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A trait‐based approach predicting community assembly and dominance of microbial invasive species

Citation

Kruk, Carla et al. (2021), A trait‐based approach predicting community assembly and dominance of microbial invasive species, Dryad, Dataset, https://doi.org/10.5061/dryad.8cz8w9gpf

Abstract

Understanding the mechanisms underlying community assembly helps to define success and susceptibility to biological invasions. A methodological approach to this aim is to use trait-based approaches. Under the hypothesis that the morphology-based functional groups (MBFG) clusters species with similar niche we analyse how trait-related differences in fitness influence the outcome of invasion. The invasive dinoflagellate Ceratium furcoides (CF) can be used as the model species considering its morphological (e.g. volume) and physiological traits (e.g. growth rates) comparing with species from the same (MBFG V) and different (MBFG VII: colonial cyanobacteria) MBFG. Here we present the information needed to apply this approach with similar or different questions including information from two aquatic environments from South America: the first one located in Argentina (Miní flood-plain Lake) and the second one in Uruguay (Salto Grande Reservoir). Phytoplankton morphological traits measured from field samples, along with richness, abundance, biovolume and environmental variables are presented. Phytoplankton individuals and species are classified in terms of MBFG and focus on the invasive species Ceratium furcoides. The literature derived information includes growth rates with temperature for phytoplankton species classified in MBFG V including Ceratium furcoides.

Methods

Study area and sampling design

Two contrasting ecosystems are considered, a large human-made reservoir (Salto Grande) and a natural floodplain lake (Miní Lake) (Figure 1: location of sampling sites in both ecosystems). Salto Grande is a eutrophic freshwater reservoir with low flushing rate and an area of 783 ha (Martínez de la Escalera et al. 2017, Kruk et al. 2017) where samples were taken bimonthly in six occasions (January 2013 - March 2014), including littoral and open water samples (n = 12). The Miní Lake is located within the Paraná River floodplain and is permanently connected to the river, modulated by hydrological and sedimentological pulses of the fluvial system (Mayora et al. 2013). In Miní Lake, samplings were carried out approximately fortnightly from November 2009 to December 2010, and from January to December 2012 (n = 38).

At each sampling occasion, temperature (°C) and depth (m) were measured. Qualitative samples for phytoplankton analysis were taken with 25 μm mesh-size plankton net and fixed with carbonate formaldehyde 4%. Phytoplankton samples for counting were taken with Niskin bottles and fixed in acid lugol solution (1%). Zooplankton samples in Salto Grande Reservoir were obtained by filtering 10 to 20 litres of water with a 50 µm mesh and were fixed with acid lugol solution (1%). Counting was done following Paggi and José de Paggi (1974) in Sedgewick-Rafter chambers. Zooplankton species were classified into rotifers, cladocerans and copepods.

Phytoplankton abundance (organisms mL-1) was counted using settling chambers (Utermöhl 1958), at several magnifications (10 to 1000×) until reaching at least 100 organisms of the most frequent species (Lund et al. 1958). Organism dimensions, including maximum lineal dimension (MLD, μm) were measured. Individual volume (V, μm3) and surface area (S, μm-1) were calculated according to Hillebrand et al. (1999). The presence of flagella, aerotopes and mucilage was noted for each organism. Based on their individual morphological traits each organism was classified into one of seven morphology-based functional groups (MBFG: I-VII) (Kruk et al. 2010, Kruk and Segura 2012).

Biovolume (mm3 L-1) was used as a metric of biomass and was calculated by multiplying each organism individual volume by its abundance, all organisms of the same sample were combined to calculate total phytoplankton biovolume and the same was performed for each of the MBFG. Species richness was estimated in each sample after identifying all organisms to the species level, when possible.

Literature review for temperature response growth

Information from controlled experiments designed to quantify population growth rates of C. furcoides (n = 13, Butterwick et al. 2005) and MBFG V representatives as a function of temperature (n = 89) as well as for MBFG VII (Kruk et al. 2017). Only data from single-species cultures with unlimited nutrients and saturating light conditions were considered.

