Performance of the human counting machine : evaluation of manual microscopy for enumerating plankton

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1 JOURNAL OF PLANKTON RESEARCH j VOLUME 34 j NUMBER 12 j PAGES j 2012 Performance of the human counting machine : evaluation of manual microscopy for enumerating plankton MATTHEW R. FIRST 1 * AND LISA A. DRAKE 2 1 SAIC, INC., KEY WEST, FL 33041, USA AND 2 CHEMISTRY DIVISION, NAVAL RESEARCH LABORATORY, KEY WEST, FL 33041, USA *CORRESPONDING AUTHOR: matthew.first.ctr@nrl.navy.mil Received May 14, 2012; accepted August 26, 2012 Corresponding editor: John Dolan In this study, live plankton (50 mm in minimum dimension) in samples were counted by three analysts to determine counting rates, accuracy and precision of manual microscopy. Counting rates were compared with sample characteristics (e.g. concentration of dead organisms). In separate experiments, plankton proxies, spherical microbeads (49 and 150 mm in diameters), were added to samples with varying degrees of debris loading, including samples from full-scale, replica ballast tanks. These analyses were used to test the hypothesis that as debris loading increases, counting rate increases and accuracy decreases. Highly concentrated samples and samples with high concentrations of dead plankton resulted in significantly slower counting rates. The recovery of 50-mm microbeads was lowest (75%) in laboratory samples that contained the highest debris load. The recovery of 150- mm microbeads was very high (.98%) in laboratory samples with and without debris. Field samples from the replica ballast tank were highly turbid, and microbead recoveries were low for both microbead sizes. Sample quality, therefore, will affect counting rates and accuracy and will limit the volume of the sample that can be analyzed within short-time windows available for counting live plankton samples. KEYWORDS: ballast water; microzooplankton; management; zooplankton; methods INTRODUCTION Estimating in situ concentrations of plankton generally consists of two processes: (i) concentrating organisms from large volumes of water (e.g s of m 3 ) and (ii) manually counting a portion of the concentrated sample, which is typically chemically preserved to mitigate the degradation of organisms. Concentrating plankton from large volumes of water is necessary as plankton (especially zooplankton) are often unevenly and sparsely distributed in aquatic environments. Large sample volumes allow for valid statistical estimates of such sparse populations (Wiebe, 1970). The concentration factor will depend upon the expected in situ concentrations, where more sparse populations require larger sample volumes. Sampling and concentrating plankton (e.g. by using a plankton net) may lead to error and variability in concentration estimates. For example, diel vertical migrations can result in stratification of the plankton community throughout the day, and sampling multiple strata may be required to properly account for spatial variation (UNESCO, 1968). Problems with sampling doi: /plankt/fbs068, available online at Advance Access publication September 28, 2012 Published by Oxford University Press 2012

2 M.R. FIRST AND L.A. DRAKE j ACCURACY AND PRECISION OF PLANKTON ENUMERATION devices, such as net clogging and poor retention of target populations, can also introduce error and variability into concentration estimates. The focus on sampling devices and methods has yielded advances in instrument design and sampling protocols, such as the development of undulating towed nets and the use of in situ video cameras (Wiebe and Benfield, 2003). In contrast to sampling and concentrating plankton, the error associated with counting samples has received less attention, likely because of the lower variability in the counting procedures. Small volumes used in counting can be well mixed prior to subsampling. Additionally, counting preserved samples in laboratory environments, while labor intensive, is not time-constrained: chemically fixed plankton are stable over lengthy analysis periods, for months or years. Some investigations, however, require the estimation of living plankton. For example, live counts of organisms in two size classes (50 mm and 10 to,50 mm) are needed to evaluate whether ballast water management systems (BWMSs) used by ships to kill, remove or inactivate organisms in ballast water, and thus reduce the transport and introduction of aquatic nuisance species, meet proposed discharge standards (International Maritime Organization, 2004; U.S. Coast Guard, 2009; U.S. Environmental Protection Agency, 2011a, b). This requirement introduces several constraints. First, plankton mortality can be expected to occur rapidly in densely concentrated samples within laboratory containers. Therefore, analysis must occur quickly after collection. Second, motile plankton can move during analysis, and counts not performed continuously are likely to be invalid (i.e. an analyst cannot discontinue counting and expect to restart sampling at the same point). The time constraint necessitates an understanding of the performance and efficiency of the human counting machine, as both metrics are expected to decrease as researchers become fatigued after hours of counting. Further, high detrital load (non-plankton particles in the sample) and numerous dead organisms will likely increase the counting time and reduce accuracy. Depending on the source water quality and BWMS, treated samples may contain high concentrations of detritus or dead organisms, which can lower the detection efficiency of researchers searching for live organisms. Evaluation of ballast water, in particular, requires a high degree of precision and accuracy. High variation among replicate samples (or among counts from multiple analysts) reduces confidence in the estimates of organism concentrations. During certification testing of BWMSs, inaccurate counts could lead to ineffective systems being approved for use, and their installation aboard vessels would allow for the delivery of unacceptably high numbers of living organisms to receiving waters. During compliance testing of BWMSs, which will assess BWMS performance against a discharge standard, inaccurate counts could lead to erroneous assessments of the efficacy of BWMSs, which, in turn, could lead to fines being levied on vessel operators or higher numbers of organisms being discharged than have been deemed to be environmentally protective. The goal of this work is to critically evaluate the performance of manual microscopy for estimating concentrations of live plankton 50 