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Schickele, A.

Publications and source records attributed to Schickele, A..

4 recordsLinked to original sources

Global pattern of nitrogen metabolism in marine prokaryotes

The ocean nitrogen cycle is driven by an ensemble of metabolic processes sustaining marine ecosystems and ocean productivity. However, the spatial distribution and environmental drivers of its major pathways, i.e., nitrogen fixation, denitrification, assimilatory and dissimilatory nitrate reduction to ammonium (ANRA, DNRA), and nitrification are not well known. Furthermore, the taxonomic composition of the prokaryotes supporting each pathway remain incompletely understood. Leveraging newly assembled global marine metagenomic datasets and a state-of-the-art machine learning framework, we inferred the global biogeography of the genomic potential for key metabolic pathways of the marine nitrogen cycle. This was achieved using a multi-output regression of gene read counts against environmental climatologies. Our results reveal distinct biogeographic patterns of genomic potential: anaerobic or light-inhibited pathways are enriched in high-latitude regions, eastern boundary upwelling systems, and deeper ocean layers, while nitrogen fixation and ANRA dominate in oligotrophic gyres. These patterns are consistent with known metabolic strategies, model-based estimates, and underlying taxonomy. Indeed, we identify that Cyanobacteria associate primarily with aerobic, biosynthetic pathways, while Gammaproteobacteria and Nitrososphaeria encode for nitrogen transformations related to energy requirements. By coupling microbial community composition with genome-level information, our approach advances understanding of the microbial foundations of nitrogen transformation pathways and offers new insights on underrepresented processes into biogeochemical models. We highlight the growing value of omic data to better understand marine ecosystem function in relation to environmental gradients and community composition, and their use as a potential observation-based alternative or complement to biogeochemical models.

biochemistry↗

Variations in the latitudinal diversity gradients of the ocean microbiome

Latitudinal diversity gradients (LDGs), typically declining from equator to poles, are a pervasive macroecological pattern, yet their generality and drivers in the ocean microbiome remain widely unresolved. We integrated global-scale metagenomic data with habitat modeling to study marine microbial LDGs across seasons and depths. Surface mixed layer microbiomes exhibited diversity peaks at (sub)tropical latitudes and a poleward decline, whereas mesopelagic communities (200-1,000 m) showed no latitudinal diversity structuring. Taxonomic resolution revealed that the mixed layer LDG was underpinned by Alphaproteobacteria and Cyanobacteriia, while other taxa exhibited distinct or contrasting LDGs. Diversity structuring also varied by seasons and regions, governed by temperature and nutrient availability. Together, these findings highlight that within the ocean microbiome, LDGs are not universal, but lineage-specific ecological strategies and responses to environmental gradients. Our study provides fundamental insights into the structuring of ocean microbiome diversity and lays the foundation for predicting responses to environmental change.

microbiology↗

Standardizing marine habitat modelling practices to enhance inter-comparability across biological observations

In recent years, the volume of accessible marine pelagic observations has increased exponentially and now incorporates a wealth of new data types, including information derived from metagenomics and quantitative imaging. This calls for standardized modelling protocol across taxonomically harmonized observations, to better predict biogeographic patterns in space and time, and thus investigate marine ecosystem structure and functioning on a macroecological scale. In this context, we introduce CEPHALOPOD (Comprehensive Ensemble Pipeline for Habitat modelling Across Large-scale Ocean Pelagic Observation Datasets), a standardized and flexible framework to perform multi-species marine habitat modelling across data types and data sources. We built this new framework on observational data from federating initiatives such as AtlantECO, OBIS, GBIF, associated with already existing statistical and machine learning methods that enable to extract and model information from heterogeneous, scarce, and biased field observations. Here, we first document our statistical ensemble modelling approach and then assess its strength and limitations with a virtual ecologist approach. We show how our framework performs in reproducing a range of distributions from biased field samples. Then, we illustrate its performance and comparability across data types by investigating the global diversity patterns of coccolithophores from both abundance and metagenomic data. Our modelling framework serves as a foundation for the consistent generation of Essential Biodiversity and Ocean Variables (EBVs and EOVs) and carries the potential to significantly advance our comprehension of biodiversity and marine ecosystems functioning. Finally, it provides an unprecedented opportunity to foster collaborations in the field of marine science, sustainable ecological practices, and, ultimately, contribute to the preservation of global marine biodiversity.

ecology↗

Picoeukaryotic photosynthetic potential is functionally redundant but taxonomically structured at global scale

Primary production, performed by RUBISCO, and often associated with carbon concentration mechanisms, is of major importance in the oceans. Thanks to growing metagenomic resources (e.g., eukaryotic Metagenome-Assembled-Genomes; MAGs), we provide the first reproducible machine-learning-based framework to derive the potential biogeography of a given function, through the multi-output regression of the standardized number of reads of the associated genes on environmental climatologies. We use it to study the genomic potential of C4-photosynthesis of picoeukaryotes, a diverse and abundant group of marine unicellular photosynthetic organisms. We show that the genomic potential supporting C4-enzymes and RUBISCO exhibit strong functional redundancy and an important affinity towards tropical oligotrophic waters. This redundancy is then structured taxonomically by the dominance of Mamiellophyceae and Prymnesiophyceae in mid and high latitudes. Finally, unlike the genomic potential related to most C4-enzymes, the one of RUBISCO showed a clear pattern affinity for temperate waters.

ecology↗