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Dryad

Macrosystem community change in lake phytoplankton and its implications for diversity and function

Cite this dataset

Weigel, Benjamin et al. (2022). Macrosystem community change in lake phytoplankton and its implications for diversity and function [Dataset]. Dryad. https://doi.org/10.5061/dryad.h70rxwdnp

Abstract

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.

Methods

The National Finnish Phytoplankton Monitoring Database is maintained by the Finnish Environment Institute (SYKE; open data portal http://www.syke.fi/en-US/Open_informationand 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.

Usage notes

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. 

Funding

Strategic Research Council of the Academy of Finland, Award: 312650 BlueAdapt

Academy of Finland, Award: 309581 (OO)

European Research Council, Award: 856506; ERC-synergy project LIFEPLAN (OO)

Academy of Finland, Award: 311229 (KV)