mm in minimum dimension. Although several automated techniques are available to identify and enumerate plankton (Buskey and Hyatt, 2006; Olson and Sosik, 2007), they cannot (as yet) stimulate movement in organisms (a measure of viability), and so this study used the manual counting protocol described in the U.S. Environmental Protection Agency s (EPA) Environmental Technology Verification (ETV) Program Generic Protocol for the Verification of Ballast Water Treatment Technology (U.S. Environmental Protection Agency, 2010). Here, plankton are counted under magnification, and nonmoving organisms are gently touched with a fine probe or dissecting needle to stimulate movement. In this manner, all living organisms 50 mm in minimum dimension were counted. This size group is dominated by zooplankton, although autotrophs (e.g. diatoms) are also included. Researchers performing the counts had multiple years experience in quantitative microscopy and a long record of generating comparable counts of replicate samples. Additionally, the analysts performing these counts were experienced in rapid counting under time constraints of working with live samples. METHOD Sample characteristics and plankton counting rates A database of historical plankton analyses that had been collected over 1 year ( ) was used to investigate the relationship between sample characteristics and counting rate. Sample characteristics included sample volume (range m 3 ), concentration factor and absolute concentration of dead plankton (Table I). The experimental work yielded 88 unique sample types. Typically, five analytical replicates (i.e. subsamples) were counted for each sample type (range four to six analytical replicates per sample type) for a total of 453 analyses (hereafter, samples ). For the purpose of this study, which focused on the factors influencing counting times, all analyses were treated as independent 1029

3 JOURNAL OF PLANKTON RESEARCH j VOLUME 34 j NUMBER 12 j PAGES j 2012 Table I: Plankton sample characteristics Sample characteristic Units Data range Mean data value (+1 SD) Categories n Ballast Fill without additives 261 Sample type Drain with additives (þ) 117 Drain without additives ( ) 75 Sample volume m Concentration No units, see equation (3) , Factor Live Ind , mm plankton Sample Count Dead Ind , mm plankton Sample Count n, number of samples in the category; SD, standard deviation; Ind., individual. The complete data set (n ¼ 453, 428 for dead plankton) was categorized according to the sample characteristics. For example, all of the samples fell into one of three ballast sample types: ballast tank fill samples (Fill), drain samples of ballast water containing additives (þ) to meet challenge water conditions and drain samples without additives ( ); sample volume was binned into one of four volumes (e.g m 3 ). measurements. While analytical replicates would not be considered independent measurements of organism concentrations, treating the counting times as independent of the sample source was appropriate as the analytical replicates were analyzed by multiple analysts. With the exception of 25 of 453 samples (in which dead plankton were not measured), live and dead plankton concentrations were determined for each sample following a standard protocol (described below). Plankton counting was performed by three analysts, each with.7 years experience and each contributing.100 plankton counts to the database. Analysts manually recorded the start and end times during counting; therefore, these counts could be used to test the hypothesis that there is a relationship between sample counting rate and sample quality. Sample collection and preparation Experimental work was performed at the Naval Research Laboratory in Key West, Florida (NRL). The laboratory includes a facility to conduct research related to BWMSs, which consists of two full scale, aboveground tanks (ballast control and treatment tanks, 151 and 382 m 3 in capacity, respectively), a discharge tank (394 m 3 in capacity) and a piping and pumping system for filling and draining the tanks. The piping system includes inline sensors to monitor pressure and flow rates (GF Signet, El Monte, CA, USA) with measurements collected at 1-min intervals by an automated process control system (Experion PKS, Honeywell, Morristown, NJ, USA). Seawater for experiments was collected through an intake pipe (15 cm in diameter) with 184 holes (each 1.6 cm in diameter) throughout the top 2 m of the water column. Ambient, oligotrophic seawater at NRL has a low particle load, and in several field experiments, seawater was amended with compounds to increase the concentration of dissolved and suspended materials so that water was more representative of ports with waters challenging BWMSs operation (U.S. Environmental Protection Agency, 2010). Hereafter, this water is referred to as amended water (Table II). These materials have been used in ballast water experiments for several years at NRL and do not lead to high rates of plankton mortality (unpublished data). Sampling was generally conducted using a closed-housing filter array (i.e. a filter skid ), which contained individual mesh nets in each filter housing (First et al., 2012) or a plankton net. Target sample volumes ranged from 3 to 60 m 3, and actual sample volumes were typically within 10% of the target volume. Sample volumes were measured by inline flow sensors and data logging software, which totaled the volume flowing through the piping system during sampling. Several nylon mesh plankton nets used in these experiments had nominal mesh sizes of 25, 31 or 35 mm, which correspond to hypotenuse lengths (i.e. the largest openings) of 35, 44 and 49 mm, respectively. Each filter skid housing contained a removable mesh bag with either a 25 or 35 mm mesh. The sample concentration factor for plankton samples (X) 1030

