First Harmful Dinophysis (Dinophyceae, Dinophysiales) Bloom in the U.S
Total Page：16
File Type：pdf, Size：1020Kb
J. Phycol. 46, 66–75 (2010) Ó 2009 Phycological Society of America DOI: 10.1111/j.1529-8817.2009.00791.x FIRST HARMFUL DINOPHYSIS (DINOPHYCEAE, DINOPHYSIALES) BLOOM IN THE U.S. IS REVEALED BY AUTOMATED IMAGING FLOW CYTOMETRY1 Lisa Campbell2 Department of Oceanography and Department of Biology, Texas A&M University, College Station, Texas 77843, USA Robert J. Olson, Heidi M. Sosik Department of Biology, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, USA Ann Abraham Gulf Coast Seafood Laboratory, U.S. Food and Drug Administration, P.O. Box 158, 1 Iberville Drive, Dauphin Island, Alabama 36528, USA Darren W. Henrichs Department of Biology, Texas A&M University, College Station, Texas 77843, USA Cammie J. Hyatt and Edward J. Buskey Marine Science Institute, The University of Texas, 750 Channelview Drive, Port Aransas, Texas 78373, USA Imaging FlowCytobot (IFCB) combines video and of water flow was into the estuary, suggesting the flow cytometric technology to capture images of source of the bloom was offshore. nano- and microplankton (10 to >100 lm) and to Key index words: automated; dinoflagellate; early measure the chlorophyll fluorescence associated warning; flow cytometry; Gulf of Mexico; harmful with each image. The images are of sufficient reso- algae; imaging; life history; monitoring lution to identify many organisms to genus or even species level. IFCB has provided >200 million Abbreviations: cox1, cytochrome c oxidase I; DSP, images since its installation at the entrance to the diarrhetic shellfish poisoning; DTX-1, dinophysis Mission-Aransas estuary (Port Aransas, TX, USA) in toxin–1; IFCB, Imaging FlowCytobot; LC–MS ⁄ MS September 2007. In early February 2008, Dinophysis (SRM), liquid chromatography–mass spectrome- ) cells (1–5 Æ mL 1) were detected by manual inspec- try (selected reaction monitoring); OA, okadaic tion of images; by late February, abundance acid; UTMSI, University of Texas Marine Sciences ) estimates exceeded 200 cells Æ mL 1. Manual micros- Institute copy of water samples from the site confirmed that D. cf. ovum F. Schu¨tt was the dominant species, with cell concentrations similar to those calculated from IFCB data, and toxin analyses showed that okadaic Monitoring combined with rapid response has acid was present, which led to closing of shellfish been identified as one of the most effective ways to harvesting. Analysis of the time series using auto- mitigate the impact of HABs (CENR 2000). Moni- mated image classification (extraction of image fea- toring programs typically rely on manual microscopy tures and supervised machine learning algorithms) for phytoplankton identification, but the levels of revealed a dynamic phytoplankton community com- expertise and effort required for manual analyses position. Before the Dinophysis bloom, Myrionecta make it difficult to obtain temporal resolution suffi- rubra (a prey item of Dinophysis) was observed, and cient for early warning and analysis of bloom another potentially toxic dinoflagellate, Prorocen- dynamics. Continuous automated methods can trum, was observed after the bloom. Dinophysis cell- potentially solve this problem; one such approach is division rates, as estimated from the frequency of imaging-in-flow cytometry. The success of early dividing cells, were the highest at the beginning of attempts to use commercially available imaging- the bloom. Considered on a daily basis, cell in-flow equipment for HAB detection was limited by concentration increased roughly exponentially up to inadequate image quality and image recognition the bloom peak, but closer inspection revealed that software, and by rapid fouling (Buskey and Hyatt the increases generally occurred when the direction 2006, Campbell et al. 2008). A new imaging system developed to overcome these limitations, IFCB (Olson and Sosik 2007), is capable of unattended 1 long-duration deployments and produces high- Received 7 April 2009. Accepted 29 August 2009. 2Author for correspondence: e-mail [email protected]. quality images that allow many phytoplankton cells 66 DINOPHYSIS DYNAMICS FROM IMAGING FLOWCYTOBOT 67 to be identified to genus or even species. In addi- seawater sampling tube (15 m Tygon tube, ID 1 ⁄ 8¢¢) extended to 3 m beneath the water surface at mean tide, and the tion, an automated image classifier with accuracy )1 comparable to that of human experts has been pumping rate was 90 mL Æ min . Copper screen (1 ⁄ 16¢¢) excluded large particles from the Tygon tubing. Fouling was demonstrated (Sosik and Olson 2007). Effectiveness removed by running bleach through the sample delivery tubing and durability have been demonstrated by nearly at weekly intervals. The IFCB sample was taken from this flow continuous deployment of an IFCB off the coast of through PEEK tubing (2 m long, ID 1 mm), with 150 lm nylon New England since summer 2006 (Sosik et al. screening to prevent larger particles from entering the IFCB. A 2009). 