Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations

JGLR-01149; No. of pages: 11; 4C: Journal of Great Lakes Research xxx (2016) xxx–xxx

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Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations Sara M. Thomas a,⁎, John H. Chick b, Sergiusz J. Czesny a a b

Lake Michigan Biological Station, Illinois Natural History Survey, Prairie Research Institute, University of Illinois, 400 17th St, Zion, IL 60099, USA Great Rivers Field Station, Illinois Natural History Survey, Prairie Research Institute, University of Illinois, Alton, IL 62002, USA

a r t i c l e

i n f o

Article history: Received 16 June 2016 Accepted 20 October 2016 Available online xxxx Communicated by William D. Taylor Keywords: Rotifers Microzooplankton Sampling methods Great Lakes Crustacean zooplankton

a b s t r a c t Microzooplankton (rotifers, copepod nauplii, and dreissenid veligers) are an important but overlooked part of zooplankton communities and aquatic food webs, particularly in the Great Lakes. Most studies that do include microzooplankton data are not describing the full picture due to inappropriate sampling methodology. We compared the traditional macrozooplankton sampling method (64-μm mesh plankton net) to a microzooplankton method using a 20-μm mesh screen in various habitats in Lake Michigan. The macrozooplankton method significantly underestimated total rotifer density by an order of magnitude, veliger density by nearly an order of magnitude, and copepod nauplii density by threefold. Combining macrozooplankton method estimates for cladocerans and copepods with estimates of rotifer, nauplii, and veligers from the microzooplankton method samples showed rotifers contributed 51% of total mean zooplankton biomass, refuting the past notion that rotifers contribute little to overall zooplankton biomass. Our study demonstrates that the traditional one-size fits all sampling approach used in the majority of zooplankton monitoring studies in the Great Lakes significantly underestimates microzooplankton abundance and its relative importance. Biassed information on Great Lakes zooplankton community composition has ramifications beyond a basic understanding of Great Lakes food webs. The lack of accurate data on microzooplankton abundance suggests that prey resources available to Asian carp in Lake Michigan have been greatly underestimated along with the likelihood these invasive species could become established. The dual sampling approach must become the norm rather than the exception for zooplankton research in the Great Lakes and other freshwater systems. © 2016 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Introduction Microzooplankton may be defined as 20 to 200 μm in size (Sieburth and Smetacek, 1978) and include rotifers, copepod nauplii, and Dreissena spp. veligers. These microzooplankton are an important part of planktonic communities and aquatic food webs. They play important roles in nutrient cycling and energy transfer within ecosystems, with some species consuming algae, protozoans, and bacteria, whereas others are intra-guild predators (Makarewicz and Likens, 1979; Segers, 2008). Microzooplankton are consumed by other zooplankton, larval fish, and filter feeding fish, and compete with cladocerans for the same food resources (Brandl, 2005; Stemberger and Gilbert, 1987). Rotifers also have high growth rates, quick population turnover, high dispersal rates, and parthenogenetic reproduction (Segers, 2008; Stemberger, 1995). Despite all this, rotifers and other microzooplankton are often overlooked, likely because of their small size (Chick et al., 2010; Segers, 2008). ⁎ Corresponding author at: Michigan Department of Natural Resources, Waterford Fisheries Station, 7806 Gale Rd., Waterford, MI 48327, USA. E-mail address: [email protected] (S.M. Thomas).

