Julian Merder

Carnegie Institution for Science

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Jan A. Freund

Carl von Ossietzky Universität Oldenburg

Thorsten Dittmar

Helmholtz Institute for Functional Marine Biodiversity

Maren Striebel

Carl von Ossietzky Universität Oldenburg

Ding He

Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)

Yuanbi Yi

Hong Kong University of Science and Technology

Helmut Hillebrand

Helmholtz Institute for Functional Marine Biodiversity

Anne Power

Ollscoil na Gaillimhe – University of Galway

Alina M. Wieczorek

National Institute of Water and Atmospheric Research

A. M. Michalak

Carnegie Institution for Science

Patricia Browne

University of St Andrews

Cooperative Institutions

Université du Québec à Montréal

Natural Sciences and Engineering Research Council

Carl von Ossietzky Universität Oldenburg

Ecological Society of America

John Wiley & Sons (United States)

Chinese Academy of Sciences

Wageningen University & Research

University of Göttingen

Centre National de la Recherche Scientifique

Centre de Coopération Internationale en Recherche Agronomique pour le Développement

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Temperature drives co-limitation of light and nutrients in phytoplankton

Zenodo (CERN European Organization for Nuclear Research) (2023)

Anna Lena Heinrichs Anika Happe Apostolos‐Manuel Koussoroplis Helmut Hillebrand Julian Merder

These files contain all data necessary to create the figures published in the study "Temperature drives colimitation of light and nutrients in phytoplankton" The uploaded ZIP file temperature drives colimitation.zip contains all R codes and tables needed for the data analyses used in this study All data needed for the R-scripts are available in the folder 'RTables' A READMe file that descripe all data files is avalable in the ZIP file R-scripts "a_growth rate_package" and "b_growth rate manually.R": We determined the maximum linear growth rate using the optical density measurements (needed table is "Opticaldensity.txt") R-script: "c_gamlss_growthrates.R": We used a GAMLSS to search for a model that best described the observed growth rates and used a null model that predicts additive effect on growth (needed table is "growthrates_ODratiobased.txt") - generates data for Figure 1 and Figure 2, and Table 2 R-script "d_gamlss_LRR.R": We calculated the LRR between observed growth rates and growth rates predicted by the null model to classify co-limitation responses in sub-additive (LRR<0) and super-additive (LRR>0) (needed table is "gamlss_fig3") - generates data for Figure 3 and Table 3 R-script "e_data_vizualisation.R" : Contains all R codes to generate all figures

10.5281/zenodo.8328238

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Density-dependent growth in ‘catch-and-wait’ fisheries has implications for fisheries management and Marine Protected Areas

AMBIO (2019)

Julian Merder Patricia Browne Jan A. Freund Liam Fullbrook Conor T. Graham

Stock enhancement activities provide an opportunity to examine density-dependent suppression of population biomass which is a fundamental issue for resource management and design of no-take-zones. We document 'catch-and-wait' fisheries enhancement where all but the largest lobsters are thrown back, recapturing them later after they have grown to a larger size. The residency, rate of return, and potential negative density-dependent effects of this activity are described using a combination of tagging and v-notching and by relating spatial growth patterns to population density defined with Catch Per Unit Effort. The results successfully demonstrated the concept of catch-and-wait practices. However, a density-dependent suppression of growth (in body size) was observed in male lobsters. This demonstrates a mechanism to explain differences in lobster sizes previously observed across EU fishing grounds with different stock densities. This negative effect of density could also affect individual biomass production in marine reserve or no-take zones.

Stock (firearms)

Density dependence

Marine Fisheries

Stock assessment

Population density

10.1007/s13280-019-01158-1

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Citations (13)

Improved Mass Accuracy and Isotope Confirmation through Alignment of Ultrahigh-Resolution Mass Spectra of Complex Natural Mixtures

Analytical Chemistry (2019)

