The use of trait-based approaches and trait data in zooplankton ecology is rapidly growing to better understand and predict the patterns of zooplankton distributions and their role in aquatic ecosystems and biogeochemical cycles. Although the number of zooplankton trait-based studies and available trait datasets is increasing, several challenges remain for the findability, accessibility, interoperability, and reusability (FAIR) in trait-based approaches that, if unaddressed, may stifle progress in this research area. Here, we review recent applications of trait-based approaches in zooplankton research and summarize the currently available trait data resources. To realize the potential of trait-based approaches to resolve ecological roles of zooplankton, datasets and approaches must adhere to FAIR principles. We provide recommendations and pathways forward to ensure FAIRness while highlighting the importance of collaborative efforts. These practical and easily implementable strategies will enhance the FAIRness of trait data, ultimately advancing zooplankton ecological research and connecting these findings to aquatic ecosystem functioning.

Water temperature controls the physiology, growth rate, distribution, and behavior of most plankton populations in the sea and thus affects the energy transfer in marine ecosystems. The present study focuses on the influence of seasonal changes in sea surface temperature on phytoplankton and the size distribution of copepods in the Arraial do Cabo Upwelling System (Brazil), where a wind-driven coastal upwelling can lead to multiple distinct bottom-up cascade effects on the food web. To address the potential effect of the seasonal changes, environmental data were obtained and the abundance of plankton determined from monthly samples collected in triplicate from 2010 to 2014. The samples were analyzed on a Benchtop FlowCAM (FC), and copepods (<1000 µm) were classified according to their Ellipses Equivalent Major Axis using image analysis software ImageJ (IJ). For IJ analysis, a batch-processing macro was built to open all FC raw images and then crop each copepod individually into a single picture. Using these images, prosome and urosome lengths were manually measured with the straight-line tool in IJ. With the combinations of measurements obtained in the IJ adjusted as FC measurements, we established a new, faster, and more effective way to measure copepods. With the copepod size classification, we found that there is a cycle in copepod size combined with the upwelling cycle that is related to temperature rather than to phytoplankton growth. Copepod abundance as a whole peaked during the autumn, winter, and spring seasons. The method performed here proved that FC is an effective tool for classifying copepod sizes and detecting seasonal variation.

Plankton communities form the foundation of marine food webs, determining the ecosystem carrying capacity and the services they provide. Their spatial distribution patterns are highly dynamic, influenced by ocean stratification, mixed layer depth, and nutrient availability. In this study, we combined an advanced imaging system with a machine learning model to explore how environmental factors shape plankton distribution on the South Brazilian Shelf. We selected four representative categories (Rhizaria, Diatoms, Copepoda and Gelatinous) to encompass the broader diversity of planktonic groups. Our three-dimensional predictions highlight depth as a key driver for all plankton categories, while revealing taxon-specific responses to water column stratification. Gelatinous are notably confined to the mixed layer and dominate oligotrophic regions, irrespective of coastal nutrient availability. In contrast, copepods exhibit a heterogeneous and continuous distribution, with higher abundances near nutrient-rich coastal areas. These contrasting patterns underscore the intricate dynamics of plankton communities and reinforce the need for detailed investigations to better understand their ecological roles and responses to environmental changes.