The majority of plankton studies focus only on crustacean zooplankton, here referred to as macrozooplankton, and do not include data on rotifers or dreissenid veligers (Orcutt and Pace, 1984; Pace and Orcutt, 1981), particularly in the Laurentian Great Lakes (Evans, 1986; Nauwerck, 1978; Pothoven and Fahnenstiel, 2015). For example, although data exist documenting changes in Great Lakes crustacean zooplankton populations since arrival of invasives such as alewife (Alosa psuedoharengus), zebra mussels (Dreissena polymorpha) and quagga mussels (Dreissena rostriformis bugensis) (Madenjian et al., 2002, 2015; Wells, 1970), comparable data are lacking for microzooplankton (Lavrentyev et al., 2014; Vanderploeg et al., 2012). Several researchers have suggested that invasive Cercopagis pengoi, a predatory cladoceran, may negatively affect rotifer abundance and/or composition in Lakes Ontario and Michigan (Barbiero and Warren, 2011; Benoît et al., 2002; Witt et al., 2005), but other trophic changes and the lack of targeted rotifer data make this hard to untangle. Those studies that do focus on and present data for rotifers and other microzooplankton are likely not describing the full picture due to sampling and laboratory/counting methodology. Several older (Bottrell et al., 1976; Likens and Gilbert, 1970; Makarewicz and Likens, 1979; Orcutt and Pace, 1984) and more recent papers (Chick et al., 2010)

http://dx.doi.org/10.1016/j.jglr.2016.11.002 0380-1330/© 2016 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Please cite this article as: Thomas, S.M., et al., Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations, J. Great Lakes Res. (2016), http://dx.doi.org/10.1016/j.jglr.2016.11.002

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S.M. Thomas et al. / Journal of Great Lakes Research xxx (2016) xxx–xxx

documented the need for specific field and laboratory methodology, including fine mesh sizes (e.g., 20–35 μm) and enumeration with compound microscopes, to accurately document rotifer abundance. However, there has not been a concerted effort by scientists to adopt these methods. Chick et al. (2010) conducted a literature review and found that only five out of 23 papers presenting quantitative rotifer data used mesh sizes b 35 μm. We conducted a similar search, concentrating on the Great Lakes, and found similar results (with no overlap between studies presented). We used Biological Abstracts BIOSIS to search for articles published from 1973 to 2015 with the phrase “rotifer” and “Great Lakes” (and each lake individually named) in the topic search field. We were able to access 18 of 19 articles that directly related to quantitative sampling in open water; we also included eight additional papers detected through other search methods. Only five of these 26 studies used mesh sizes ≤ 35 μm in their methodology. The most commonly used mesh sizes were from 62 to 64 μm, and three studies even presented rotifer data when sampling with ≥ 110 μm mesh (Table 1). Using large mesh sizes to sample zooplankton is not only problematic for rotifers, but also for copepod nauplii and dreissenid veligers (Chick et al., 2010; Makarewicz and Likens, 1979). The lack of accurate data on veligers, nauplii, and rotifers in the Great Lakes is particularly concerning given the strong risk of introduction of bighead carp (Hypophthalmichthys nobilis) and silver carp (Hypophthalmichthys molitrix) (Asian carps; collectively Asian carp) into the Great Lakes. Asian carps are filter feeders that can ingest very small particles; bighead carp routinely consume items as small as 50 μm in size, whereas silver carp can consume items as small as 4–17 μm (Cremer and Smitherman, 1980). Asian carps feed on detritus, phytoplankton, and both micro- and macro-zooplankton and both species can switch primary prey types depending on environmental availability (Cremer and Smitherman, 1980; Dong and Li, 1994; Sampson et al., 2009). Within the Mississippi and Illinois Rivers, Sampson et al. (2009) used a compound scope to examine diet contents and found rotifers made up the majority of zooplankton taxa consumed by both silver and bighead carp; both species also consumed copepods and cladocerans, with nauplii comprising 5% of bighead carp diets by number. Williamson and Garvey (2005) looked at silver carp diets in the middle Mississippi River using two different methods; they used chlorophyll a extraction to approximate phytoplankton consumed and quantified zooplankton by counting subsamples with a dissecting scope.

