Biology subjects

Pomati, F.

Publications and source records attributed to Pomati, F..

16 recordsLinked to original sources

bioRxiv2025

Zooplankton feeding behaviour and survival to toxic and non-toxic cyanobacteria during the seasonal bloom progression of a eutrophic lake

Harmful cyanobacterial blooms pose increasing threats to aquatic ecosystems and human health; yet, the role of zooplankton grazing in regulating blooms remains understudied. We investigated the seasonal feeding behaviour and fitness consequences of feeding preferences in natural zooplankton communities for toxic (microcystin-producing) versus non-toxic cyanobacteria across temperature gradients in eutrophic Lake Greifen, Switzerland. We conducted monthly experiments from April to October 2023 to test the grazing behaviour of four zooplankton groups (daphnids, calanoid copepods, cyclopoid copepods, and microzooplankton) exposed to mixed diets of green algae and either toxic or non-toxic Microcystis strains at 15{degrees}C and 25{degrees}C. Contrary to expectations of cyanobacteria avoidance, zooplankton exhibited predominantly non-selective grazing throughout the seasonal succession, consuming both toxic and non-toxic cyanobacteria at similar rates, regardless of temperature. Notably, during the peaks of phytoplankton abundance (April and September), mesozooplankton demonstrated a selective preference for cyanobacteria over green algae, particularly non-toxic strains. Temperature effects were subtle but revealed metabolic constraints: elevated temperatures occasionally triggered selective consumption of cyanobacteria in copepods, while fitness costs (survival) from exposure to toxic species were mostly restricted to transitional bloom periods and high-temperature conditions. These findings suggest that toxic cyanobacteria may not always evade grazing pressure through secondary metabolite deterrent effects. Our results suggest that zooplankton communities can adapt and graze on cyanobacteria regardless of toxicity under the tested conditions, even during bloom conditions. These observations highlight the potential for zooplankton to interact with cyanobacterial populations, which may have implications for bloom prediction and management strategies, particularly under climate warming scenarios. Manuscript HighlightsO_LIZooplankton grazed on toxic and non-toxic cyanobacteria with similar effects across seasons in a eutrophic lake characterised by toxic blooms. C_LIO_LINatural zooplankton communities showed no systematic avoidance of microcystin-producing cyanobacteria. C_LIO_LIHigh temperature effects on feeding selectivity were subtle and taxon-specific. C_LIO_LIFitness costs from exposure to cyanobacteria (including microcystin-producing isolates) were rare and occurred only during transitional bloom periods. C_LIO_LIResults suggest that zooplankton communities may be adapted to cyanobacterial blooms, even when dominated by toxic species. C_LI

ecology↗

bioRxiv2025

Rewilding beyond the wilderness: Beavers can restore stream biodiversity from urban to agricultural to natural landscapes

1 Beavers have been promoted as a cost-effective nature-based solution to restore degraded stream ecosystems and enhance overall biodiversity. However, the extent to which beaver-engineering boosts biodiversity in human-impacted landscapes remains poorly understood. 2 We assessed the responses of aquatic and terrestrial taxa (amphibians, dragonflies, fish, macrophytes, plankton, terrestrial vegetation, bats, aquatic, flying and terrestrial invertebrates) to beaver-engineering along a gradient of human land-use intensity in Switzerland. In 16 streams, we measured species richness, abundance, unique species richness, evenness, beta and gamma diversity in a beaver-engineered site with beaver dams and a control site without beaver activity. 3 Especially aquatic and semi-aquatic species profited from beaver-engineering. In response to beaver-engineering, amphibians, dragonflies, macrophytes, and plankton increased in species richness, abundance and unique species richness, while fish and terrestrial plants increased in species richness and unique species richness. Bats only increased in unique species richness, while for the other terrestrial taxa, we found no difference between beaver-engineered and control sites. Community evenness remained largely unaffected across all groups. 4 Overall, beaver-engineering increased biodiversity across a land-use gradient. For macrophytes and amphibians (species richness, unique species richness and abundance), as well for dragonflies (species richness and unique species richness), increases were smaller under higher land-use intensity. For the remaining taxa and metrics, we found no relationship between land-use intensity and responses to beaver engineering. 5 However, for beta-diversity, we found that sites with a high land-use intensity had a lower component of nestedness and stronger community turnover in comparison to more natural sites, with a significant effect for macrophytes. Gamma species richness was generally unaffected by land- use intensity, except for amphibians, which were often only found in beaver-engineered sites in high- land-use intensity areas. 6 Synthesis and applications. Our findings demonstrate that beaver-engineering significantly enhances local biodiversity across aquatic and terrestrial ecosystems, even at sites with high land-use intensity. Hence, beavers can effectively restore stream biodiversity from urban to agricultural to natural landscapes. Integrating beaver-engineering into river restoration strategies can substantially advance biodiversity goals under frameworks such as the EU Water Framework Directive and national conservation policies.

