DOI: 10.5061/dryad.h70rxwdnp
title: Macrosystem community change in lake phytoplankton and its implications for diversity and function

description: Aim: We use lake phytoplankton community data to quantify the
 spatio-temporal and scale-dependent impacts of eutrophication, land-use,
 and climate change on species niches and community assembly
 processes while accounting for species traits and phylogenetic
 constraints. Location: Finland Time period: 1977-2017 Major taxa:
 Phytoplankton Methods: We use Hierarchical Modelling of Species
 Communities (HMSC) to model meta-community trajectories at 853
 lakes over four decades of environmental change, including a hierarchical
 spatial structure to account for scale-dependent processes. Using a
 ‘region of common profile’ approach, we evaluate compositional changes of
 species communities and trait profiles and investigate their temporal
 development.  Results: We demonstrate the emergence of novel and
 widespread community composition clusters in previously more compositional
 homogeneous communities, with cluster-specific community trait profiles,
 indicating functional differences. A strong phylogenetic signal of
 species' responses to the environment implies similar responses among
 closely related taxa. Community cluster-specific species prevalence point
 to lower taxonomic dispersion within the current dominant clusters
 compared to the historically dominant cluster and overall higher
 prevalence of smaller species sizes within communities. Our findings
 denote profound spatio-temporal structuring of species co-occurrence
 patterns and highlight functional differences of lake
 phytoplankton communities. Main conclusions: Diverging community
 trajectories have led to a nationwide reshuffling of lake phytoplankton
 communities. At regional and national scales, lakes are not single
 entities but metacommunity hubs in an interconnected waterscape. The
 assembly mechanisms of phytoplankton communities are strongly structured
 by spatio-temporal dynamics, which have led to novel community types but
 only a minor part of this reshuffling could be linked to temporal
 environmental change.
description: The National Finnish Phytoplankton Monitoring Database is maintained by the Finnish
 Environment Institute (SYKE; open data portal http://www.syke.fi/en-US/Open_information) and comprises nationwide phytoplankton community data of lake surface water samples.  We used data collected in the late summer months with samples taken from early July to late August, reflecting the peak productivity season of lake phytoplankton communities. To ensure consistent sampling methodology, we included only data between the years 1977 and 2017. All phytoplankton samples were preserved with acid Lugol’s solution and analysed using the standard Utermöhl technique. As detailed in the manuscript this data supports, we included only species that contributed to the cumulative upper 95% of the total biomass during the four decades of monitoring. This resulted in a total of 165 species, for which we here provide sample-specific presence-absence data. Our study area includes 853 boreal lakes in Finland with 1057 distinct sample locations. All lakes were not monitored each year, with the majority being monitored sporadically over the past four decades. Large lakes were commonly monitored at multiple, spatially distant, sites. The provided data (.Rdata) includes: “S” = Data frame with all relevant information regarding the study design, including units/scales of study (Year, Site, Watershed, Riverbasin) and Site coordinates for random effect structure.  “X" = Data frame with all covariates used as predictors  “Y” = Matrix with species occurrence data at sites “Tr” = Data frame with species-specific trait data  “P” = Phylo list object of type .tre with phylogenetic structuring of all species We also provide an R script for model fitting and post-processing.
description: All analysis was performed in
 R version 3.6.3 (R
 Development Core Team, 2020) and is open-source. The provided script
 includes all necessary steps of opening and structuring the data, as well
 as fitting and post-processing of the models to reproduce the results of
 the manuscript.