J. Plankton Res. 28, 361–373. Tirok, K., Gaedke, U., 2007a. The effect of irradiance, vertical mixing and temperature on spring phytoplankton dynamics under climate change: long-term observations and model analysis. Oecologia 150, 625–642. Tirok, K., Gaedke, U., 2007b. Regulation of planktonic ciliate dynamics and functional composition during spring in Lake Constance. Aquat. Microb. Ecol. 49, 87–100. Tirok, K., Gaedke, U., 2010. Internally driven alternation of functional traits in a multispecies predator–prey system. Ecology 91, 1748–1762. Tirok, K., Bauer, B., Wirtz, K., Gaedke, U., 2011. Predator–prey dynamics driven by feedback between functionally dverse tophic levels. PLoS One 6, e27357. Tollrian, R., Harvell, C.D., 1999. The Ecology and Evolution of Inducible Defenses. Princeton Univ. Press, Princeton, New Jersey. Turley, C.M., Newell, R.C., Robins, D.B., 1986. Survival strategies of two small marine ciliates and their role in regulating bacterial community structure under experimental conditions. Mar. Ecol. Prog. Ser. 33, 59–70. Van Wichelen, J., Johansson, L.S., Vanormelingen, P., Declerck, S.A., Lauridsen, T.L., De Meester, L., Jeppesen, E., Vyverman, W., 2013. Planktonic ciliate community structure in shallow lakes of lowland Western Europe. Eur. J. Protistol. 49, 538–551. Verity, P.G., 1985. Grazing, respiration, excretion, and growth rates of tintinnids. Limnol. Oceanogr. 30, 1268–1282. Verity, P.G., 1991. Feeding in planktonic protozoans, evidence for non-random acquisition of prey. J. Protozool. 38, 69–76. Verity, P.G., Villareal, T.A., 1986. The relative food value of diatoms, dinoflagellates, flagellates, and cyanobacteria for tintinnid ciliates. Arch. Protistenkd. 131, 71–84. Verni, F., Rosati, G., 2011. Resting cysts: a survival strategy in Protozoa Ciliophora. Ital. J. Zool. 78, 134–145.


Villéger, S., Mason, N.W., Mouillot, D., 2008. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301. Violle, C., Navas, M.-L., Vile, D., Kazakou, E., Fortunel, C., Hummel, I., Garnier, E., 2007. Let the concept of trait be functional! Oikos 116, 882–892. Ward, B.A., Follows, M.J., 2016. Marine mixotrophy increases trophic transfer efficiency, mean organism size, and vertical carbon flux. Proc. Natl. Acad. Sci. U. S. A. 113, 2958–2963. Warren, A., Patterson, D.J., Dunthorn, M., Clamp, J.C., AchillesDay, U.E.M., Aescht, E., Al-Farraj, S.A., Al-Quraishy, S., Al-Rasheid, K., Carr, M., Day, J.G., Dellinger, M., El-Serehy, H.A., Fan, Y., Gao, F., Gao, S., Gong, J., Gupta, R., Hu, X., Kamra, K., Langlois, G., Lin, X., Lipscomb, D., Lobban, C.S., Luporini, P., Lynn, D.H., Ma, H., Macek, M., Mackenzie-Dodds, J., Makhija, S., Mansergh, R.I., Martín-Cereceda, M., McMiller, N., Montagnes, D.J.S., Svetlana, N., Ong’ondo, G.O., Pérez-Uz, B., Purushothaman, J., Quintela-Alonso, P., Rotterová, J., Santoferrara, L., Shao, C., Shen, Z., Shi, X., Song, W., Stoeck, T., La Terza, A., Vallesi, A., Wang, M., Weisse, T., Wiackowski, K., Wu, L., Xu, K., Yi, Z., Zufall, R., Agatha, S., 2017. Beyond the code: a guide to the description and documentation of biodiversity in ciliated protists (Alveolata, Ciliophora). J. Eukaryot. Microbiol., http://dx.doi.org/10.1111/jeu.12391. Weisse, T., 2002. The significance of inter- and intraspecific variation in bacterivorous and herbivorous protists. Antonie Van Leeuwenhoek 81, 327–341. Weisse, T., 2003. Pelagic microbes—protozoa and the microbial food web. In: O’Sullivan, P., Reynolds, C.S. (Eds.), The Lakes Handbook. Blackwell Science Ltd., Oxford, pp. 417–460. Weisse, T., 2004. Meseres corlissi: a rare oligotrich ciliate adapted to warm water and temporary habitats. Aquat. Microb. Ecol. 37, 75–83. Weisse, T., 2006. Freshwater ciliates as ecophysiological model organisms—lessons from Daphnia, major achievements, and future perspectives. Arch. Hydrobiol. 167, 371–402. Weisse, T., 2014. Ciliates and the rare biosphere—community ecology and population dynamics. J. Eukaryot. Microb. 61, 419–433. Weisse, T., Frahm, A., 2002. Direct and indirect impact of two common rotifer species (Keratella spp.) on two abundant ciliate species (Urotricha furcata, Balanion planctonicum). Freshw. Biol. 47, 53–64. Weisse, T., Montagnes, D.J.S., 1998. Effect of temperature on inter- and intraspecific isolates of Urotricha (Prostomatida, Ciliophora). Aquat. Microb. Ecol. 15, 285–291. Weisse, T., Rammer, S., 2006. Pronounced ecophysiological clonal differences of two common freshwater ciliates, Coleps spetai (Prostomatida) and Rimostrombidium lacustris (Oligotrichida), challenge the morphospecies concept. J. Plankton Res. 27, 55–63. Weisse, T., Sonntag, B., 2016. Ciliates in planktonic food webs: communication and adaptive response. In: Witzany, G., Nowacki, M. (Eds.), Biocommunication of Ciliates. Springer International Publishing, Cham, pp. 351–372. Weisse, T., Stadler, P., 2006. Effect of pH on growth, cell volume, and production of freshwater ciliates: and implications for their distribution. Limnol. Oceanogr. 51, 1708–1715. Weisse, T., Scheffel, U., Stadler, P., Foissner, W., 2007. Local adaptation among geographically distant clones of the cosmopolitan

