DOI: 10.5061/dryad.fxpnvx130
title: Data from biotic variability and synchrony across hierarchical levels and freshwater networks

description: Understanding temporal variability and synchrony across biological
 hierarchical levels (from species to metacommunities) and ecosystems, and
 their underlying drivers, remains a fundamental ecological question.
 However, most studies are performed within an ecosystem, overlooking the
 complexity structuring natural high-level metacommunities across
 ecosystems. By applying theoretical frameworks of metacommunity
 variability with phytoplankton datasets across a pond-stream-lake
 continuum, we show that (i) temporal variability decreases from species to
 metacommunities, while synchrony exhibits complex hierarchical patterns
 and varies depending on spatial scales; (ii) temporal variability and
 synchrony are lower in streams than ponds or the lake, and spatiotemporal
 community-environment relationships are stronger in the lake than the
 ponds or streams. These patterns are strongly related to environmental
 fluctuations, dispersal and species diversity. Our study advances the
 theoretical and empirical understanding of how complex biological
 hierarchies and freshwater networks influence biotic variability and
 synchrony, providing valuable insights for aquatic biodiversity
 conservation.
description: Title: biotic variability and synchrony across hierarchical levels and
 freshwater networks We have submitted our raw data (**The data supporting
 the results of the manuscript.xlsx**). Description of files: The data are
 compiled in an Excel spreadsheet (i.e., The data supporting the results of
 the manuscript.xlsx) with 10 tabs containing the following information: 1.
 Fig.3_data, data supporing for Fig.3; 2. Fig.4_data, data supporing for
 Fig.4; 3. Fig.5ABC_data, data supporing for Fig.5ABC; 4. Fig.5DEF_data,
 data supporing for Fig.5DEF; 5. Fig.5GHI_data, data supporing for
 Fig.5GHI; 6. Fig.5JKL_data, data supporing for Fig.5JKL; 7. Fig.5MNO_data,
 data supporing for Fig.5MNO; 8. Richness_data，data may be interesting for
 audiences 9. Cell density_data，data may be interesting for audiences 10.
 Species list_data，data may be interesting for audiences Variables: Species
 variability: temporal variability in abundance at species level (Note
 overall that abundance is cell density (cells/mL)) Community variability:
 temporal variability in abundance at community level Low-level
 metaommunity variability: temporal variability in abundance within a
 low-level metacommunity High-level metaommunity variability: temporal
 variability in abundance within a high-level metacommunity Species
 synchrony: synchrony of species abundance within a local community
 Community synchrony: spatial synchrony of total community abundance among
 local communities Low-level metaommunity synchrony: spatial synchrony of
 total metacommunity abundance among low-level metacommunities Dataset:
 dataset of phytoplankton samples Freshwater network: freshwater network
 along a pond-stream-lake continuum Environment control: explanatory power
 (%) for environmental factors on spatiotemporal community dissimilarity
 Distance to centroid: environmental distance of sites and times to group
 (a freshwater network) centroid Correlation of environmental fluctuations:
 between-site correlation in temporal environmental fluctuations (temporal
 environmental changes within each of these sampling sites) Shared
 proportions: pairwise shared proportion (%) of phytoplankton species
 between sites Connectance: connectance of species-patch networks
 Simpson's diversity: Phytoplankton Simpson diversity Site_ID: unique
 ID of the sampling site Average richness: average phytoplankton richness
 for each sampling site Average cell density: average phytoplankton cell
 density (cells/mL) for each sampling site Species: identification of the
 phytoplankton at the species level R Code to plot figures 3, 4, 5 and
 calculate variability and synchrony across hierarchical levels: 1. Fig3.R,
 R Code to plot Fig.3; 2. Fig4.R, R Code to plot Fig.4; 3. Fig5.R, R Code
 to plot Fig.5; 4. var_part.R, R Code to calculate variability and
 synchrony across hierarchical levels