Trait‐based approach using in situ copepod images reveals contrasting ecological patterns across an Arctic ice melt zone

International audienceImaging techniques are increasingly used in ecology studies, producing vast quantities of data. Inferring functional traits from individual images can provide original insights on ecosystem processes. Morphological traits are, as other functional traits, individual characteristics influencing an organism's fitness. We measured them from in situ image data to study an Arctic zooplankton community during sea ice break‐up. Morphological descriptors (e.g., area, lightness, complexity) were automatically measured on ∼ 28,000 individual copepod images from a high‐resolution underwater camera deployed at more than 150 sampling sites across the ice‐edge. A statistically‐defined morphological space allowed synthesizing morphological information into interpretable and continuous traits (size, opacity, and appendages visibility). This novel approach provides theoretical and methodological advantages because it gives access to both inter‐ and intra‐specific variability by automatically analyzing a large dataset of individual images. The spatial distribution of morphological traits revealed that large copepods are associated with ice‐covered waters, while open waters host smaller individuals. In those ice‐free waters, copepods also seem to feed more actively, as suggested by the increased visibility of their appendages. These traits distributions are likely explained by bottom‐up control: high phytoplankton concentrations in the well‐lit open waters encourages individuals to actively feed and stimulates the development of small copepod stages. Furthermore, copepods located at the ice edge were opaquer, presumably because of full guts or an increase in red pigmentation. Our morphological trait‐based approach revealed ecological patterns that would have been inaccessible otherwise, including color and posture variations of copepods associated with ice‐edge environments in Arctic ecosystems