4 M.R. FIRST AND L.A. DRAKE j ACCURACY AND PRECISION OF PLANKTON ENUMERATION Table II: Concentrations of dissolved and particulate organic matter (DOC and POC, respectively), mineral matter (MM) and total suspended solids (TSS) in unamended (ambient) seawater and minimum target concentrations of amended water (i.e. water used in field experiments) Unamended seawater values (mg L 21 ; + 1 SD) DOC POC MM TSS was calculated by the equation: X ¼ S CD Target concentrations for amended water (mg L 21 ) Unamended seawater samples were collected from the seawater surface with a bucket. The water was collected within 3 m of the ballast water system intake pipes on the dates of experiments. Amended water conditions were achieved by supplementing seawater with organic carbon and mineral matter (in the manner and concentrations described in the U.S. EPA s Environmental Technology Verification Protocol; U.S. Environmental Protection Agency, 2010). Mean values for unamended seawater (n ¼ 18) are shown with one standard deviation (SD). ð1þ where S is the sample volume and C is the concentrated sample volume following filtration through a net (both reported in L). Ideally, samples concentrated in the field or laboratory would not have to be diluted (i.e. un-concentrated). However, because the concentration of abiotic and biotic particles was not predictable a priori, concentrated samples were typically diluted prior to analysis (with,1.5-mm filtered seawater, FSW, prepared within 48 h of the experiment, held at 48C and brought to room temperature before being used). Thus, the sample dilution factor, D, is included in the total concentration factor equation. The dilution factor was determined via a preliminary count of living organisms in the concentrated sample using a dissecting microscope. Here, a small volume of the concentrated sample (1 ml) was added to a Sedgewick Rafter counting chamber, and the living organisms in a small portion (10%) of the chamber were counted. The dilution factor was chosen to yield 30 plankton ml 21, which previous work had shown to be ideal for counting live plankton. Dilutions ranged from 1 (no dilution) to 30. Plankton counting technique Plankton considered in these analyses include all organisms 50 mm in minimum dimension. This size class was used because this work informed testing protocols designed to verify the performance of BWMSs, and proposed international and national standards for ballast water discharge define concentration limits using size ranges rather than functional groups (International Maritime Organization, 2004; U.S. Coast Guard, 2009). The counting approach used in this study was based upon the ETV method for evaluating organisms. Briefly, samples were counted in Bogorov chambers, which are acrylic plates (dimensions: cm) with a capacity of 4 ml. Bogorov chambers have a single sinuous chamber (40 cm in length) that holds the sample water. The chamber is narrow (to allow full coverage in the field of view at the chosen magnification) and shallow, so the majority of the water column is within the focal plane. Plankton in Bogorov chambers were counted (under light microscopy at total magnification) by moving the field of view along the length of the chamber and tallying and categorizing plankton into the appropriate taxonomic group. Spherical microbeads ( mm in diameter, Chromosphere; Fisher Scientific, Pittsburgh, PA, USA) suspended in FSW were added to the Bogorov chamber as a size reference. Typically,.10 microbeads were visible in every field of view throughout the chamber. Only organisms larger than the microbeads were counted. Organisms were typically well distributed throughout the depth of the water column (i.e. at the water surface and the chamber bottom), and so the entire sample depth was scanned at each field of view using the optical focus of the microscope. Organisms were classified into major taxonomic groups (e.g. adult and copepodite stages of copepods, copepod nauplii, ciliates, diatoms etc.), and both live and dead organisms were tallied simultaneously. This approach differed from that in the ETV Protocol (U.S. Environmental Protection Agency, 2010), in which all dead organisms are counted, the sample is preserved, all organisms are counted, and the number of live organisms is determined by subtraction. The counting approach used allowed for the enumeration of live and dead organisms in a single analysis. Live organisms were identified by motion, and if an individual was not moving, a small metal probe was used to gently touch the organism. An organism was classified as living if this stimulus provoked movement within 10 s. Some organisms (e.g. diatoms) would not be expected to respond to prodding and, therefore, were classified as living if cellular structures, such as frustules and chloroplasts, appeared intact. Concentrations of both live and dead plankton (P, individuals L 21 ) were calculated by the following equation: P ¼ ICD AS ¼ 1 AX ; ð2þ 1031

5 JOURNAL OF PLANKTON RESEARCH j VOLUME 34 j NUMBER 12 j PAGES j 2012 where I is the number of individual organisms counted and A is the volume of the sample analyzed (i.e. the Bogorov chamber, converted to L). The concentration factor (X) was calculated based upon the total sample volume (S), the dilution factor (D) and the volume of the concentrated sample following filtration through a net [C; equation (1)]. Counting rate analysis For all plankton analyses, times were recorded (to the nearest minute) at the beginning and end of analysis. Sample preparation and documentation (other than tallying organisms) were not included in this time interval. Counting rates were compared with sample characteristics, including numbers of live or dead plankton and sample source (e.g. tank fill or discharge, challenge water or unamended [ambient] seawater). Sample characteristics are summarized in Table I. For some characteristics, the subcategories were chosen to represent typical sampling conditions. For instance, target sample volumes collected for studies at NRL were usually either 3, 5, 10 or 30 m 3, so the data were binned into groups close to these target sample volumes. For biological characteristics of samples, such as live plankton concentrations, the entire data range was apportioned into four categories, each with approximately the same number of records. Counting was performed during one continuous sitting, and counts were completed within 6 h of sampling (to minimize the plankton mortality in laboratory containers). The total counting rate (recorded for each Bogorov chamber counted) was normalized to the sample volume so that counting rates (R) were reported in units of min ml 21. All sample information (including counting rates and plankton concentrations) was entered into a database. A one-way analysis of variance (ANOVA) was used to test for significant difference (a ¼ 0.05) between three or more categories (Matlab V7.9, the Mathworks, Natick, MA, USA), and pairwise differences between categories were identified with a post hoc test (Tukey s HSD). To verify that significant differences were not due to random chance in data arrangement, 1000 random data rearrangements were grouped into equal bin sizes using Matlab. These randomly populated bins were tested for significant differences with ANOVA. Determining counting accuracy using spherical