Assessing Noxious Phytoplankton in Aquaculture Systems Using Bio‐Optical Methodologies: A Review

David F. Millie, Oscar M. Schofield, Christopher P. Dionigi, Peter B. Johnsen

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Management practices in aquaculture systems contribute to maximum growth of phytoplankton, often resulting in extensive blooms of noxious cyanobacteria. Because periods of oxygen depletion and “off flavor” events correspond to intense algal growth and metabolic activity, accurate identification of algal dynamics and physiological state is important. Current efforts to assess algal assemblages rely upon microscopic evaluation; however, the incorporation of such evaluation into monitoring programs is limited due to the level of skill and training required, the excessive costs and time required to assess algal heterogeneity within/among aquaculture systems, and the lack of information provided concerning physiological state. The distinct biwptical characteristics of the blooms lend themselves to pigment‐based methodologies (pigment and in vivo absorption “signatures”, chlorophyll a fluorescence, multi‐spectral remote sensing) which complement microscopic evaluation and can be implemented into large‐scale monitoring programs. For example, because the key ingredient for success of such programs is the rapid, reliable, and accurate characterization of algal biomass along variable temporal/spatial scales, remotely‐sensed data acquisition most likely will be required. High‐performance liquid chromatography (HPLC)‐derived pigment and in vivo absorption “signatures” can delineate problematic algal phylogenetic groups and physiological states. Further, measurements of chlorophyll a fluorescence provide estimates of phytoplankton absorption, quantum efficiency, and potentially production potential and growth rate. As such, they can be used to confirm the systematic significance of remotely‐sensed data. It would be highly desirable to integrate an evaluation program using bio‐optical methodologies into a geographic information system to allow for integrating, modeling, and predicting parameters of management interest over the scales relevant to aquacultural and water resource management.

Original languageEnglish (US)
Pages (from-to)329-345
Number of pages17
JournalJournal of the World Aquaculture Society
Volume26
Issue number4
DOIs
StatePublished - Dec 1995

All Science Journal Classification (ASJC) codes

  • Aquatic Science
  • Agronomy and Crop Science

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