Biology subjects

Merz, E.

Publications and source records attributed to Merz, E..

8 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↗

bioRxiv2024

Biodiversity forecasting in natural plankton communities reveals temperature and biotic interactions as key predictors

As natural ecosystems experience unprecedented human-made degradation, it is urgent to deliver quantitative anticipatory forecasts of biodiversity change and identify relevant biotic and abiotic predictors. Forecasting natural ecosystems has been challenging due to their complexity, chaotic nonlinear nature and the availability of adequate data. Here, we use four years of daily abundance of a complex lake planktonic ecosystem and its abiotic environment to model and forecast biodiversity metrics. Using a state-of-the-art equation-free modelling technique, we forecast community richness and turnover with a proficiency greater than the constant predictor several generations ahead (30 days). Short-term forecasts improve substantially using biotic predictors (i.e., autoregressive term or community richness). Long-term forecasts require a more complex set of variables (i.e., biotic interactions), and the forecast proficiency depends strongly on including abiotic predictors such as water temperature. Depending on the forecast horizon, biotic and abiotic predictors can interact nonlinearly and synergistically, enhancing each others effects on biodiversity metrics. Our findings showcase the challenges of forecasting biodiversity in natural ecosystems and stress the importance of monitoring focal biotic and abiotic predictors to anticipate undesired changes.

ecology↗

bioRxiv2024

The interplay between light, arsenic and H2O2 controls oxygenic photosynthesis in a Precambrian analog cyanobacterial mat.

The delayed rise of atmospheric oxygen, despite the early evolution of oxygenic photosynthesis (OP), remains a central puzzle in Earth history. Numerous ecological and geochemical constraints on OP have been proposed, but the role of environmental stressors at the physiological and ecosystem level is poorly understood. Here we show that Chl-f-harboring cyanobacteria in a high-altitude Andean microbial mat - an analog for Precambrian ecosystems - switch from OP to arsenite-driven anoxygenic photosynthesis (AP) under high light. Using microsensor profiling, mat incubations, and metatranscriptomics, we show that this shift is triggered by the accumulation of reactive oxygen species (ROS), especially hydrogen peroxide, which suppresses OP. Instead of ceasing activity, cyanobacteria reroute electron flow, using arsenite as the electron donor to sustain photosynthesis while avoiding both intracellular ROS from OP and extracellular ROS from aerobic arsenite oxidation. This switch is reversible and coordinated with diel cycles of light and arsenic speciation, sustained by a cryptic arsenic redox cycle, continuously regenerating arsenite for AP. Although the enzymatic basis remains unresolved, these findings reveal a hidden layer of metabolic plasticity in cyanobacteria and suggest that oxidative stress-responsive metabolic shifts may have supported early phototroph survival while limiting oxygen release - potentially contributing to Earths protracted oxygenation.

biochemistry↗

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

Selection for later flowering time in an orchid through frost damage and pollinator activity

Flowering time is a key trait for plant reproductive success ensuring both overlap with pollinator activity and favorable conditions for fruit development. To quantify selection on flowering time, we individually marked 1250 plants of the Small Spider Orchid, Ophrys araneola RchB., in six populations in Northern Switzerland and surveyed them during three years. We recorded the date of first flowering, frost damage, and fruiting success of individual plants. In addition, we analyzed historical records of the orchid and its only verified pollinator, the solitary bee Andrena combinata in Northern Switzerland, to estimate potential desynchronization due to climate change. We documented strong selection for later flowering driven by frost damage, with all populations showing significant selection for later flowering in at least one year. Selection for later flowering driven by pollination (fruit set) could only be analyzed in one population due to the overall low fruit set, where it was significant in one year. The historical data from between 1970 and 2019 indicated low synchronization between orchid flowering and bee occurrence, with mean flowering three weeks earlier than the mean peak of bee occurrence, corroborating selection for later flowering through fruit set. The data also showed a significant advance of flowering time and bee-occurrence in the last decades, but to a similar degree in orchids and bees, hence without an indication of increasing desynchronization through climate change. Our study shows that selection for later flowering is mostly caused by frost damage, but also by the little synchronized flowering and pollinator activity, which is however unlikely to be a consequence of climate change in this orchid.

evolutionary biology↗

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↗