microbeads Counting accuracy was tested by using microbeads to simulate plankton and determining the recovery of the microbeads. In this work, plankton samples were simulated in the laboratory by adding microbeads to various concentrations of added suspended and dissolved materials (no living plankton were present). Field samples were collected from a large-scale simulated ballast water experiment, in which 200 m 3 of ambient water (amended with suspended and dissolved materials) was held for 5 days and then sampled upon draining the tank; microbeads were added to the concentrated plankton samples. The advantages of using spherical microbeads are that: (i) a known quantity of microbeads can be added to samples, (ii) microbeads will not degrade during counting, (iii) microbeads are within a known size range and, therefore, can be used to test the counting accuracy of objects near the minimum of a size class (e.g. 50 mm) or larger objects (150 mm), and (iv) unlike many live plankton, microbeads do not move, and so their detection can be more difficult than mobile plankton. Thus, in some aspects, detecting and counting microbeads was more challenging than detecting plankton, as plankters movement allows for improved detection, especially when organisms are obscured by debris. On the other hand, the bright color and uniform shape of the microbeads made them easier to detect than some organisms. Initial microbead counting and preparation Microbeads used in this study were either mm (coefficient of variation, CV, ¼ 8%; hereafter, 50 mm) or mm (CV ¼ 6%) in diameter. Suspensions of microbeads (both 50 and 150 mm diameter) were created by adding 50 ml of dry microbeads to 1.5 ml of Type II water (water purified by deionization and reverse osmosis) in a microcentrifuge tube. The microbead suspension was then vortexed at high speed to suspend and disperse the microbeads, and a small volume (10 20 ml) of the microbead suspension was added to a clean glass microscope slide. The droplet with suspended microbeads was examined under a dissecting microscope (Nikon AZ100) at magnification. To count the number of microbeads in each droplet, still images of the suspended microbeads were captured with a digital camera attached to the microscope and analyzed with image acquisition and processing software (Nikon Elements AR, Nikon, Melville, NY, USA). Droplets containing.300 microbeads were rejected because high numbers of microbeads are difficult to count (due to overlapping and touching microbeads). The microbead count generated by the object counting software was manually reviewed to verify that all microbeads were counted correctly. Any incorrect counts (e.g. microbead doublets that were counted as single microbeads) were corrected by adding or subtracting the appropriate number from the total count. Usually, the quantity of microbeads incorrectly counted by the software was 1032

6 M.R. FIRST AND L.A. DRAKE j ACCURACY AND PRECISION OF PLANKTON ENUMERATION,5% of the total count. Once the count was verified, another droplet with microbeads was added to the slide, and the counting process was repeated until the target number was achieved. Typically, three to four droplets were added and counted on each glass slide. Before the water in the droplet evaporated (which would result in the microbeads adhering to the glass slide), the droplets were rinsed into a cleaned 50-mL glass beaker. Type II water in a spray bottle was used to thoroughly rinse the slide. After rinsing, the slide was examined under a dissecting microscope (10 20, darkfield illumination) to count any microbeads that remained on the slide. Generally, microbeads were completely rinsed from the slides. Any microbeads remaining on the slide were subtracted from the final count. The beaker containing the known quantity of suspended microbeads was stored at 48C until analysis, that is, when the microbead suspension would be mixed into the sample. The final concentration of microbeads in the suspension was known only to the analyst preparing the suspensions. Analysts counting the samples spiked with this microbead suspension were unaware of the number of microbeads added. Microbead recovery in laboratory experiments Simulated plankton samples were prepared by amending FSW with suspended and dissolved materials that would have been found in 1 m 3 of amended water from a test of a BWMS conducted following requirements in the ETV Protocol (Fig. 1, Table II). The simulated sample volume was 1 L; therefore, this sample was 1000 more concentrated than 1 m 3 of amended water. To approximate the process of collecting and concentrating plankton, the amended water was poured through a 35-mm mesh-size filter bag (18-cm mouth diameter, 81 cm in length; Filter Specialists, Michigan City, IN, USA) and rinsed with FSW. This process removed most of the small particles, colloids and dissolved materials. The material retained in the filter bag (i.e. the filtrand) was rinsed into a glass beaker. FSW was added to the filtrand to bring the sample volume to 0.5 L, representing an undiluted concentrated sample. Samples representing several dilutions (25, 50 and 75%) were prepared by repeating this process and diluting the concentrated sample with FSW to a final volume of 0.5 L. Type II water was used to prepare a control sample (100% dilution, 0.5 L). Next, microbead suspensions were added to the samples. Approximately 100 ml of the sample was removed and used to rinse the microbead suspension from its beaker into the sample. The beaker containing the microbeads was examined using a dissecting microscope to account for any microbeads that were not deposited in the sample. These preparations yielded a series of simulated plankton samples with varying quantities of suspended materials and known quantities of microbeads. Aliquots (10 ml) of each sample were examined in large Bogorov chambers (15 ml capacity) using a dissecting microscope (15 30 magnification) with darkfield illumination. Sample water was transferred from its beaker to Bogorov chambers with a 10-mL glass pipette, which was used throughout the analysis. After the entire sample was analyzed, the pipette was examined using a dissecting microscope for the presence of residual microbeads. When a microbead was found (which was rare), it was added to the final microbead tally. The analysis time was not to exceed 12 h of observation (6 h for each of the two analysts), and analysis was typically complete within 48 h of sample preparation. These time constraints were the same as the limits for the analysis of live samples, as longer holding times