357

freshwater ciliate Meseres corlissi. II. Response to pH. Aquat. Microb. Ecol. 47, 289–297. Weisse, T., Scheffel, U., Stadler, P., Foissner, W., 2013a. Functional ecology of the ciliate Glaucomides bromelicola, and comparison with the sympatric species Bromeliothrix metopoides. J. Eukaryot. Microb. 60, 578–587. Weisse, T., Scheffel, U., Stadler, P., Foissner, W., 2013b. Bromeliothrix metopoides, a boom and bust ciliate (Ciliophora: Colpodea) from tank bromeliads. Eur. J. Protistol. 49, 406–419. Weisse, T., Stadler, P., Lindström, E.S., Kimmance, S.A., Montagnes, D.J.S., 2002. Interactive effect of temperature and food concentration on growth rate: a test case using the small freshwater ciliate Urotricha farcta. Limnol. Oceanogr. 47, 1447–1455. Weisse, T., Anderson, R., Arndt, H., Calbet, A., Hansen, P.J., Montagnes, D.J.S., 2016. Functional ecology of aquatic phagotrophic protists concepts, limitations, and perspectives. Eur. J. Protistol. 55, 50–74. Weisse, T., Karstens, N., Meyer, V.C.M., Janke, L., Lettner, S., Teichgräber, K., 2001. Niche separation in common prostome freshwater ciliates: the effect of food and temperature. Aquat. Microb. Ecol. 26, 167–179. Weisse, T., Müller, H., Pinto-Coelho, R.M., Schweizer, A., Springmann, D., Baldringer, G., 1990. Response of the microbial loop to the phytoplankton spring bloom in a large prealpine lake. Limnol. Oceanogr. 35, 781–794. Weithoff, G., 2003. The concepts of ‘plant functional types’ and ‘functional diversity’in lake phytoplankton—a new understanding of phytoplankton ecology? Freshw. Biol. 48, 1669–1675. Wickham, S.A., 1995. Trophic relations between cyclopoid copepods and ciliated protists: complex interactions link the microbial and classic food webs. Limnol. Oceanogr. 40, 1173–1181. Wickham, S.A., Gilbert, J.J., 1991. Relative vulnerabilities of natural rotifer and ciliate communities in cladocerans: laboratory and field experiments. Freshwater Biol. 26, 77–86. Wickham, S.A., Gugenberger, E., 2008. Evaluating inducible morphological defences in the common freshwater ciliate, Coleps hirtus. J. Plankton Res. 30, 1315–1321. Wickham, S.A., Lynn, D.H., 1990. Relations between growth rate, cell size, and DNA content in colpodean ciliates (Ciliophora: Colpodea). Europ. J. Protistol. 25, 345–352. Wollrab, S., Diehl, S., 2015. Bottom-up responses of the lower oceanic food web are sensitive to copepod mortality and feeding behavior. Limnol. Oceanogr. 60, 641–656. Worden, A.Z., Follows, M.J., Giovannoni, S.J., Wilken, S., Zimmerman, A.E., Keeling, P.J., 2015. Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes. Science 347, 1257594. Wymore, A.S., Keeley, A.T., Yturralde, K.M., Schroer, M.L., Propper, C.R., Whitham, T.G., 2011. Genes to ecosystems: exploring the frontiers of ecology with one of the smallest biological units. N. Phytol. 191, 19–36. Xu, G., Xu, Y., Xu, H., 2016. Insights into discriminating water quality status using new biodiversity measures based on a trait hierarchy of body-size units. Ecol. Indic. 60, 980–986. Xu, G., Zhong, X., Wang, Y., Warren, A., Xu, H., 2014. A multivariate approach to the determination of an indicator species pool for community-based bioassessment of marine water quality. Mar. Pollut. Bull. 87, 147–151. Yang, Z., Lowe, C.D., Crowther, W., Fenton, A., Watts, P.C., Montagnes, D.J., 2013. Strain-specific functional and numeri-

358


cal responses are required to evaluate impacts on predator–prey dynamics. ISME J. 7, 405–416. Yoo, D.Y., Jeong, H.J., Lee, S.Y., Yoon, E.Y., Kang, N.S., Lim, A.S., Lee, K.H., Jang, S.H., Park, J.Y., Kim, H.S., 2015. Feeding by heterotrophic protists on the toxic dinoflagellate Ostreopsis cf. ovata. Harmful Algae 49, 1–9. Zhang, S., Chan, K.Y.K., Shen, Z., Cheung, S., Landry, M.R., Liu, H., 2017. A cryptic marine ciliate feeds on progametes of Noctiluca scintillans. Protist 168, 1–11. Zhao, L., Xu, G., Wang, Z., Xu, H., 2016. Body-size spectra of biofilm-dwelling protozoa and their seasonal shift in coastal ecosystems. Eur. J. Protistol. 56, 32–40.

Zhong, X., Xu, G., Wang, Y., Xu, H., 2014. An approach to determination of functional species pool for community research. Ecol. Indic. 46, 78–83. Zubkov, M.V., Tarran, G.A., 2008. High bacterivory by the smallest phytoplankton in the North Atlantic Ocean. Nature 455, 224–226. Zuur, A.F., Ieno, E.N., Smith, G.M. (Eds.), 2007. Analysing Ecological Data. Statistics for Biology and Health. Springer, New York.