They found much higher concentrations of chlorophyll a compared to zooplankton, but rotifers accounted for 37–69% wet weight of zooplankton consumed. Lack of accurate estimates of microzooplankton density and biomass creates a distinct data gap that will hinder reliable assessments of the ability of Asian carp to invade Lake Michigan. For example Cooke and Hill (2010) ran bioenergetics models and suggested that low abundance of zooplankton and phytoplankton might pose a barrier to invasion of Asian carp in most open waters of the Great Lakes. However, the authors noted that data on rotifers were missing from most zooplankton data sources used in their modeling efforts. Those Great Lakes sources that included rotifers used methods (i.e., mesh size ≥ 63 μm) that have been shown to underestimate rotifer abundance by two to three orders of magnitude in other freshwater ecosystems (Chick et al., 2010). Given that rotifers and other microzooplankton are important prey items of Asian carp, the lack of accurate data on their density in Lake Michigan raises serious questions about our ability to accurately predict whether the Lake Michigan food web could sustain populations of Asian carp and how these invasive species might alter Lake Michigan's food web. The need for accurate data on rotifers is not limited to assessing impacts from invasive filter feeding species. Because rotifers feed on bacteria, flagellates, and ciliates, in addition to phytoplankton, they form an important link between microbial resources and the metazoan community of aquatic food webs. Our objective in this study was to compare density and biomass estimates of rotifers, copepod nauplii, and dreissenid veligers from two sampling methodologies in Lake Michigan. We chose to focus on these particular metazoan microzooplankton only, because other types of microzooplankton such as ciliates are consistently studied with specialized sampling techniques (Fahnenstiel et al., 1998). Studies in other freshwater systems have consistently found that mesh sizes N 35 μm underestimate metazoan microzooplankton abundance, but the magnitude of this underestimation varies from study to study. We hypothesized that in Lake Michigan, the traditional method of using a 64-μm mesh plankton net would provide estimates of microzooplankton density and biomass that were significantly lower compared to whole water samples filtered through a 20-μm mesh. Methods Field sampling

Table 1 Mesh sizes used to provide quantitative estimates of rotifer density in the Great Lakes and Lake St. Clair from published studies. Eighteen studies were derived from a Biological Abstracts search of articles published from 1973 to 2015 with the phrase “rotifers and Great Lakes/individual Lake names” in the topic search field. * Additional papers addressing rotifers as a main focus but not detected in this search. † Citations noting use of a compound scope for rotifer identification. ◊ Two studies that used dual mesh sizes for rotifers, giving a total of 26 individual references.

Mesh sizes (μm)

# of studies Citations

≤35

5

N35 to b62

3

62–64

13

73–76

3

≥110

3

Not 1 reported/unclear

Mazumder et al. (1992); Fahnenstiel et al. (1998)†; Johannsson et al. (2000); Bowen and Johannsson (2011)*; Lavrentyev et al. (2014)† Stemberger (1974)†*; Stemberger et al. (1979)†*; Sprules and Goyke (1994)◊ Duffy and Liston (1978)†*; Nauwerck (1978); Evans (1986); Makarewicz (1991)†*; Makarewicz (1993); Makarewicz et al. (1995)*; Johnson et al. (2004); Barbiero and Warren (2011)†; Barbiero et al. (2012)†*; George et al. (2013)◊; Thomasen et al. (2013)*; O'Malley and Bunnell (2014)†; Makarewicz and Lewis (2015)† Stemberger and Evans (1984)†; MacIsaac et al. (1995); Witt et al. (2005) Sprules and Munawar (1991); Sprules and Goyke (1994)◊; George et al. (2013)◊; Heath et al. (2003)

A total of 81 paired plankton samples were collected at 43 locations in Lake Michigan's open water, harbor, and drowned river mouth habitats during May 9–October 1, 2012 (Fig. 1). Twenty open water and three harbor sites were in Illinois, three open water sites were in Indiana, one harbor and two open water sites were located in Wisconsin, and six open water and eight harbor/drowned river mouth sites were in Michigan. The majority of sites were sampled on only one occasion, but seven sites have samples from three or more sampling dates. Sampling in the nearshore of the open lake occurred at a water depth range of 5–10 m and samples in harbors and drowned river mouths were generally from depths of 5 m or less. At each location, two different methods were used to sample zooplankton. The first, referred to hereafter as the microzooplankton method, used a 2.5 L Van Dorn water sampler to collect water from each meter in the water column down to 0.5–1.0 m off the bottom. The entirety of the individual Van Dorn samples were integrated in a bucket and all water collected was then filtered through a 20-μm mesh sieve. The second method (hereafter the macrozooplankton method), is commonly used in zooplankton studies (Table 1), and consists of vertical net hauls with mesh sizes ranging from 54 to 158 μm. The macrozooplankton sample was collected concurrently using a 64-μm mesh, 0.5 m diameter, 2.0 m length conical plankton net lowered to 0.5 m above the bottom and steadily pulled up through the water column. Plankton nets were not metered and water volume sampled was calculated using area of

