Exploring and exploiting new tools for functional trait extraction from in situ plankton images
Résumé
Plankton ecologists are increasingly looking toward functional trait-based approaches to analyze pelagic marine ecosystems. Within this framework, an individual organism’s fitness is assessed as a function of its particular combination of traits. Likewise, the distribution of organisms in the ocean can be understood as an ecosystem structured by functional traits. Lots of progress has been made from a modelling and theoretical perspective, but observational studies remain limited. Many traits of interest, however, are either directly measurable or inferable from data collected by in situ imaging systems. Here we discuss several such traits and the methods one might use to extract them from plankton image data. We particularly focus on quantifying the mass of egg sacs on ovigerous copepods directly from a globally distributed set of images collected by the Underwater Vision Profiler. Early results drawing on modern object detection, segmentation, and deep regression methods will be discussed. While these techniques are applied to a very particular set of data, they are quite general and could be broadly applied in image-based studies of other pelagic organisms.