Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.

Over the past decades, climate change has induced transformations in the weed flora of arable ecosystems in Europe. For instance, thermophile weeds, late-emerging weeds, and some opportunistic weeds have become more abundant in some cropping systems. The composition of arable weed species is indeed ruled by environmental conditions such as temperature and precipitation. Climate change also influences weeds indirectly by enforcing adaptations of agronomic practice. We therefore need more accurate estimations of the damage potential of arable weeds to develop effective weed control strategies while maintaining crop yield. Here we review the mechanisms of responses of arable weeds to the direct and indirect effects of climate change. Climate change effects are categorized into three distinct types of shifts occurring at different scales: (1) range shifts at the landscape scale, (2) niche shifts at the community scale, and (3) trait shifts of individual species at the population scale. Our main conclusions are changes in the species composition and new species introductions are favored, which facilitate major ecological and agronomical implications. Current research mainly considers processes at the landscape scale. Processes at the population and community scales have prevalent importance to devise sustainable management strategies. Trait-climate and niche-climate relationships warrant closer consideration when modeling the possible future distribution and damage potential of weeds with climate change.

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant–organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.

Nicole M. van Dam and Roberto Salguero-Gómez joint last authorship.