“Quando eu disse ao caroço da laranja que dentro dele dormia um laranjal inteirinho, ele me olhou estupidamente incrédulo.” Hermógenes
73 Relative importance of intraspecific variation and species sorting selective pressures on phytoplankton size variation in tropical lakes
Letícia Barbosa Quesado1,2, Lynn Govaert2, Caroline Souffreau2, Luc De Meester2, Camila Cabral, Fabíola da Costa Catombé Dantas, André Megali Amado3,Adriano Caliman1, Luciana Silva Carneiro1
Addresses:
1Universidade Federal do Rio Grande do Norte (UFRN) – Lab. of Aquatic Ecology, Natal – Brasil. ZipCode: 59.072-970
2KU Leuven, Laboratory of Aquatic Ecology, Evolution and Conservation, Leuven – Belgium. B-3000
3Universidade Federal do Rio Grande do Norte (UFRN) – Lab. of Limnology, Natal– Brazil. ZipCode: 59.014-002
74 Abstract
Trait-based approaches are increasingly used in community ecology to explain and predict functional community structure and dynamics in response to environmental gradients. The trait variation over an environmental gradient is usually assigned to species turnover (SPT), however intraspecific trait variation (ITV) have shown contributions greater than expected. We hypothesized that for phytoplankton the increase in nutrient concentrations will increase total abundance with a predomination of large filamentous and mucilaginous algae; while the increase in predator biomass will reduce community abundance and promote predominance of protected algae; and that the variation of phytoplankton size will be driven by interspecific trait variation independent on the gradient (abiotic or biotic) considered. For the latter, we performed an analysis of variation partitioning for continuous gradients, where SPT was the mean trait values of each species-weighted by abundance and ITV was the difference between the trait values of each species locally weighted and SPT. Phytoplankton community of 98 freshwater environments was sampled along environmental conditions and herbivore pressure gradients in the northeast of Brazil. Overall, we found that there was an increase in average phytoplankton community body size with increasing Cyclopoida biomass. Functional group abundance and size presented distinct patterns along an eutrophication gradient, while this gradient affected unicellular and siliceous algae in abundance, changed the size of mucilaginous and filamentous algae. Species turnover was predominant as the pattern of size variation. The prevalence of SPT was found for every functional group, except for the non-toxic unicellular algae with hard cell walls and the toxic filamentous algae. For these groups, ITV was more important along predator (Calanoida) biomass and eutrophication gradient, respectively. This study highlights that the ecological drivers acting on phytoplankton size variation among communities are different for intra- and interspecific components and that species sorting has a strong influence on determining phytoplankton size responses to broad gradients.
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Keywords: Community-weighted traits, functional traits, species turnover, microorganisms,
76 Introduction
Recently, the use of trait-based approaches in community ecology has increased due to their potential to explain and predict functional community structure and dynamics in response to environmental gradients (Lavorel et al. 2008; Litchman & Klausmeier 2008). Traits can be any characteristic that describes morphology, biochemistry, physiology, structure, phenology or behavior of an organism that influences its performance or fitness (Violle et al. 2007). In this way, traits connect individual organisms to ecosystem properties and services. For example, in plants, leaf mass per area (LMA) is a trait known to affect nutrient cycling, as leaves with more mass per unit area decompose slower, therefore reducing nutrient cycling in a mixture of species with high LMA compared to a mixture with low LMA (Nock et al. 2016). Therefore, LMA is classified to be an effect trait, as it impacts some ecosystem property or service (e.g., nutrient cycling, toxin production, primary productivity; Nock et al. 2016). Some traits, on the other hand, are classified as response traits, because they influence the species’ ability to colonize and persist through the environmental change (e.g., ecological tolerances and sensitivities; Díaz et al. 2013; Nock et al. 2016). Other traits can be both, such as body size (Litchman & Klausmeier 2008). Body size can be a response trait when affected by predation pressure, promoting the increase in size as an avoidance mechanism, or as an effect trait when causing alterations (Nock et al. 2016) e.g. the sinking rate of large plankton and fecal pellets, which can impact biogeochemical cycling (Jiang et al. 2005; Naselli-Flores et al. 2007; Nock et al. 2016). Among all traits of an organism, body size is often considered a ‘master trait’ as it impacts multiple biological aspects, as reproductive and core metabolic rates, abundance, resource acquisition, and others (Peters 1983; Naselli-Flores et al. 2007; Velghe & Gregory-Eaves 2013).