have led to mortality in previous experiments conducted at NRL (unpublished data). Note that this maximal sample holding time will likely vary based upon the community composition. Counting did not occur in a continuous, 6-h block; instead, the entire time spent counting totaled 6 h, as plankton die-off was not a Fig. 1. Schematic for the preparation of laboratory samples, which were used to determine microbead recovery in laboratory experiments. Shown here are the dilutions used for the 50-mm microbead experiments: 0, 25, 50, 75 and 100% (type II water with microbeads added served as a control). Experiments using 150-mm microbeads (0 and 100% dilution, only) were prepared in the same manner. For field experiments, both 50 and 150-mm microbeads were added to the plankton samples that were collected as the ballast tank was drained (0% dilution, 1 L volume). Type II water is 0.22 mm filtered and deionized tap water; POC, particulate organic carbon; DOC, dissolved organic carbon; MM, mineral matter (concentrations are found in Table II). 1033

7 JOURNAL OF PLANKTON RESEARCH j VOLUME 34 j NUMBER 12 j PAGES j 2012 concern. Microbead recovery (i.e. the number of microbeads counted compared with the microbeads added) was determined for both 50-mm microbeads (in 0, 25, 50, 75 and 100% dilutions) and for 150-mm microbeads (in 0 and 100% dilutions). Only two types of samples were created for the larger microbeads because previous work had shown their recovery, even in samples with high levels of debris, to be quite high. For inter-comparison between analysts, at least five Bogorov chambers (containing 10 ml samples) were examined by both analysts per experiment. In a typical experiment, a total of 50 Bogorov chambers would be analyzed per analyst; thus, at least 10% of the chambers were examined by both analysts. Microbead counts were not shared until both analysts had completed counting. These counts were used to assess whether the counting procedure used was repeatable and whether the results of two experienced analysts were comparable. Microbead recovery in ballast water field experiments Microbead recovery was tested in plankton samples obtained after ambient seawater was held for 5 days. Experiments were performed during 9 14 September 2010 and during September In both experiments, the ballast tank was filled with 200 m 3 of seawater with an ambient assemblage of organisms, which had been amended with additives (see Table II). Water was held in the tank for 5 days, which is the minimum hold time specified in the IMO G8 guidelines for BWMS verification testing (International Maritime Organization, 2005), and to date, all BWMSs that have been type approved by Administrations (i.e. verified to meet a given discharge standard) have been evaluated using this hold time. In addition, the 5-day period allowed for the biological and physical processes that transform organic matter (i.e. particulate formation) to occur. Water was then discharged into a separate tank for treatment to remove additives before it was released. During the discharge, 10 m 3 of the ballast water was sampled in a filter skid, which consisted of four stainless steel housings (First et al., 2012). Water was first filtered through two housings (in parallel) that each contained 50-mm mesh filter bags with a mouth diameter of 18 cm and length of 81 cm (material collected in these first filter bags was not analyzed) The 50-mm mesh filter bags simulated a BWMS that incorporated a coarse filter either on uptake or upon discharge. A second series of two housings in parallel each contained 35-mm mesh filter bags (with the same mouth diameter and filter bag length as above). The filtrand collected in the second set of filter housings (with 35-mm mesh filter bags) was designated as the concentrated plankton sample. The sample was rinsed into a glass beaker and diluted with FSW to yield a 1 L concentrated sample. Both 50- and 150-mm diameter microbeads were added to the concentrated sample, and microbeads were counted following the method described above (organisms were not counted in these experiments). In the first 5-day experiment, the filtrand collected in both of the two filter bags was combined (representing 10 m 3 of discharged ballast water). In the second 5-day experiment, the filtrand in only one of the two filter bags was analyzed for plankton (thus representing 5 m 3, assuming the flow was evenly split between the two filter housings); the filtrand in the second filter bag was analyzed for total suspended solids (TSS) and particulate organic carbon (POC). The water collected in the samples was highly turbid, and these measurements were performed to quantify the debris load. The TSS was measured gravimetrically after filtering the sample water through a pre-weighed glass fiber filter, drying the filter (1048C for 1 h) and weighing it. Next, the filter was combusted at 5508C and weighed. POC was calculated as the mass lost after combustion. Analysis of microbead counting The microbead counting data (collected from both laboratory and field experiments) were used for multiple analyses: (i) counting rates along a range of debris concentrations, (ii) microbead recovery, which represents the accuracy of the analysis and (iii) variation among analytical replicates, which represents the precision of analysis. Counting rates were normalized to the volume of sample analyzed (in this case 10 ml). Microbead recovery was calculated as the number of observed microbeads counted in the sample relative to the number of microbeads expected for the volume counted 100. Variation was measured as the CV among counts. Determining the plankton limits of detection and counting effort The minimum unit of plankton detection is 1 individual (ind.), and the limit of detection for manual counting is a function of the several volumes: the sample volume (S,m 3 ), the concentrated sample volume (C, ml)andthevolume of the concentrated sample that is analyzed (A, ml). Therefore, the lower limit of plankton detection (P LoD, ind. m 23 ) can be calculated by the following equation: P LoD ¼ 1 ind: C F S : ð3þ The fraction of the sample counted (F, no units) is the proportion of the concentrated sample analyzed (i.e. A/C) and can range from.0 to 1. Therefore, equation (3) can 1034