ecology↗

bioRxiv2025

Biotic interactions shape the realised niche of toxic cyanobacteria

Cyanobacterial blooms increasingly threaten vital freshwater ecosystems, with harmful impacts exacerbated by climate change and eutrophication. Despite extensive research on temperature and nutrient effects, our predictive capacity remains limited. We propose that this limitation stems from insufficient understanding of how biotic interactions modify cyanobacterial responses to abiotic conditions. Using five years of daily monitoring data from a eutrophic lake and state-space reconstruction modelling, we show that interactions with co-occurring plankton species fundamentally reshape the realised niche of bloom-forming cyanobacteria. Biotic interactions shift temperature thresholds by up to 13{degrees}C and phosphorus requirements by over 20 g/L--effects substantial enough to determine whether environmental conditions support or prevent blooms in Microcystis and Dolichospermum. Grazing inhibits bloom formation across cyanobacterial taxa, while facilitation by other phytoplankton may allow blooms at unexpectedly low temperatures and phosphate concentrations. These findings address a fundamental research gap--how species interactions shape realised niches in natural systems--while offering practical insights for bloom management. By integrating biotic interactions into monitoring programs and predictive models, we can improve forecasting accuracy and develop targeted interventions that complement traditional nutrient control approaches. These findings parallel recent advances in ecology suggesting the fundamental role of biotic interactions in mediating species responses to environmental change.

ecology↗

bioRxiv2025

Five years of high-frequency data of phytoplankton zooplankton and limnology from a temperate eutrophic lake

This study presents a comprehensive dataset from Lake Greifen, Switzerland, collected between April 2018 and June 2023, using high-frequency automated monitoring systems. The dataset integrates meteorological data, nutrient chemistry, water column profiles for water physics, and plankton underwater imaging, offering insights into the lakes physical and biological processes. A dual-magnification dark field underwater microscope captured hourly plankton dynamics at 3 m depth, providing size, shape, and taxonomic information. A profiler with a multiparametric probe monitored water temperature, oxygen, and other key parameters from 1 to 17 m depth, while weekly nutrient sampling complemented the measurements. Data processing involved rigorous cleaning protocols to remove technical artefacts, ensuring data quality. Our dataset showcases the utility of integrating different approaches for high-frequency monitoring to detect lake temporal processes, from phytoplankton blooms to zooplankton vertical migration and seasonal shifts in water column stability. This dataset provides a unique resource for studying limnology and plankton community ecology. All data and related processing codes are publicly available for further research, supporting interdisciplinary studies.

ecology↗

bioRxiv2024

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

Anthropogenic changes such as climate change and pollution have strong effects on plankton and its ecosystem services. In freshwater, phytoplankton forms the basis of the food web and plays a key role in water quality. It is therefore important to monitor the plankton community. In Switzerland, lake plankton has been monitored for decades by cantonal authorities. But in the future, there will be a limitation in the availability of trained taxonomists that are able to count plankton samples under the microscope as well as time and budget constraints. New technologies will be needed to fulfil the federal requirements on the monitoring of lakes in the future. Here we focus on two such instruments, both based on plankton automated imaging: the FlowCam and the Aquascope. The first is a commercially available instrument, the second is custom made. They are the only automated imaging instruments that can track freshwater plankton across the desired size range (phytoplankton and zooplankton). Their design makes them the state of the art technologies to make quantitative observations of plankton. While the FlowCam is a laboratory instrument, the Aquascope is originally designed for field deployment. In this report, we focus the empirical work on the laboratory application of the Aquascope approach of automated imaging and classification for long term plankton monitoring. We find that there are major advantages of the Aquascope approach but the application as a laboratory instrument still needs further development. The major advantages of the Aquascope approach compared to traditional, human-supervised, microscopy methods are the automation and the speed of the data processing, the availability of individual level traits and the management of the data generated. One of the major drawbacks is the lack of taxonomic resolution (mostly at the genus level). Nevertheless, we show that with a larger benchmarking project, it might be possible to continue the long term time series on a coarse taxonomic level as usually reported by cantonal authorities to the general public. We discuss advantages and limitations of Aquascope relative to traditional microscopy and FlowCam, and propose ways forward for future development and application of this approach.

