An Automated Platform for Phytoplankton Ecology and Aquatic Ecosystem Monitoring

Abstract

High quality monitoring data are vital for tracking and understanding the causes of ecosystem change. We present a potentially powerful approach for phytoplankton and aquatic ecosystem monitoring, based on integration of scanning flow-cytometry for the characterization and counting of algal cells with multiparametric vertical water profiling. This approach affords high-frequency data on phytoplankton abundance, functional traits and diversity, coupled with the characterization of environmental conditions for growth over the vertical structure of a deep water body. Data from a pilot study revealed effects of an environmental disturbance event on the phytoplankton community in Lake Lugano (Switzerland), characterized by a reduction in cytometry-based functional diversity and by a period of cyanobacterial dominance. These changes were missed by traditional limnological methods, employed in parallel to high-frequency monitoring. Modeling of phytoplankton functional diversity revealed the importance of integrated spatiotemporal data, including circadian time-lags and variability over the water column, to understand the drivers of diversity and dynamic processes. The approach described represents progress toward an automated and trait-based analysis of phytoplankton natural communities. Streamlining of high-frequency measurements may represent a resource for understanding, modeling and managing aquatic ecosystems under impact of environmental change, yielding insight into processes governing phytoplankton community resistance and resilience.

Assessing triclosan-induced ecological and trans-generational effects in natural phytoplankton communities: a trait-based field method

“…Flow-cytometry has been used before to test responses of individual cells to common pollutants under natural environmental conditions (Czechowska and van der Meer 2011). Scanning flow-cytometry offers the extra opportunity of measuring multiple individually expressed morphological and pigment related traits in phytoplankton with high precision (Collier 2000) and over a large number of individuals per population in natural communities (Pomati et al 2011. Data obtained by scanning-flow cytometry allow to measure and group individuals based on their expressed phenotype, which is the results of acclimation and selection processes, and to derive population and community quantitative variables that link directly to individual responses (Fig.…”

“…Examples of key traits include cell size, shape and coloniality (which influence sensitivity to toxicants and nutrient uptake through surface/volume ratio), and photosynthetic pigment type and concentration (which relates to photosynthetic performance and acclimation) (Litchman and Klausmeier 2008). Recently, scanning-flow cytometry has been used to derive quantitative phytoplankton traits and group individual phytoplankton particles into morphofunctional categories, whose dynamics and trait-environment relationships reflect the structural and functional changes in natural phytoplankton communities under natural dynamics (Pomati et al 2011.…”

“…We used the scanning flow-cytometer Cytobuoy (Cytobuoy.com, Woerden, The Netherlands) for analysis, counting and characterisation of phytoplankton (Dubelaar et al 2004;Malkassian et al 2011;Pomati et al 2011). This instrument is designed to analyse the naturally occurring size range from small (e.g.…”

“…Particles were intercepted by two laser beams (Coherent solid-state Sapphire, 488 nm and 635 nm, respectively, 15 mW) at the speed of 2 m s -1 . More details on the instrument can be found elsewhere (Pomati et al 2011). Digital data acquisition was triggered by the sideward scatter (SWS) signal with a trigger-level of 40 mV, which excludes particles smaller than circa 0.9 lm.…”

“…Cytobuoy particle descriptors were standardised and, by principal component analysis (PCA), reduced to 30 orthogonal vectors covering 99 % of the total variance in the data. Principal components (PCs) were utilised for grouping particles into functional categories based on Cytobuoy-derived morphology (Pomati et al 2011. In this study we used unsupervised model-based clustering to group phytoplankton, applying maximum likelihood estimation and Bayes criteria to identify the most likely model and number of clusters.…”