Commonly, traits are measured at the species level, describing community trait distributions using species mean trait values (e.g. Kraft et al. 2008), this under the assumption that interspecific trait variation is larger than intraspecific variation (McGill et al. 2006). Recently, however, one has realized that trait values within populations might vary a lot among
77 sites and thus that intraspecific trait variation (ITV) should be considered in community analyses (Violle et al. 2012). Studies have shown that ITV in response to environmental factors can be greater than expected (Cianciaruso et al. 2009) and can mediate species interactions such as coexistence and, consequently, affect community structure and ecosystem functioning (Bolnick et al. 2003). When assessing traits at the within species-level at local sites, one can calculate the relative contribution of ITV and species turnover (i.e., changes in the species composition; SPT) among sites over the chosen environmental gradient. If ITV is the main variation along the gradient, it expresses the importance of local responses of species to the environmental changes and its trait range (Violle et al. 2007), either through plasticity (fast response) or evolution (genetic response) (Reed et al. 2010). If, however, the variation is mostly due to SPT, this reflects the need of dispersal, which will allow species to spatially track their environmental optimum to maintain trait–environment matching under environmental change (Parmesan 2006). Studies in this research field with microorganisms are rare, despite their tremendous functional importance in ecosystems. In this study, we focus on phytoplankton which is an extremely diverse and polyphyletic group (Reynolds 2006), considered an essential component of several ecological processes and services, such as primary productivity, the biogeochemical cycle of many elements, and can drastically affect higher trophic levels (Sterner & Elser 2002; Falkowski et al. 2004; Huisman et al. 2005). Due to its enormous diversity, phytoplankton is often subdivided into functional groups such as related to nutrient levels and turbulence gradients (Margalef 1978), or to ecological tolerance and sensitiveness (Reynolds 1984; Reynolds 2006; Reynolds et al. 2002; Padisak et al. 2009), or even considering only morphological traits (i.e., volume, presence of flagella) that can be related to physiology (e.g., size and growth rates) (Kruk et al. 2010). Most of the functional ecology studies of freshwater phytoplankton focused on responses of groups of similar species, rather than analyzing their traits. Recently, the trait-based approaches have increased in phytoplankton community studies, exploring patterns in eco-
78 physiological and morphological traits (e.g., Litchman et al. 2003; 2009; 2015; Schwaderer et al. 2011; Edwards et al. 2012; Kruk et al. 2015; Thomas et al. 2016).
Almost all trait-based studies on phytoplankton incorporate organism size, and studies that define phytoplankton functional groups use size as a division criterion (e.g., Reynolds 2002; Salmaso & Padisak 2007; Kruk et al. 2010). Phytoplankton size is a highly diverse trait and can vary intra- and interspecifically and over several orders of magnitude, from 1 µm for unicellular individuals to more than 1 mm for colonial and filamentous organisms (Naselli-Flores et al. 2007; Litchman & Klausmeier 2008; Litchman et al. 2009). Such diversity in size variation suggests that different selective pressures act on the phytoplankton community selecting distinct sizes (Litchman et al. 2009). Indeed, an increase in phytoplankton size has been found in high nutrient concentrations (Acevedo-Trejo et al. 2013). In Brazil, for example, a dominance is observed for cyanobacteria species with large size and toxins production under high eutrophication (Soares et al. 2013), mainly filamentous or mucilaginous taxa. A similar increase in size has been found along increasing predation pressure (Kâ et al. 2012), resulting in a decrease in total biovolume and selection of predation-resistant species such as cyanobacteria (Kâ et al. 2012).