ecology↗

bioRxiv2024

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

The interplay between abiotic (resource supply, temperature) and biotic (grazing) factors determines growth and loss processes in phytoplankton through resource competition and trophic interactions, which are mediated by morphological traits like size. Here, we study the relative importance of grazers, water physics and chemistry on the daily net accumulation rates (AR) of individual phytoplankton from natural communities, grouped into six size classes from circa 10 to 500 m. Using a Random Forest modelling approach and four years of daily data from a lake, we find that water temperature is generally a pivotal control of all phytoplankton AR. At the same time, nutrients and light are important for the smallest and the largest classes. Mesozooplankton abundance is a key predictor of the AR for small phytoplankton, with microzooplankton being important for the middle-size range. In our data, large and small phytoplankton have different (seasonal) blooming patterns: small forms are favoured by low temperature and grazing, and high phosphorus levels. Larger forms show positive ARs at high temperatures and low phosphorus (being relatively insensitive to zooplankton grazing). These results help us understand the opportunities and limitations of using size to explain and model phytoplankton responses to biotic and abiotic environmental change.

ecology↗

bioRxiv2024

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

Two fundamental questions in ecology are how biodiversity is maintained and how it affects ecosystem functioning. Until now, it has been difficult to study the above mechanisms in natural microbial communities, yet they are important drivers of biogeochemical ecosystem functions. Here, we use a new approach to define and measure biodiversity in complex lake microbial communities (Lake Cadagno, Switzerland) based on the cell-to-cell variation in multiple functionally relevant phenotypic traits. We use stable isotope probing coupled to correlative imaging using confocal laser scanning microscopy (CLSM) and nanometer-scale secondary ion mass spectrometry (NanoSIMS) to obtain morphological (size), physiological (pigments) and metabolic (carbon and nitrogen isotope uptake and sulfur content) traits for a large number of individual cells along the environmental gradient found across lake depth. We show that cell-to-cell trait variation is significantly correlated with cell densities as a proxy for ecosystem functioning, whereas genetic diversity measured at the level of 16S and 18S is not. Our single-cell analysis provides evidence for a simultaneous increase in niche partitioning (measured as increased evenness in pigment composition) and decrease in fitness differences (measured as decreased variability in sulfur content) due to light limitation and competition for nutrients in deep layers of the lake. This leads to a negative relationship between niche and fitness differences. Our results suggest that niche and fitness differences in natural microbial communities can be understood at the level of single-cell traits, providing a mechanistic understanding of the relationship between microbial diversity and ecosystem functioning.

microbiology↗

bioRxiv2023

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

Facing climate change and biodiversity loss, it is critical that ecology advances so that processes, such as species interactions and dynamics, can be correctly estimated and skillfully forecasted. As different processes occur on different time scales, the sampling frequency used to record them should intuitively match these scales. Yet, the effect of data sampling frequency on ecological forecasting accuracy is understudied. Using a simple simulated dataset as a baseline and a more complex high-frequency plankton dataset, we tested how different sampling frequencies impacted abundance forecasts of different plankton classes and the estimation of their interactions. We then investigated whether plankton growth rates and body sizes could be used to select the most appropriate sampling frequency. The simple simulated dataset showed that the optimal sampling frequency scaled positively with growth rate. This finding was not repeated in the analyses of the plankton time series, however. There, we found that a reduction in sampling frequency worsened forecasts and led us to both over- and underestimate plankton interactions. This suggests that forecasting can be used to determine the ideal sampling frequency in scientific and monitoring programs. A better study design will improve theoretical understanding of ecology and advance policy measures dealing with current global challenges. Open research statementData and code used for the analyses and figures are available on Zenodo: https://doi.org/10.5281/zenodo.10066786. Environmental (lake) data (Merkli et al. 2022) are available from ERIC: https://doi.org/10.25678/00066D.

