Influence of inter-annual environmental variability on chrysophyte cyst assemblages: insight from a 2-years sediment trap study in lakes from northern Poland

Abstract

Quantitative paleonvironmental studies using transfer functions are developed from training sets. However, changes in some variables (e.g., climatic) can be difficult to identify from short-term monitoring (e.g., less than one year). Here, we present the study of the chrysophyte cyst assemblages from sediment traps deployed during two consecutive years (November 2011-November 2013) in 14 lakes from Northern Poland. The studied lakes are distributed along a W-E climatological gradient, with very different physical, chemical and morphological characteristics, and land-uses. Field surveys were carried out to recover the sediment trap material during autumn, along with the measurement of several environmental variables (nutrients, major water ions, conductivity, pH, dissolved oxygen and chlorophyll-a). During the study, one year experienced mild seasonal changes in air temperature (November 2011-November 2012; TS1), typical of oceanic climate, while the other year was characterized by colder winter and spring (November 2012-November 2013; TS2), and higher summer temperatures, more characteristic of continental climate. Other environmental variables (e.g., nutrients) did not show great changes between both years. Multivariate statistical analyses (RDA and DCA) were performed on individual TS1 and TS2 datasets. Water chemistry and nutrients (pH, TN and TP) explained the largest portion of the variance of the chrysophyte data for the individual years. However, analyses of the combined TS1 and TS2 datasets show that strong changes between summer and autumn (warm period, ice-free period with thermal stratification) and winter and spring (cold period, ice-cover period) play the most important role in the inter-annual variability in the chrysophyte assemblages. We show how inter-annual sampling maximizes ecological gradients of interest, particularly in regions with large environmental diversity, and low climatic variability. This methodology could help to identify distinct seasonal and inter-annual changes of biological communities to improve its application in paleoclimate studies.