In this context, disentangling the variation in organism size into intra- and interspecific components can help us understand which process is explaining the general pattern of the trait- environment relationship. We tested the relative importance of within and among species variation in total phytoplankton size variation along eutrophication and predation gradients in Northern Brazil for a set of 98 communities. In this, we expect that the increase in total phytoplankton size can be due to increase in size itself and increase in abundance. Therefore, an increase in nutrient concentrations can increase the total phytoplankton size variation with the predominance in abundance of large filamentous and mucilaginous algae. While the increase in predator biomass will reduce total abundance, however, the community will increase in size due to protected algae. We expect that the variation of phytoplankton size will be driven mainly by
79 interspecific size variation independent of the gradient (abiotic or biotic) investigated. To test these, we specifically assessed (1) whether and how phytoplankton community size variation can be explained by the environmental conditions and predator’s biomass; (2) how these patterns differ among functional groups, and (3) how variations within and among species or within functional groups contribute to the observed changes in size along different gradients.
Materials and methods
Study area
The field survey was carried out in 98 freshwater ecosystems (Fig. 1), 50 of them were natural lakes, and 48 were artificial, man-made reservoirs located in the Rio Grande do Norte state (Brazil). The state has a precipitation gradient ranging from the humid coast in the East towards the semi-arid land in the West. The total extent of the sampling area covered 33,562 km2.
80 Figure 1 – Map of the 98 freshwater phytoplankton communities sampled in Rio Grande do Norte, Brazil, in September 2012. Pies represent the relative abundance of the seven functional groups in each community and in the whole metacommunity. The subtitle presents the functional groups in order of contribution, from the most to the least abundant one.
Sampling and sample analysis
We sampled 98 freshwater ecosystems during the dry season (INPE 2015) in September 2012, a year marked by a severe drought (EMPARN 2015). For each ecosystem, we measured in the field: light availability (cm, Secchi disc used as a proxy, Megard et al. 1980) and pH (benchtop pH meter – MS Tecnopon®/mPA 210) in the limnetic region. Water samples were collected on the subsurface of the water column for characterization of phytoplankton community and concentration of total phosphorus (TP) and total nitrogen (TN). Zooplankton was sampled from the subsurface water column (30 to 100 L depending on the turbidity of each environment) using a zooplankton net (mesh size 50 μm). Phytoplankton samples (100 mL) were preserved with lugol solution (0.2%, final concentration) and zooplankton samples were preserved with sugar formaldehyde solution (4%, final concentration). In the laboratory, TN was determined by a Total Organic Carbon and Nitrogen analyzer (TOC analyzer with the VPN module; Shimadzu). TP was determined after oxidation of most phosphate compounds with persulfate (Valderrama 1981) and estimated by measuring reactive soluble phosphorus (Murphy & Riley 1962). For more variables measured, but not used here due to the variables selection analysis, see Appendix A.
Phytoplankton was counted and identified in random fields using an inverted microscope (Utermöhl 1958; Uhelinger 1964). Thresholds for counting species were based on two criteria: up to 100 individuals (i.e., cells, colonies or filaments) of the most abundant species (Lund et al. 1958) and until saturation of the species accumulation curve. Species were classified using the major taxonomic schemes from the Integrated Taxonomic Information System (itis.gov), Tree of Life database (tolweb.org) and Adl et al. (2012), except for Cyanobacteria (Komárek et al. 2014). To assess local average species size, 30 (or all if <30) random individuals were measured (maximum linear dimension of individual cells, colonies or filaments) for each species present in a sample. We combine size with different traits which offer an advantage against predators, like the presence of chemical (potential toxicity) and mechanical (coloniality, filamentous form, mucilage production,
82 and cell wall protection) defenses. These classification criteria led to the following groups: toxic filamentous, non-toxic filamentous, toxic mucilaginous, non-toxic mucilaginous, non-toxic naked colonies, non-toxic unicellular with hard cell walls and unicellular species. A list of the functional groups with the assigned species is given in Appendix B. These combinations of traits can help to understand their relation and variation along environmental and predator gradients, as size already related to environmental condition gradient when small-size algae have a bigger surface-volume ratio and are good on up taking nutrients from the surrounding environment (Kruk et al. 2010). Community data were expressed in species abundances.