8 M.R. FIRST AND L.A. DRAKE j ACCURACY AND PRECISION OF PLANKTON ENUMERATION be simplified to P LoD ¼ 1 ind: F S : ð4þ Counting effort (E, min) is the total time required for counting and is a function of the counting rate (R, min ml 21 ) and analysis volume (A,mL). E ¼ R A: ð5þ Potential ranges of P LoD and E were calculated for a range of values of S (1 10 m 3 ), F (0.01 to 1) and A ( ml) using minimum and maximum counting rates that were measured empirically, from both the microbead analysis and the database of plankton counts. Contour plots of P LoD and E values were generated using SigmaPlot (V11.0; San Jose, CA, USA). Fig. 2. A histogram showing the counting rates (min ml 21 ) for 453 plankton samples analyzed between 2010 and RESULTS Sample characteristics and plankton counting rates The mean plankton counting rate was min ml 21 (range: min ml 21, n ¼ 453; Fig. 2). Differences in counting rates between observers were small (range of mean rates: min ml 21, n ¼ 3), but significantly different. To adjust for the small variations between observers, counting rates were normalized to the mean value for each observer and deemed relative counting rates. They were influenced by sample size, concentration factors and concentration of dead organisms (Table III). Drain samples from ballast water experiments without amended seawater had significantly higher relative counting rates (Fig. 3A). This subcategory included a large number of 30 m 3 samples (n ¼ 46) compared with drain samples with additives, in which only 17 samples represented volumes 30 m 3. Large sample volumes, generally, resulted in longer counting rates; counting rates were.30% higher for 30 m 3 samples compared with the other sample volumes (Fig. 3B). Relative counting rates for volumes 10 m 3 were not significantly different from each other. Low sample concentration factors (,500) resulted in significantly lower relative counting rates than higher concentration factors (P, 0.05, Fig. 3C). Numbers of plankton 50 mm in the counting chamber influenced relative counting rates. When there were.110 live organisms in the 4-mL counting chamber, relative counting rates were significantly higher (P, 0.05; Fig. 4A). Concentrations of dead plankton also influenced the relative counting rates, where samples with high concentrations of dead plankton (.15 ind. sample 21 ) had the longest relative counting rates (Fig. 4B). Significant differences in relative counting rates observed in the variable categories were Table III: ANOVA results of the relative counting rate (min ml 21 ) among categories of experimental variables; the relative counting rate is the counting rate normalized to each analyst s mean counting rate Variable Number of categories Total number of samples Critical F-value F Random data rearrangements (1000 simulations) Mean F sim Ballast sample type F (2, 450) ¼ (+0.94) Sample volume F (3, 449) ¼ (+0.82) Concentration factor F (2, 450) ¼ (+1.04) Live plankton 50 mm F (3, 449) ¼ (+0.92) Dead plankton 50 mm a F (3, 424) ¼ (+0.86) The categories examined for significant differences are described in Table I. Also shown here are the results of the randomized data set analysis in the right-hand column; F sim represents the F-value for the simulations. Relative counting rates were considered significantly different if the F-values (from experiments) or F sim values (from simulations) were greater than the critical F-values (P, 0.05); bold: significantly different comparisons. a Records without dead plankton counts (n ¼ 25) were removed prior to analysis. 1035

9 JOURNAL OF PLANKTON RESEARCH j VOLUME 34 j NUMBER 12 j PAGES j 2012 Fig. 3. Relative counting rates (i.e. counting rate normalized to each analyst s mean counting rate) of live plankton 50 mm for categories of sample variables. (A) Samples were collected from ballast tank fill operations ( Fill ; without amendments) and ballast tank drain ( Drain ) operations. Drain samples originated both from experiments without ( ) amendments used to meet target water conditions and experiments with (þ) amendments (see Table II). (B) Relative counting rates were grouped according to the total sample volume (3 to 30 m 3 ) and (C) according to the sample concentration factor. Bars show the mean and one standard deviation of the relative counting rates; sizes of the categories (n) are reported in Table I. Dotted lines represent the mean counting rates (1.0). Bars marked with different letters are significantly different (P, 0.05). not reproduced by 1000 random rearrangements of the data set (Table III). Determining counting accuracy using microbeads The average recovery of 50-mm microbeads in laboratory experiments with amended water was % (range: 75 90%; Fig. 5A). Undiluted water (0% dilution) had the highest concentration of debris, as well as the lowest microbead recovery rate (75%). However, microbead recovery rates were not significantly related to the dilution (linear regression, P. 0.05; data not shown). Greater than 92% of the 0.5 L sample was examined in all of the 50 mm microbead experiments. The recovery of 150-mm microbeads was.98% in both the undiluted (0% dilution) and control (100% dilution) amended water samples (Fig. 5B). Counting rates were significantly lower when counting 150-mm microbeads ( min ml 21, n ¼ 100) than in experiments counting 50-mm microbeads ( min ml 21, n ¼ 244; data not shown), and the entire volume was counted in both 150-mm microbead experiments. Fig. 4. Relative counting rates of plankton (i.e. counting rate normalized to each analysts mean counting rate) for several categories of variables. (A) Samples were categorized based upon the concentration of live plankton 50 mm in the 4 ml Bogorov counting chambers [individual (ind.) sample 21 ], and (B), dead plankton 50 mm. Bars show the mean and one standard deviation of the relative counting rates; sizes of the categories (n) are reported in Table I. Dotted lines represent the relative counting rate (1.0). Bars marked with different letters are significantly different (P, 0.05). Field experiments for microbead recovery, where microbeads were added to highly turbid samples of ballast water, had the lowest recovery rates (Fig. 5C) and the longest counting rates (data not shown). Counting rates were not significantly different between the two experiments ( and min ml 21 for samples representing 5 and 10 m 3 of ballast water volumes, respectively; data not shown). In both experiments, the concentrated sample was 1 L, and only 51 and 39% of the volume could be counted in the 12 h window for the 5 and 10 m 3 field experiments, respectively. Microbead recovery rates were similar for both 50-mm microbeads (36.5 and 32.5%) and 150-mm microbeads (73.5 and 72.9%) for the 5 and 10 m 3 experiments, respectively. Counting precision was measured by comparing the number of microbeads recovered from each Bogorov chamber (10 ml sample volume) to the number of microbeads expected in that volume assuming that the entire sample was well mixed before each 10 ml aliquot was removed and analyzed. In the laboratory samples, with the exception of the 0% dilution sample with 50-mm microbeads, the expected count was within the inter-quartile range of the observations (represented by the bars in Fig. 6). The smallest inter-quartile ranges were observed in the field experiments, where the concentration estimates were furthest from expected values. In the laboratory experiments, microbead counts agreed closely between analysts (data not shown). 1036

