F. Pomati

Laboratory application of the Aquascope approach of automated imaging and classification for long-term plankton monitoring

The paper evaluates the laboratory application of the Aquascope for automated plankton imaging and classification, highlighting its advantages and limitations compared to traditional methods.

Automated plankton monitoring suggests a key role of microzooplankton and temperature for predicting dynamics of phytoplankton size classes

The paper uses Random Forest models on four years of lake data to show temperature and microzooplankton grazing significantly influence phytoplankton size class dynamics.

Single-cell trait diversity explains niche and fitness differences in aquatic microbial communities

The paper measures single-cell trait diversity in lake microbial communities and finds it correlates with ecosystem functioning, while genetic diversity does not.

The dependence of forecasts on sampling frequency as a guide to optimizing monitoring in community ecology

This study investigates how sampling frequency affects ecological forecasts and finds that optimal frequency depends on growth rates and interactions, suggesting it can guide monitoring strategies.

Underwater dual-magnification imaging for automated lake plankton monitoring

The paper introduces a dual-magnification imaging system for automated plankton monitoring, showing it can accurately detect and characterize plankton species and track ecological dynamics in both lab and field settings.

Deep Learning Classification of Lake Zooplankton

The paper develops deep learning models to classify lake zooplankton images with high accuracy and evaluates their performance on external datasets.

A Universal Phase Transition in Plankton Trait Dynamics

The paper demonstrates a critical phase transition in plankton trait dynamics, showing experimental evidence for a shift from growth- to division-dominated phases in phytoplankton under varying light conditions.

High dispersal levels and lake warming are emergent drivers of cyanobacterial community assembly over the Anthropocene in peri-Alpine lakes

The paper examines cyanobacterial community assembly in peri-Alpine lakes, finding high stochasticity and climate warming as key drivers over the Anthropocene.

Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering

The predictability of a lake phytoplankton community, from hours to years

This study uses machine learning to assess the predictability of phytoplankton communities in a lake across multiple time scales, finding high predictability from hours to months and novel ecological trade-offs.