Julian Merder Jan A. Freund Ulrike Feudel Jutta Niggemann Gabriel Singer

Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is one of the state-of-the-art methods to analyze complex natural organic mixtures. The precision of detected masses is crucial for molecular formula attribution. Random errors can be reduced by averaging multiple measurements of the same mass, but because of limited availability of ultrahigh-resolution mass spectrometers, most studies cannot afford analyzing each sample multiple times. Here we show that random errors can be eliminated also by averaging mass spectral data from independent environmental samples. By averaging the spectra of 30 samples analyzed on our 15 T instrument we reach a mass precision comparable to a single spectrum of a 21 T instrument. We also show that it is possible to accurately and reproducibly determine isotope ratios with FT-ICR-MS. Intensity ratios of isotopologues were improved to a degree that measured deviations were within the range of natural isotope fractionation effects. In analogy to δ13C in environmental studies, we propose Δ13C as an analytical measure for isotope ratio deviances instead of widely employed C deviances. In conclusion, here we present a simple tool, extensible to Orbitrap-based mass spectrometers, for postdetection data processing that significantly improves mass accuracy and the precision of intensity ratios of isotopologues at no extra cost.

Isotopologue

Orbitrap

10.1021/acs.analchem.9b04234

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Citations (34)

Determinants of Microbial-Derived Dissolved Organic Matter Diversity in Antarctic Lakes

Environmental Science & Technology (2023)

Morimaru Kida Julian Merder Nobuhide Fujitake Yukiko Tanabe Kentaro Hayashi

Identifying drivers of the molecular composition of dissolved organic matter (DOM) is essential to understand the global carbon cycle, but an unambiguous interpretation of observed patterns is challenging due to the presence of confounding factors that affect the DOM composition. Here, we show, by combining ultrahigh-resolution mass spectrometry and nuclear magnetic resonance spectroscopy, that the DOM molecular composition varies considerably among 43 lakes in East Antarctica that are isolated from terrestrial inputs and human influence. The DOM composition in these lakes is primarily driven by differences in the degree of photodegradation, sulfurization, and pH. Remarkable molecular beta-diversity of DOM was found that rivals the dissimilarity between DOM of rivers and the deep ocean, which was driven by environmental dissimilarity rather than the spatial distance. Our results emphasize that the extensive molecular diversity of DOM can arise even in one of the most pristine and organic matter source-limited environments on Earth, but at the same time the DOM composition is predictable by environmental variables and the lakes' ecological history.

Carbon fibers

10.1021/acs.est.3c00249

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Citations (7)

Sedimentology

Figshare (2019)

Anne Power Julian Merder Patricia Browne Jan A. Freund Liam Fullbrook

Dataset related to sediment size and sorting information sampled adjacent to the Nephrops norvegicus fishing grounds, to enable ground-truthing of acoustic seabed bottom types.

10.6084/m9.figshare.7149908

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ICBM-OCEAN: Processing Ultrahigh-Resolution Mass Spectrometry Data of Complex Molecular Mixtures

Analytical Chemistry (2020)

Julian Merder Jan A. Freund Ulrike Feudel Christian T. Hansen Jeffrey A. Hawkes

Untargeted molecular analyses of complex mixtures are relevant for many fields of research, including geochemistry, pharmacology, and medicine. Ultrahigh-resolution mass spectrometry is one of the most powerful tools in this context. The availability of open scripts and online tools for specific data processing steps such as noise removal or molecular formula assignment is growing, but an integrative tool where all crucial steps are reproducibly evaluated and documented is lacking. We developed a novel, server-based tool (ICBM-OCEAN, Institute for Chemistry and Biology of the Marine Environment, Oldenburg-complex molecular mixtures, evaluation & analysis) that integrates published and novel approaches for standardized processing of ultrahigh-resolution mass spectrometry data of complex molecular mixtures. Different from published approaches, we offer diagnostic and validation tools for all relevant steps. Among other features, we included objective and reproducible reduction of noise and systematic errors, spectra recalibration and alignment, and identification of likeliest molecular formulas. With 15 chemical elements, the tool offers high flexibility in formula attribution. Alignment of mass spectra among different samples prior to molecular formula assignment improves mass error and facilitates molecular formula confirmation with the help of isotopologues. The online tool and the detailed instruction manual are freely accessible at www.icbm.de/icbm-ocean.

