DOI: 10.5061/dryad.18931zcvt
title: Data from: Submerged macrophytes affect the temporal variability of aquatic ecosystems

description: 1. Submerged macrophytes are important foundation species that can
 strongly influence the structure and functioning of aquatic ecosystems,
 but only little is known about the temporal variation and the timescales
 of these effects (i.e. from hourly, daily, to monthly). 2. Here, we
 conducted an outdoor experiment in replicated mesocosms (1000 L) where we
 manipulated the presence and absence of macrophytes to investigate the
 temporal variability of their ecosystem effects. We measured several
 parameters (chlorophyll-a, phycocyanin, dissolved organic matter [DOM],
 and oxygen) with high-resolution sensors (15 min intervals) over several
 months (94 days from spring to fall), and modelled metabolic rates of each
 replicate ecosystem in a Bayesian framework. We also implemented a simple
 model to explore competitive interactions between phytoplankton and
 macrophytes as a driver of variability in chlorophyll-a. 3. Over the
 entire experiment, macrophytes had a positive effect on mean DOM
 concentration, a negative effect on phytoplankton biomass, and either a
 weak or no effect on mean metabolic rates, DOM composition, and
 conductivity. We also found that macrophytes increased the variance of DOC
 composition and metabolic rates, and, at some times of the observed
 period, increased the variance of phytoplankton biomass and conductivity.
 The observation that macrophytes decreased the mean but increased the
 variance of phytoplankton biomass was consistent with the model that we
 implemented. 4. Our high-resolution time series embedded within a
 manipulative experiment reveal how a foundation species can affect
 ecosystem properties and processes that have characteristically different
 timescales of response to environmental variation. Specifically, our
 results show how macrophytes can affect short-term dynamics of algal
 biomass, while also affecting the seasonal buildup of DOM and the variance
 of ecosystem metabolism.
description: The dataset
 was assembled by means of four autonomous multi parameter instruments
 (EXO2 modular sensor platform [YSI-WTW]). The sondes were placed
 approximately at the center of the mesocosm (~0.5 m depth), away from the
 walls and outside of patches of macrophytes. Additionally, we measured
 photosynthetically active radiation (PAR) in 15 min intervals using a
 quantum sensor (Li-Cor) installed onsite to estimate surface light
 irradiance. PAR and temperature data (Fig. S2) were used together with the
 dissolved oxygen data to calculate metabolic rates. Prior to the
 experiment, we performed a 48h cross-comparison trial where we installed
 all the sondes in a single mesocosm, enabling us to correct for
 differences among sensors and calibrate them against each other. During
 the cross-comparison trial we also quantified chlorophyll-a concentration
 by analyzing water samples with high performance liquid chromatography
 (HPLC, Jasco), and calibrated the optical sensors installed on the sondes
 in accordance with the manufacturer’s manual (YSI-WTW). Hence, we report
 chlorophyll-a as µg * L<sup>-1</sup>, Phycocyanin and fDOM as
 raw fluorescence units.  The oxygen sensors were calibrated against
 water-saturated air. Prior to the
 statistical analysis we removed incomplete days at the beginning and end
 of each measurement period (five time series: t1-t5). After this, each of
 the five time series had 864 data points (15 min interval = 96 data points
 per day = 9 days) for t1-3 and 576 data points (= 6 days) for t4 and t5.
 In a second step, we identified residuals of the detrended data that were
 outside 2.5 times the interquartile range as outliers and removed them
 from the data set. Finally, we used sliding windows with a size of 96
 timepoints (= 1 day) to calculate time series of mean and cv, resulting in
 768 data points for t1-t3 and 480 data points for t4-t5 (8 and 5 days,
 respectively).
description: Here we provide both the corrected/calibrated, full data set and
 the processed data (sliding windows, sliding window summaries, metabolism
 modelling, manual DOC measurements). We also provide a script from which
 the figures can be reproduced, as an entry point to the comprehensive
 dataset. See the readme on how to get started.