Imaging 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 statisticallydefined 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. Functional traits are any features-morphological, physiological, etc-measurable at the individual-level and affecting the fitness of the organism (Violle et al. 2007). They can be classified according to the ecological function that they influence, such as feeding, growth, reproduction, and survival (Litchman et al. 2013). Trait-based approaches appeared in plant ecology in the 70s (Grime 1974) and stated being used by aquatic ecologists in the early 2000s (Willby et al. 2000; Usseglio-Polatera et al. 2000; Benedetti et al. 2016; Martini et al. (in press)). Trait-based analyses are relevant in community ecology because an individual's set of traits given environment determines its success (Violle et al. 2007). Ecological interactions (predation, mutualism, etc.) happen between individuals, not between taxonomic groups. Therefore, using trait composition can simplify the analysis of ecosystem complexity by focusing on a few characteristics transcending taxonomic distinctions and impacting ecological strategies (Litchman et al. 2013). By studying the composition and distribution of individual traits in an ecosystem, its structure and dominant proc...

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.1 Note that in this paper "morphology" should be understood in its biological sense, that is, the visually identifiable properties of an object, rather than in its computer vision sense, that is, the numerical characteristics derived from the binary mask of the object (its "imprint" in the image).