5 mL sample was analyzed by IFCB every 20 min; fluorescent Our objective was to monitor the phytoplankton standard particles (red fluorescent 9 lm, XPR-1653; Duke in Texas coastal waters, specifically to detect the Scientific Corp., Palo Alto, CA, USA) were run after every 50th sample. Images were transferred via the Internet for analysis toxic dinoflagellate Karenia brevis, which has been and archiving. increasing in frequency in this area. Although no Automated classification and abundance estimates. IFCB images K. brevis blooms occurred in Texas waters in 2008, were automatically processed and classified following the the imaging approach revealed a different, unex- approach described in Sosik and Olson (2007). The approach pected, and also potentially harmful dinoflagellate relies on a supervised machine learning algorithm (Support species. From mid-February to the end of March Vector Machine) where training is based on example images categorized by manual inspection. The specific classifier 2008, Dinophysis spp., predominantly D. cf. ovum, developed in Sosik and Olson (2007) was not applicable to were recorded by IFCB. this data set because the community composition was very Species of Dinophysis may produce okadaic acid different. To build a new classifier, training set images for this (OA) and structurally related derivatives known as work were taken from samples drawn intermittently from dinophysis toxins (Yasumoto et al. 1985). These throughout the Port Aransas time series. Because the resulting heat-stable and lipophilic toxins are protein phos- number of manually identified categories was large (>40) and our immediate goal was to optimize accurate enumeration of phatase inhibitors; they are concentrated by filter- selected categories (i.e., Dinophysis spp., Prorocentrum spp., feeding bivalves and can cause diarrhetic shellfish Myrionecta rubra), we used a modification of Sosik and Olson poisoning (DSP) in humans. Although DSP is a (2007). For this situation, we constructed a series of ‘‘one- major concern in Europe and Japan, and several versus-all’’ support vector machines, instead of a single multi- occurrences of Dinophysis have been reported for class (‘‘one-vs.-one’’) classifier. The main advantage of this the East Coast of the U.S. (Maranda and Shimizu variant is that it allows us to use different subsets of image 1987, Tango et al. 2004), DSP-causing toxins in features for each classifier, where each subset is optimized for discriminating the corresponding category (from all other shellfish have not previously been reported above categories combined). All images from the time series the action level in the US. (unknowns) were evaluated against each resulting classifier; Here, we describe how continuous and auto- in the case of conflicts (image positively associated with more mated monitoring provided the necessary informa- than one category), final identity was assigned to the category tion for timely closure of shellfish harvesting, which with the highest probability of affiliation. Images that did not prevented a potentially large number of cases of have positive affiliation with any of the training set categories were labeled ‘‘other.’’ As in Sosik and Olson (2007), final human illness from DSP (J. R. Deeds, K. Wiles, abundance estimates for the categories of interest were G. B. Heideman VI, K. D. White, and A. Abraham, determined after correction for classification error rates by unpublished data). The high temporal resolution implementing the procedure of Solow et al. (2001). and long duration of the observations allowed us to Water sampling. In addition to the IFCB sampling, surface examine factors that could influence population seawater samples were manually collected from the pier, near dynamics, including life-history stages (gametes and the intake of the IFCB. On 15 February 2008, a sample was inspected by manual microscopy to verify the presence of planozygotes), cell-division rate (based on frequency Dinophysis. On 25 February 2008, replicate aliquots of seawater of dividing cells), and possible prey (M. rubra). were preserved with Lugol’s iodine for cell counting by manual LM and cytochrome c oxidase I (cox1) sequence analysis and frozen for toxin analysis. Additional samples were collected for MATERIALS AND METHODS cell counts only on five dates later in February and March, and Instrument and deployment. IFCB (Olson and Sosik 2007) two samples archived before 15 February 2008 (for other captures images of nano- and microplankton (10 to purposes) were also later inspected for Dinophysis. >100 lm) together with the light scattering and chlorophyll Manual