10 M.R. FIRST AND L.A. DRAKE j ACCURACY AND PRECISION OF PLANKTON ENUMERATION Fig. 5. Microbead recovery (bars) and volume analyzed (black circles) for both laboratory samples and field samples using 50- and 150-mm microbeads. The percent dilution ranged from 0% (no dilution) to 100% (control samples) for both 50-mm (A) and 150-mm (B) diameter microbeads in laboratory experiments ( prepared as in Fig. 1; Table II). Microbead recovery and volume analyzed from two field experiments (C), with samples volumes 5 and 10 m 3 ; both 50- and 150-mm microbeads were added to the concentrated samples prior to analysis. White bars ¼ 50-mm microbeads; gray bars ¼ 150-mm microbeads. Differences were,5% for 50-mm microbead samples (n ¼ 33 samples, representing 330 ml of sample water); total counts were equal for 150-mm microbeads (n ¼ 10 samples representing 100 ml). Differences between analysts were larger in the field experiments: microbead counts differed by 56% and 40% for 50-mm and 150-mm microbeads, respectively (data not shown). The concentration of TSS in a 5 m 3 sample collected from the second field experiment (representing the mm size fraction) was mg L 21. POC was 40% of TSS. Determining the plankton limits of detection and counting effort Because large sample volumes (30 m 3 ) resulted in significantly higher counting rates than smaller samples volumes, only sample volumes 1 10 m 3 were considered when calculating the range of plankton limits of detection (Fig. 7). The plot, generated with equation (4), Fig. 6. Microbeads observed in each 10 ml sample from a large (15 ml capacity) Bogorov counting chamber compared with the number of microbeads expected. The percent difference from the expected value is shown for laboratory samples using (A) 50-mm microbeads (n ¼ 46 51; the number of analytical replicates from a single sample), (B) 150-mm microbeads (n ¼ 50 for both samples) in laboratory experiments (samples prepared as in Fig. 1 and amended according to Table II) and (C) microbeads with both diameters in field samples, with the number of 10 ml analytical replicates of 39 and 51 for 5 and 10 m 3 total sample volumes, respectively. The dotted line represents no difference from expected values. Outer error bars of the box plot show the 10th and 90th percentiles of the data; the bar boundaries show the 25th and 75th percentile values and the median value is marked by a vertical line in the bar. The inter-quartile range is represented by the distance within the bars (i.e. between the 25th and 75th percentiles). The white diamond with error bars show the mean and one standard error of the counts. White bars ¼ 50 mm microbeads; gray bars ¼ 150 mm microbeads. demonstrates two optimal situations for lowering limits of detection: (i) counting large portions of small (i.e.,3 m 3 ) sample volumes, and, (ii) collecting large sample volumes (10 m 3 ), but analyzing only a small portion of the concentrated sample. For example, analyzing all (fraction analyzed ¼ 1.0) of a 3 m 3 sample and a small portion (fraction analyzed ¼ 0.3) of a 10 m 3 sample both yield a limit of the detection of 0.33 ind. m 23. Counting effort (i.e. total time spent analyzing the sample) is a function of the mean counting rate and the volume of the concentrated sample (ranging from 50 to 600 ml). As counting rates approach the relatively long mean counting rates measured for field plankton samples (4.9 min ml 21, counting 75 ml exceeds the 6 h time window for the analysis of live samples (Fig. 8). In contrast, a large volume of sample (.600 ml) can be analyzed in short periods as the counting rate approaches 1037

11 JOURNAL OF PLANKTON RESEARCH j VOLUME 34 j NUMBER 12 j PAGES j 2012 Fig. 7. Contour plot showing limits of detections for live plankton 50 mm (P LofD ; Ind. m 23 ) along a range of total sample volumes and fractions of the concentrated sample analyzed. Values are calculated based on equation (4). the minimum value (0.2 min ml 21 ), which was achieved in the microbead counts of laboratory samples. DISCUSSION Accurate estimations of in situ plankton concentrations are critical for understanding key processes in oceanic food webs, e.g. rates of primary consumption and food availability to higher trophic levels. New and emerging approaches for estimating plankton concentrations have been adopted to overcome the limitations of manual counting, such as lengthy analysis times and variation between analysts. Unfortunately, these techniques are currently not applicable to analysis of plankton in ballast water discharge because proposed international and national discharge standards specify limits on the concentration of living organisms in a specific size class. Thus, in the largest size class, organisms should be directly counted, and distinguished as live or dead, and only organisms 50 mm in minimum dimension should be included in the tally (International Maritime Organization, 2004; U.S. Coast Guard, 2010). The analyst must interact with the samples (i.e. probe nonmoving zooplankters to stimulate movement and determine viability) and, to our knowledge, equipment for rapidly performing these complex tasks is not readily available or easily developed. Automated approaches, if refined to address these constraints, could be applied to Fig. 8. Contour plot showing counting effort (i.e. total analysis time, h) along a range of concentrated sample volumes and the range of counting rates observed in this study from the relatively short microbead counting rates to the longer mean plankton counting rates ( min ml 21, respectively). Values are calculated based on equation (5). examine ballast water discharge or evaluate the performance of BWMSs. For example, flow-through imaging cytometers are capable of counting and sizing plankton and have fluorescence sensors that may be used to measure vital fluorescent labels (Buskey and Hyatt, 2006; Olson and Sosik, 2007). Advances in object identification and classification (e.g. Sosik and Olson, 2007) demonstrate the promise that automated techniques may have for plankton detection in ballast water samples. Until these technologies are in place and validated, manual counting is the most appropriate approach to count and categorize live organisms 50 mm. Approaches to minimize counting rate Several approaches can accelerate the counting rate of live organisms within ballast water discharges. First, higher concentration factors can increase the volume of the sample analyzed over time. For example, if a 1 m 3 sample is concentrated to 50 ml and analyzed without dilution, the entire volume could be counted in 4 h. Concentrating the sample only 2000 to 500 ml would require 40 h of analysis time to completely count the entire sample. However, counting live plankton has the additional challenge that organisms occasionally migrate across the field of view, potentially leading to erroneously high counts. In practice, these moving organisms are tallied and are subtracted from 1038

