Predation-driven dynamics of zooplankton and phytoplankton communities in a whole-lake experiment

Seasonal diversity dynamics of a boreal zooplankton community under climate impact

Seasonality and long-term environmental variability affect species diversity through their effects on the dynamics of species. To investigate such effects, we fitted a dynamic and heterogeneous species abundance model generating the lognormal species abundance distribution to an assemblage of freshwater zooplankton sampled five times a year (June-October) during the ice-free period over 28 years (1990-2017) in Lake Atnsjøen (Norway). By applying a multivariate stochastic community dynamics model for describing the fluctuations in abundances, we show that the community dynamics was driven by environmental variability in spring (i.e., June). In contrast, community-level ecological heterogeneity is highest in autumn. The autumn months (i.e., September and October) that rearranged the community are most likely crucial months to monitor long-term changes in community structure. Indeed, noises from early summer are filtered away, making it easier to track long-term changes. The community returned faster towards equilibrium when ecological heterogeneity was the highest (i.e., in September and October). This occurred because of stronger density-regulation in months with highest ecological heterogeneity. The community responded to the long-term warming of water temperature with decreasing species diversity and increasing abundance. Unevenness associated with variabilities in abundances might affect species interactions within the community. These can have consequences for the stability and functioning of the ecosystem.

Changes of the phytoplankton community as symptoms of deterioration of water quality in a shallow lake

Covering more than 60% of the lake surface, macrophytes determined the taxonomic composition of phytoplankton. We have found numerous indications of ecological deterioration and an increased trophic level year to year: an increased total number of taxa; a significantly increased number of species of Chlorophyta, Bacillariophyceae and Cyanoprokaryota; a decreased number of Chrysophyceae; increased Nygaard index, and high diversity and variability of phytoplankton functional groups. Within 2 years (2002 and 2003) algal biomass doubled: from 3.616 to 7.968 mg l^-1. An increased contribution of Chlorococcales and Cyanoprokaryota indicates progressive eutrophication of the lake. The average size of planktonic algae increased, particularly Cyanoprokaryota, where small-celled decreased dramatically and were replaced by large colonies. Cyanoprokaryota remained the dominant group of phytoplankton after 10 years, and the ecosystem of the lake remained in the turbid state. This group of algae had the average biomass 9.734 mg l^-1, which constituted almost 92% of the total biomass.

Cohesion: a method for quantifying the connectivity of microbial communities

The ability to predict microbial community dynamics lags behind the quantity of data available in these systems. Most predictive models use only environmental parameters, although a long history of ecological literature suggests that community complexity should also be an informative parameter. Thus, we hypothesize that incorporating information about a community's complexity might improve predictive power in microbial models. Here, we present a new metric, called community 'cohesion,' that quantifies the degree of connectivity of a microbial community. We analyze six long-term (10+ years) microbial data sets using the cohesion metrics and validate our approach using data sets where absolute abundances of taxa are available. As a case study of our metrics' utility, we show that community cohesion is a strong predictor of Bray-Curtis dissimilarity (R²=0.47) between phytoplankton communities in Lake Mendota, WI, USA. Our cohesion metrics outperform a model built using all available environmental data collected during a long-term sampling program. The result that cohesion corresponds strongly to Bray-Curtis dissimilarity is consistent across the six long-term time series, including five phytoplankton data sets and one bacterial 16S rRNA gene sequencing data set. We explain here the calculation of our cohesion metrics and their potential uses in microbial ecology.