Biology subjects

Dennis, S.

Publications and source records attributed to Dennis, S..

6 recordsLinked to original sources

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

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↗

bioRxiv2022

SOAR: a spatial transcriptomics analysis resource to model spatial variability and cell type interactions

Spatial transcriptomics provides researchers with a better understanding of gene expression within the tissue context. Although large volumes of spatial transcriptomics data have been generated, the lack of systematic curation and analysis makes data reuse challenging. Herein, we present Spatial transcriptOmics Analysis Resource (SOAR), a resource with an extensive, systematically compiled collection of spatial transcriptomics data across tissues, organs, and pathological conditions. SOAR is a comprehensive database with uniformly processed and annotated samples, facilitating future benchmark studies and method development. SOAR also offers multi-pronged analysis capability, including an integrative approach toward drug discovery that allows for efficient exploration of novel and targeted therapeutic uses for existing compounds.

bioinformatics↗

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↗

bioRxiv2020

Integrating multimodal connectivity improves prediction of individual cognitive abilities

SO_SCPLOWUMMARYC_SCPLOWHow white matter pathway integrity and neural co-activation patterns in the brain relate to complex cognitive functions remains a mystery in neuroscience. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how multimodal brain connectivity relates to cognition. Specifically, we evaluate whether integrating functional and structural connectivity improves prediction of individual crystallised and fluid abilities in 415 unrelated healthy young adults from the Human Connectome Project. Our primary results are two-fold. First, we demonstrate that integrating functional and structural information - at both a model input or output level - significantly outperforms functional or structural connectivity alone to predict individual verbal/language skills and fluid reasoning/executive function. Second, we show that distinct pairwise functional and structural connections are important for these predictions. In a secondary analysis, we find that structural connectivity derived from deterministic tractography is significantly better than structural connectivity derived from probabilistic tractography to predict individual cognitive abilities.

neuroscience↗