ecology↗

bioRxiv2021

Deep Learning Classification of Lake Zooplankton

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, FlowCytobot and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.

ecology↗

bioRxiv2021

Underwater dual-magnification imaging for automated lake plankton monitoring

We present an approach for automated in-situ monitoring of phytoplankton and zooplankton communities based on a dual magnification dark-field imaging microscope/camera. We describe the Dual Scripps Plankton Camera (DSPC) system and associated image processing, and assess its capabilities in detecting and characterizing plankton species of different size and taxonomic categories, and in measuring their abundances in both laboratory and field applications. In the laboratory, body size and abundance estimates by the DSPC significantly and robustly scale with the same measurements derived by traditional microscopy. In the field, a DSPC installed permanently at 3 m depth in Lake Greifensee (Switzerland), delivered images of plankton individuals, colonies, and heterospecific aggregates without disrupting natural arrangements of interacting organisms, their microenvironment or their behavior at hourly timescales. The DSPC was able to track the dynamics of taxa in the size range between [~]10 m to [~] 1 cm, covering virtually all the components of the planktonic food web (including parasites and potentially toxic cyanobacteria). Comparing data from the field-deployed DSPC to traditional sampling and microscopy revealed a general overall agreement in estimates of plankton diversity and abundances, despite imaging limitations in detecting small phytoplankton species and rare and large zooplankton taxa (e.g. carnivorous zooplankton). The most significant disagreements between traditional methods and the DSPC resided in the measurements of community properties of zooplankton, organisms that are heterogeneously distributed spatially and temporally, and whose demography appeared to be better captured by automated imaging. Time series collected by the DSPC depicted ecological succession patterns, algal bloom dynamics and circadian fluctuations with a temporal frequency and morphological resolution that would have been impossible with traditional methods. We conclude that the DSPC approach is suitable for stable long-term deployments, and robust for both research and water quality monitoring. Access to high frequency, reproducible and real-time data of a large spectrum of the planktonic ecosystem might represent a breakthrough in both applied and fundamental plankton ecology.

ecology↗

bioRxiv2019

A Universal Phase Transition in Plankton Trait Dynamics

Key ecological traits, like cell size, often follow scale-free or self-similar distributions. This indicates that these systems might operate near a critical (i.e. second-order) phase transition where macroscopic system behaviour is largely decoupled from microscopic system details, allowing an extremely simple, yet accurate and robust mathematical system characterisation. However, how trait-distribution scaling results from a critical transition has not yet been explicitly demonstrated. Here, we demonstrate that a generic class of cell growth and division models exhibits a critical transition from a growth-dominated to a division-dominated phase. We find experimental evidence for this transition, both in the population dynamics and in the moment scaling of chlorophyll distributions, for prokaryotic and eukaryotic phytoplankton growth under different light intensities. Our approach offers testable predictions of the response of unicellular trait-distributions to perturbations.

ecology↗

bioRxiv2018

High dispersal levels and lake warming are emergent drivers of cyanobacterial community assembly over the Anthropocene

Disentangling the relative importance of deterministic and stochastic processes in shaping natural communities is central to ecology. Studies on community assembly over broad temporal and spatial scales in aquatic microorganisms are scarce. Here, we used 16S rDNA sequence data from lake sediments to test for community assembly patterns in cyanobacterial phylogenies across ten European peri-Alpine lakes and over a century of eutrophication and climate warming. We studied phylogenetic similarity in cyanobacterial assemblages over spatial and temporal distance, and environmental gradients, comparing detected patterns with theoretical expectations from deterministic and stochastic processes. We found limited evidence for deviation of lake communities from a random assembly model and no significant effects of geographic distance on phylogenetic similarity, suggesting no dispersal limitation and high levels of stochastic assembly. We did not detect a significant effect of phosphorus and nitrogen levels on deviation of community phylogenies from random. We found however a significant decay of phylogenetic similarity for non-random communities over a gradient of air temperature and water column stability. We show how phylogenetic data from sedimentary archives can improve our understanding of microbial community assembly processes, and support previous evidence that climate warming has been the strongest environmental driver of cyanobacterial community assembly over the Anthropocene.