Zooplankton (Rotifera, Copepoda, and Cladocera) were identified to the species level using a Sedgewick-Rafter camera and microscope for Rotifera, and a Bogorov camera and stereo microscope for Copepoda (Calanoida, Cyclopoida, and each respective copepodites) and Cladocera. Identification and counting were performed on three replicates per lake with at least 100 individuals of the most abundant organisms in each. The samples have been fully evaluated to search for rare species. To calculate Cladocera, Calanoida and Cyclopoida biomass (mg/mL), we first determined the individual species weight by measuring 30 random individuals and used these body sizes in allometric equations (Hall et al. 1970; Bottrell et al. 1976; Wetzel & Likens 1991). Then, we multiplied the result by its density and summed this over the species within each group. To determine Rotifera biomass (mg/mL), we used literature data on average individual weight for each species (Bottrell et al. 1976; Pauli 1989; Elmoor-Loureiro 1997; Silva & Matsumura-Tundisi 2005; Debastiani Jr et al. 2009), and multiplied this by each species density followed by summing over the species.
83 From a set of 43 environmental variables (details in Appendix A), we first tested this set of variables for high correlation values (r Pearson > 0.6) among them, and then, we selected the ones which best explained the community trait variation based on Akaike’s Information Criterion (AIC – Johnson & Omland 2004). The selected ones were related to resource use (TN, TP, and light availability), chemical conditions (pH), and biological interactions (biomass of Cladocera, Cyclopoida, Calanoida, and Rotifera). The biological interactions data were log10-transformed beforehand, while with the abiotic variables we performed a PCA to summarized them. The first PCA axis (52.9%) represented a eutrophication gradient, where positive values were marked by the increase in productivity (pH, TN, and TP), and the decrease in light availability and lake depth (see Figure S1 in Appendix C).
Trait-environment relationships among communities
From a set of 43 environmental variables (details in Appendix A), we first tested this set of variables for high correlation values (r Pearson > 0.6) among them, and then, we selected the ones which best explained the community trait variation based on Akaike’s Information Criterion (AIC – Johnson & Omland 2004). The selected ones were related to resource use (TN, TP, and light availability), chemical conditions (pH), and biological interactions (biomass of Cladocera, Cyclopoida, and Calanoida, and Rotifera). All environmental variables were log10-transformed beforehand (except pH) and summarized in a PCA. The first PCA axis (52.9%) represented a eutrophication gradient, where positive values were marked by the increase in productivity (pH, TN, and TP), and the decrease in light availability and lake depth (see Figure S1 in Appendix C).
Trait-environment relationships were determined at two hierarchical levels of community organization, total community and within individual functional groups. For each hierarchical level, we tested if the variation in phytoplankton CWM size could be explained by one (or more) of the
84 five gradients (eutrophication gradient, biomass of Cladocera, Cyclopoida, Calanoida, and Rotifera). To better understand if and how the trait-environment relationship differed among functional groups and to the total phytoplankton community pattern, we also evaluated the relative abundances of the functional groups, the species-weighted functional group mean size and the abundance-weighted functional group mean size. The latter being the product of the relative abundance of the functional group and the species-weighted functional group mean.
The relative importance of intra- and interspecific trait variation to community size variation
We used the variation partitioning method proposed by Lepš et al. (2011), modified by Kichenin et al. (2013) for continuous gradients, to quantify the total trait variation in community-weighted mean (CWM) size and the contribution of intra- (ITV) and interspecific trait variation (or species turnover – SPT). Similar as with the trait-environment relationships, the relative contribution of trait variability was determined at two hierarchical levels of total community and within individual functional groups. For each hierarchical level, we quantified the contribution of ITV and SPT.
