Quantitative size and biomass distributions from particle images: An improved algorithm applied to IFCB observations

Heidi M Sosik1, Emily Peacock1, E Taylor Crockford1, Kevin Archibald2, Bethany Fowler3 and Alexi Shalapyonok1, (1)Woods Hole Oceanographic Institution, Woods Hole, MA, United States, (2)University of California Santa Barbara, Department of Ecology, Evolution, and Marine Biology, Santa Barbara, United States, (3)Woods Hole Oceanographic Institution, Biology, Woods Hole, MA, United States
Automated imaging is a powerful approach for detailed characterization of plankton and other particles that impact ocean optical properties, structure food webs, and influence carbon cycling and export processes. Quantitative interpretation of images is essential to produce size and biomass estimates that are unbiased and consistent across different particle types and different measurement systems. Here we show step-by-step evaluation of a new algorithm for analysis of Imaging FlowCytobot (IFCB) data and show that it produces quantitative particle sizes that are consistent with independent assessments. We recommend that this new algorithm (ifcb-analysis, v4) should replace the current standard (v2) in the IFCB user community for applications where quantitative particle sizing is a priority. Application of this approach to an existing IFCB data set highlights the power of automated imaging combined with verified algorithms for characterizing high frequency variability in taxon-specific plankton biomass.