DOI: 10.5061/dryad.9w0vt4bh4
title: Data from: Phytoplankton functional composition determines limitation by nutrients and grazers across a lake productivity gradient

description: 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.
description: 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
 (µm<sup>3</sup>/mL). After all cell
 biovolumes were calculated and normalized by sample volume
 (µm<sup>3</sup>/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).
description: This dataset contains phytoplankton functional group (indicated
 in column 'functional.group') biovolume data
 (µm<sup>3</sup>/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').