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Data from: Phytoplankton functional composition determines limitation by nutrients and grazers across a lake productivity gradient

Citation

Schulhof, Marika; Van de Waal, Dedmer; Declerck, Steven; Shurin, Jonathan (2022), Data from: Phytoplankton functional composition determines limitation by nutrients and grazers across a lake productivity gradient, Dryad, Dataset, https://doi.org/10.5061/dryad.9w0vt4bh4

Abstract

Functional tradeoffs among ecologically important traits govern the diversity of communities and changes in species composition along environmental gradients. A tradeoff between predator defense and resource competitive ability has been invoked as a mechanism that may maintain diversity in lake phytoplankton. Tradeoffs may promote diversity in communities where grazing- and resource-limited taxa coexist, which determines the extent to which communities are resource- or consumer-controlled. In addition, changes in temperature may alter nutrient demands and grazing pressure, changing the balance between the two regulating factors. Our study aims to understand whether a tradeoff between grazer vulnerability and nutrient limitation promotes coexistence of phytoplankton functional groups in communities that differ in trophic status, and how this tradeoff may shift with warming. We conducted multifactorial experiments manipulating grazing, nutrients, and temperature in phytoplankton communities from three Dutch lakes varying in trophic status, and used a trait-based approach to classify functional groups based on grazing susceptibility. We found no associations between susceptibility to grazing and response to nutrient additions in any of the communities or temperature regimes, indicating that a competition-defense tradeoff is unlikely to explain diversity within the tested communities. Instead, we observed a tendency towards both a higher grazing-resistance and weaker nutrient limitation along with a shift in the functional composition of phytoplankton in communities across a gradient from low to high productivity.

Methods

We conducted multifactorial microcosm experiments on phytoplankton communities from three Dutch lakes and manipulated temperature, grazing pressure, and nutrient load. We tested the effects of nutrient addition and grazing at ambient and warmed temperatures on distinct phytoplankton functional groups using a trait-based framework (from Kruk et al. 2010). To test the generality of our results, we chose to work on lakes differing widely in productivity: phytoplankton communities collected from lakes Maarsseveen, Loosdrecht, and Tjeukemeer are referred to as the “low”, “medium” and “high” productivity communities, respectively.

Using a fully factorial design, the culture containers were subjected to two temperature, nutrient, and grazing treatments, for a total of eight factorial treatment combinations (see methods in accompanying publication for details). Each of the eight treatments was replicated four times, resulting in thirty-two experimental units for each of three experiments. Each experiment ran for a duration of 6 days, when phytoplankton communities were harvested. Samples from each culture vessel were collected and three of four replicates for each treatment were analyzed using microscopy. Phytoplankton communities were counted via microscopy using the Utermöhl method (Utermöhl 1958; see methods in accompanying publication for further details)

Cell dimensions were measured from microscope images and biovolumes were calculated based on cell geometry according to Hillebrand (1999). For taxa that exhibited a constant size range across all treatments, mean measurements taken across all treatments were used to calculate biovolumes. However, for cells that varied in their length (i.e. chainforming diatoms), treatment-specific means were calculated from length measurements taken in each treatment. Additionally, for filamentous cyanobacterial cells that were counted via images for lakes Loosdrecht and Tjeukemeer, cell dimensions were measured on all counted cells in each image such that the total biovolume was calculated for each image. Biovolumes were then normalized by sample volume (µm3/mL).

After all cell biovolumes were calculated and normalized by sample volume (µm3/mL), they were binned into seven morphology-based functional groups developed by Kruk et al. (2010). The seven morphology-based functional groups are as follows: small organisms with high surface area to volume ratio (FG I; high grazing susceptibility), small flagellated organisms with siliceous exoskeletons (FG II; low grazing susceptibility), large filaments with aerotopes (FG III; low grazing susceptibility), organisms of medium size lacking specialized traits (FG IV; high grazing susceptibility), unicellular flagellates of medium to large size (FG V; medium grazing susceptibility), non-flagellated organisms with siliceous exoskeletons (FG VI; medium grazing susceptibility), and large mucilaginous colonies (FG VII; low grazing susceptibility) (Kruk et al., 2010, Colina et al. 2016).

Usage Notes

This dataset contains phytoplankton functional group (indicated in column 'functional.group') biovolume data (µm3/mL; indicated in column 'biovolume') for three out of four replicates for each experimental treatment (indicated in column 'treatment'). In the 'treatment' column, each multifactorial treatment combination is indicated by elevated ('+') or ambient ('0') levels of temperature ('T'), nutrients ('N') and zooplankton grazers ('Z'). For example, a treatment labeled 'T0N+Z0' indicates ambient temperature level, elevated nutrient level, and ambient grazer level. Data are included for each phytoplankton community (low, medium and high productivity communities; indicated by column 'productivity').

Funding

National Science Foundation, Award: DEB 1457737

National Science Foundation, Award: Graduate Research Fellowship

National Science Foundation, Award: Graduate Research Opportunities Worldwide (GROW) Fellowship

Royal Netherlands Academy of Arts and Sciences, Award: Ecology Fund