10.1021/acs.analchem.9b05659

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Citations (119)

Unraveling the Linkages between Molecular Abundance and Stable Carbon Isotope Ratio in Dissolved Organic Matter Using Machine Learning

Environmental Science & Technology (2023)

Yuanbi Yi Tongcun Liu Julian Merder Chen He Hongyan Bao

Dissolved organic matter (DOM) is a complex mixture of molecules that constitutes one of the largest reservoirs of organic matter on Earth. While stable carbon isotope values (δ13C) provide valuable insights into DOM transformations from land to ocean, it remains unclear how individual molecules respond to changes in DOM properties such as δ13C. To address this, we employed Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterize the molecular composition of DOM in 510 samples from the China Coastal Environments, with 320 samples having δ13C measurements. Utilizing a machine learning model based on 5199 molecular formulas, we predicted δ13C values with a mean absolute error (MAE) of 0.30‰ on the training data set, surpassing traditional linear regression methods (MAE 0.85‰). Our findings suggest that degradation processes, microbial activities, and primary production regulate DOM from rivers to the ocean continuum. Additionally, the machine learning model accurately predicted δ13C values in samples without known δ13C values and in other published data sets, reflecting the δ13C trend along the land to ocean continuum. This study demonstrates the potential of machine learning to capture the complex relationships between DOM composition and bulk parameters, particularly with larger learning data sets and increasing molecular research in the future.

Carbon fibers

10.1021/acs.est.3c00221

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Citations (14)

Geographic redistribution of microcystin hotspots in response to climate warming

Nature Water (2023)

Julian Merder Ted D. Harris Gang Zhao Dimitrios Stasinopoulos Robert A. Rigby

Abstract High concentrations of cyanobacterial toxins such as microcystin represent a global challenge to water quality in lakes, threatening health, economies and ecosystem stability. Lakes are sentinels of climate change but how warming will affect microcystin concentrations is still unclear. Here we examine how warming impacts the probability of exceeding microcystin water quality thresholds across 2,804 lakes in the United States and show how future warming will alter these probabilities. We find that higher temperatures consistently increase the likelihood of microcystin occurrence but that the probability of microcystin concentrations above water quality thresholds is highest for water temperatures between 20 and 25 °C. Regions with temperatures that promote microcystin will shift to higher latitudes in the coming decades, leading to relative changes in exceedance probabilities of more than 50% in many basins of the United States. High nitrogen concentrations amplify the impact of rising temperatures, calling for increased awareness of a substantial hazard to ecosystems and human health under global warming.

Cyanotoxin

Freshwater ecosystem

10.1038/s44221-023-00138-w

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Citations (27)

Reply to: Empirical pressure-response relations can benefit assessment of safe operating spaces

Nature Ecology & Evolution (2021)

Helmut Hillebrand Ian Donohue W. Stanley Harpole Dorothee Hodapp Michal Kučera

10.1038/s41559-021-01484-2

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Citations (3)

The Hunt for Chemical Dark Matter across a River-to-Ocean Continuum

Environmental Science & Technology (2024)

Ruanhong Cai Piao Yao Yuanbi Yi Julian Merder Penghui Li

Thousands of mass peaks emerge during molecular characterization of natural dissolved organic matter (DOM) using ultrahigh-resolution mass spectrometry. While mass peaks assigned to certain molecular formulas have been extensively studied, the uncharacterized mass peaks that represent a significant fraction of organic matter and convey biogenic elements and energy have been previously ignored. In this study, we introduce the term dark DOM (DDOM) for unassigned mass peaks and have explored its characteristics and environmental behaviors using a data set of 38 DOM extracts covering the Yangtze River-to-ocean continuum. We identified a total of 9141 DDOM molecules, which exhibited higher molecular weight and greater diversity than the DOM subset with assigned DOM formulas. Although DDOM contributed a smaller fraction of relative abundance, it significantly impacted the molecular weight and molecular composition of bulk DOM. A portion of DDOM with higher molecular weight was found to increase molecular abundance across the river-to-ocean continuum. These compounds could contain halogenated organic molecules and might have a high potential to contribute to the refractory organic carbon pool. With this study, we underline the contribution of dark matter to the total DOM pool and emphasize that more DDOM research is needed to understand its contribution to global biogeochemical cycles and carbon sequestration.

Biogeochemical Cycle

Fragmentation

Yangtze river

Mass fraction

Carbon fibers

10.1021/acs.est.4c00648

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Citations (3)