ecology↗

bioRxiv2018

Resource supply drives even spacing of individuals along multiple trait axes in light-limited phytoplankton populations

Individual-level variation arising from responses to environmental gradients influences population and community dynamics. How such responses empirically relate to the mechanisms that govern species coexistence is however not well understood. Previous results from lake phytoplankton communities suggested that the evenness of organisms in multidimensional trait space increases with resource limitation, possibly due to resource partitioning at the individual level. Here we experimentally tested the emergence of this pattern by growing two phytoplankton species (Pseudokirchneriella subcapitata, Microcystis aeruginosa) under a gradient of light intensity, in monoculture and jointly. Under low light (resource) conditions, the populations diversified into a wide range of phenotypes, which were evenly distributed in multidimensional trait space (defined by four pigment-related trait dimensions), confirming the observed field pattern. Our results provide prime experimental evidence that resource limitation induces even spacing of conspecific and heterospecific microbial phenotypes along trait axes, and advances our understanding of trait-based coexistence.

ecology↗

bioRxiv2018

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

Scanning flow cytometry (SFCM) is characterized by the measurement of time-resolved pulses of fluorescence and scattering, enabling the high-throughput quantification of phytoplankton morphology and pigmentation. Quantifying variation at the single cell and colony level improves our ability to understand dynamics in natural communities. Automated high-frequency monitoring of these communities is presently limited by the absence of repeatable, rapid protocols to analyse SFCM datasets, where images of individual particles are not available. Here we demonstrate a repeatable, semi-automated method to (1) rapidly clean SFCM data from a phytoplankton community by removing signals that do not belong to live phytoplankton cells, (2) classify individual cells into trait clusters that correspond to functional groups, and (3) quantify the biovolumes of individual cells, the total biovolume of the whole community and the total biovolumes of the major functional groups. Our method involves the development of training datasets using lab cultures, the use of an unsupervised clustering algorithm to identify trait clusters, and machine learning tools (random forests) to (1) evaluate variable importance, (2) classify data points, and (3) estimate biovolumes of individual cells. We provide example datasets and R code for our analytical approach that can be adapted for analysis of datasets from other flow cytometers or scanning flow cytometers.

ecology↗

bioRxiv2017

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

Forecasting anthropogenic changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R2 decreased from 0. 89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.

ecology↗

bioRxiv2016

Perfluorooctane sulfonate affects proliferation and differentiation of pluripotent human teratocarcinoma cells

Perfluorinated compounds have raised concern due to their potential association with detrimental postnatal outcomes in animals and humans. We tested the effects of perfluorooctane sulfonate (PFOS) on a human pluripotent teratocarcinoma (known as NCCIT) cells as an in vitro prototype for developmental toxicity in mammals. NCCIT contains stem-cells able to differentiate into endoderm, mesoderm and ectoderm. We tested our cell model using a teratogenic compound, retinoic acid (RA), a cytotoxin, nocodazole (ND), and PFOS. We assayed cells proliferation, morphology and expression of stem cell and germ layer marker genes. PFOS reduced NCCIT cell proliferation in a concentration-dependent manner and induced morphological changes in cell cultures that resembled ectodermal phenotypes. A tendency towards a differentiated state in NCCIT was confirmed by real-time gene expression. PFOS triggered up-regulation of the gene nestin, indicative of ectodermal lineage differentiation, and interfered with the expression of the pluripotency stem-cell marker TERT. PFOS produced effects on both cells proliferation and differentiation, although not as severe as those observed for RA and ND, at levels that fall within the range of concentrations found in animal and human plasma. We discuss our findings in the context of possible interference of PFOS with the processes governing the early development of mammalian tissues.

pharmacology and toxicology↗