References

Butterwick, C. et al. 2005. Diversity in the influence of temperature on the growth rates of freshwater algae, and its ecological relevance. – Freshwater Biol. 50: 291–300. doi 10.1111/j.1365-2427.2004.01317.x

Hillebrand, H. et al. 1999. Biovolume calculation for pelagic and benthic microalgae. – J. Phycol. 35: 403–424. 10.1046/j.1529-8817.1999.3520403.x

Kruk, C. and Segura, A.M. 2012. The habitat template of phytoplankton morphology-based functional groups. Hydrobiologia 698: 191–202.

Kruk, C. et al. 2010. A morphological classification capturing functional variation in phytoplankton. Freshwater Biol. 55: 614–627.

Kruk, C. et al. 2017. A multilevel trait-based approach to the ecological performance of Microcystis aeruginosa complex from headwaters to the ocean. – Harmful Algae 70: 23–36. doi: 10.1016/j.hal.

Lund, J.W.G. et al. 1958. The inverted microscope method of estimating algal numbers and the statistical basis of estimations by counting. Hydrobiologia 11: 143–170.

Martínez de la Escalera, G. et al. 2017. Dynamics of toxic genotypes of Microcystis aeruginosa complex (MAC) through a wide freshwater to marine environmental gradient. – Harmful Algae 62: 73–83. doi: 10.1016/j.hal.2016.11.012

Mayora, G. et al. 2013. Spatial variability of chlorophyll-a and abiotic variables in a river–floodplain system during different hydrological phases. – Hydrobiologia 717: 51–63. doi: 10.1007/s10750-013-1566-x

Paggi, J. C. and José de Paggi, S. J. 1974. Primeros estudios sobre el zooplancton de las aguas lóticas del Paraná medio. – Physis 33: 91–114. DOI: 10.14409/fabicib.v18i0.4853

Utermöhl, H. 1958. Zur Vervollkommnung der quantitativen Phytoplankton-Methodik: Mit 1 Tabelle und 15 abbildungen im Text und auf 1 Tafel. – Int. Ver. Theor. 9: 1–38.

Usage Notes

Table 1 contains phytoplankton total biovolume (μm3ml-1), Ceratium furcoides biovolume and species richness (S) in different sampling dates of Miní Lake (Argentina). The same variables are included in for Salto Grande reservoir (Uruguay) in Table 2.

Occurrence and value of phytoplanktonic individual volume (μm3) in different sampling dates of Miní Lake (Argentina) are included in Table 3 along with environmental variables. Here the individual volume was estimated as an average among all sites for all species except for Ceratium furcoides that was measured in each sampling occasion. Similar information with greater detail is included for the Salto Grande reservoir in Table 4. For this ecosystem all organisms were measured in all samples and morphological traits are calculated at the level of the organisms in each sample (V, S and MLD). Therefore, biovolume was also estimated individually by multiplying the individual volume by the individual abundance in the sample estimated from counts (please refer to the methods section). Table 4 also includes environmental drivers in different sampling dates and sites of Salto Grande reservoir including depth (z, m), temperature (T, oC), flushing (f, d-1) and the abundance of zooplankton is clustered in rotifers, cladocera and copepods abundance.

Finally, in Table 5 we included the growth rates with temperature for phytoplankton species from morphology-based functional group (MBFG) V including Ceratium furcoides (CF). The taxonomic group and references are also included.

Funding

Comisión Sectorial de Investigación y Ciencia (Uruguay), Award: CSIC I+D 2016-197

Programa de Medio Ambiente - LATU-ANII (Modalidad I: Proyectos de Investigación Aplicada, Uruguay)

Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) and Secretaría de Estado de Ciencia, Tecnología e Innovación (Santa Fe), Argentina, Award: SECTeI 2011