Quantification of ITV and SPT consists of three steps. First, three types of community- weighted means (CWMs) were calculated for each hierarchical level following Lajoie & Vellend (2015): CWMSPT+ITV (using local species mean trait values and abundances), CWMSPT (using species mean trait values of the total dataset and local abundances) and CWMITV (calculated as the difference between CWMSPT+ITV and CWMSPT). For the functional group's calculations, the relative abundances of the species were rescaled to 100% within each group previously. Similarly, three types of CWMs were calculated within each functional group. Second, regression analyses were performed for all three CWM-environment relationships independently, using a linear regression model. We also tested for quadratic relationships, using AIC to perform model selection followed by an F test for each CWM-environment relationship. To meet the assumption of normality for the
85 regression analysis, individual sizes were cube root-transformed beforehand. Cook’s distances and residual plots were used to visually detect outliers in each regression model. Third, the absolute explanatory power of ITV and SPT to the total variation in community size was calculated as the ratio between the regression sum of squares of the ITV or SPT model (SSRITV and SSRSPT) and the total sum of squares of the model including both components (SSRSPT+ITV). The relative importance of ITV to SPT was calculated as SSITV/(SSITV+SSSPT).
To assess the variation explained by each gradient to the three CWMs at total community and functional group level, independently, we performed a multiple regression analysis including all gradients. For the multiple regression analysis, we first fitted a full model with all five gradients (linear terms) included. Next, a stepwise selection procedure using AIC was used to assess which gradients were more important for each CWM-type and each functional group. All selected variables in any of the three models (SPT+ITV, ITV, and SPT) were kept in the final set per hierarchical level and functional group. For each CWM, the contribution of a given gradient was calculated as the standardized regression coefficient in the reduced model multiplied by the simple correlation coefficient of that gradient with the corresponding CWM (Borcard 2002). Individual contributions of each gradient summed up to the explained variation (R²) of the reduced model (Borcard 2002). For all analyses, species with one occurrence in the metacommunity were excluded. All analyses were performed in the statistical software R (R Development Core Team 2017) using the packages vegan (Oksanen et al. 2017) and lm.beta (Behrendt 2015).
Results
General characteristics of the phytoplankton communities and functional groups
We recorded a total of 158 species across all 98 phytoplankton communities belonging to seven functional groups. The average community-mean size was 13.92 µm with a standard deviation of
86 1.08 (Fig. S2A in Appendix D). This value was likely due to the high abundance of unicellular algae in the communities. We found unicellular algae to be the most dominant group in the metacommunity (relative abundance of 65.70% in the metacommunity, ranging from 16.52% to 99.85% in individual communities (Fig. 1), consisting of 75 unicellular algae species. The second most dominant group was the potentially toxic filamentous algae which consisted of six species and still accounted for 22.30% of the relative abundance in the metacommunity, varying from 0.19% to 72.85% in individual communities (Fig. 1). Non-surprisingly, filamentous algae had the largest unweighted average size (toxic: 39.26 µm and non-toxic: 42.83 µm) with the highest variation in its filament size values, varying from around 5.75 to 267.5 µm. Although the relative abundances of the functional groups within communities varied broadly among the 98 communities, the contribution of the remaining functional groups to the metacommunity was low. Non-toxic filamentous algae (7 species) contributed with 5.11%, non-toxic mucilaginous with 2.64%, non-toxic unicellular with hard cell walls 2.34%, non-toxic naked colonies with 1.73%, and toxic mucilaginous algae (3 species) only added 0.17% to the total metacommunity (Fig. 1 and Appendix B).
Community and functional group patterns in mean size along environmental gradients
Figure 2 shows the community and functional group responses of mean size along the eutrophication gradient and the predator biomass gradients (i.e., Rotifera, Cyclopoida, Calanoida, and Cladocera biomass). For the total community, we observed only a significant increase in CWM