12 M.R. FIRST AND L.A. DRAKE j ACCURACY AND PRECISION OF PLANKTON ENUMERATION the total count. However, with excessive movement, it becomes difficult to simultaneously count and identify plankton (as most analysts do) while tracking movement. In these situations, it is necessary to dilute the samples prior to counting. Second, using lower magnifications increases the volume of water visible in the field of view and expands the depth of field. This partially explains why using larger volume Bogorov chambers (used to count microbeads) resulted in shorter counting rates. Therefore, using the minimum magnification necessary to view target organisms (and a counting chamber optimized for the chosen magnification) will reduce counting rates. However, at magnifications,20, small organisms are difficult to detect, identify and manually track. Additionally, gently touching non-moving individuals is increasingly challenging at low magnifications. Relative counting rates were affected by several sample characteristics. Notably, sample volumes 30 m 3 had the longest counting rates. The longer counting rate is likely a function of increased debris, which can obscure organisms and require increased effort searching through detritus and aggregated particles. However, the increased detection rate could also be influenced by the low activity level of plankton. During lengthy sample collections (e.g..60 min), pressure in the filter skid housings increases (First et al., 2012). Lengthy exposures to high pressure differentials (and other stresses during sampling) may not kill the organisms but can potentially lead to low states of activity, which requires manual prodding of motionless individuals. The repeated process of locating an inactive organism, stimulating motion by prodding and waiting 10 s to observe the response results in longer analysis times. This extra interrogation time contributes to the longer overall counting rates for samples with high concentrations of dead organisms. Unless the BWMS has a filter to remove organisms or uses an active substance that destroys organisms, treated ballast water discharge would be expected to contain high concentrations of dead plankton. The requirement to detect low concentrations of live plankton will necessitate large sample volumes. The convergence of these processes will, unfortunately, result in lengthy analysis times. The plankton limit of detection is a function of the sample size, the volume of the concentrated sample and the fraction of the concentrated sample that is counted. Analysis time is primarily a function of the quantity of the concentrated sample that is counted. Increasing the total sample size, reducing the volume of concentrated sample or both are appealing approaches that will lower the limit of detection, as these do not directly lead to longer analysis times. However, the problems of increasing concentration factors are demonstrated in the microbead field experiments. In these experiments, extraordinarily high concentrations of POC were collected in the 50-mm size fraction. The processes leading to the high concentrations of POC were unclear, but it is likely that microbial processes by ambient bacteria (e.g. assimilation of DOC, flocculation) generated high detrital loads in the sample (Alldredge and Gotschalk, 1990). In the typical protocol, very turbid samples would be diluted prior to analysis (so that plankton are not obscured by debris). Dilution was not performed on these samples, as the goal of the microbead recovery experiments was to examine the recovery rates under the most challenging conditions. Similar challenging conditions may be common in ballast water samples, and the low recovery of microbeads demonstrates that highly concentrated samples (with high debris concentrations) can likely underestimate in situ concentrations of plankton. In contrast to the field experiments, higher recovery rates of microbeads were observed in using laboratory samples. Amendments of POC and TSS were poorly retained in the filter bags, as the microbial processes of DOC assimilation and particle aggregation that occurred in the ballast tank during the 5-day hold time did not have time to generate high concentrations of particulate matter. Consequently, much of the added particulates were not retained, as the samples were concentrated in a filter bag. Recovery rates were close to 100% for 150-mm microbeads (e.g. 523 of 529 microbeads were detected in the 10 m 3 field experiment). These high recovery rates demonstrate: (i) larger objects are counted with a very high accuracy, (ii) low detrital concentrations improve detection, even of non-moving objects (such as microbeads) and (iii) the human counting machine can, under optimal sample conditions, yield very accurate and rapid counts of plankton proxies (microbeads) in complex samples. Counting effort and limits of plankton detection Although well-trained analysts can generate accurate plankton counts, allocating the personnel time to count large volumes of complex samples (i.e. mixed communities of live and dead organisms, composed of multiple taxa along a range of body sizes) may not be feasible. If analysis should be completed within 6 h of the sampling event, only 75 ml of the sample can be counted (on average). This estimate includes neither the preparation time nor accounts for the slowing of counting rates as analysts experience fatigue during lengthy counting events. Counting large samples (0.5 1 L) would require a team of analysts to be assembled and ready to begin analysis when the sampling is complete. Therefore, analyzing large volumes may not be practical. 1039