Please cite this article as: Thomas, S.M., et al., Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations, J. Great Lakes Res. (2016), http://dx.doi.org/10.1016/j.jglr.2016.11.002

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Fig. 1. Map of zooplankton sampling locations in harbors, drowned river mouths and open waters of Lake Michigan. Shading of circles indicating sample locations corresponds to number of times location was sampled during 2012. Inset map shows location of Lake Michigan in North America.

the net opening and the depth of water sampled (Davis, 1969; Taylor et al., 1987; MacIsaac et al., 1995). Samples were rinsed into storage bottles and narcotized with an antacid tablet before 4% sucrose-buffered formalin preservative was added. Rose Bengal stain was also added to aid in identification. Laboratory processing Samples were identified and counted using several different methods depending on the taxa being counted. Microzooplankton samples were not examined for cladocerans and adult copepods because this was previously shown to be a less reliable capture method for these larger organisms due to the reduced water volume filtered (Chick et al., 2010). Identification and enumeration of nauplii, veligers, and rotifers was done using a compound scope at 100× magnification and Sedgwick-Rafter counting cells for both the micro- and macro-

method samples. Samples were enumerated in their entirety or subsampled until a minimum of 400 individuals were counted after a complete subsample. Rotifers counted under the compound scope were identified to genus using a variety of sources (Grothe and Grothe, 1977; Stemberger, 1979; Wallace and Snell, 2010). Veligers and nauplii were not classified further. Mean length of the microzooplankton taxa was obtained from measurements of 30 individuals of each taxa from digital camera pictures, taken at 100× magnification on the compound scope, and imaging software. All zooplankton taxa were also enumerated from the vertical tow macrozooplankton samples using identical methods to past years (Creque and Czesny, 2012). These samples were counted under a dissecting microscope at 25× magnification using a Ward Whipple wheel to examine three 5-mL subsamples, taken from adjusted volumes that provided a count of at least 20 individuals of the most dominant taxa. For the dissecting microscope counts, zooplankton were enumerated

Please cite this article as: Thomas, S.M., et al., Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations, J. Great Lakes Res. (2016), http://dx.doi.org/10.1016/j.jglr.2016.11.002

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and identified into the following categories: cyclopoid copepodites, calanoid copepodites, copepod nauplii, cladocerans to genus (Daphnia to species), rotifers, and Dreissena spp. veligers using available keys (Balcer et al., 1984; Dodson et al., 2010; Reid and Williamson, 2010). Due to the low magnification, rotifers were not identified to genus. For each macrozooplankton sample, up to twenty length measurements were taken on each crustacean taxon using imaging software and a digitizing pad or digital camera. Microzooplankton counts from the dissecting scope were used only to compare the reliability of enumerating these smaller zooplankton taxa at lower power versus the higher power of the compound scope. Data analysis We limited our analyses of rotifer taxa to those that accounted for at least 5% of total density for the microzooplankton method. Biomass (dry weight μg L−1) of all zooplankton taxa was calculated using our mean length values for each taxon and published length-weight regressions or reported average dry weights (Burkhardt, 1994; Culver et al., 1985; Downing and Rigler, 1984; Dumont et al., 1975; Hawkins and Evans, 1979; Ojaveer et al., 2001; Sprung, 1993). Density and biomass data were log-transformed (log10(x + 1)) prior to all analysis to reduce heteroscedasticity and to better conform to the linearity assumption of regression. We calculated mean density and biomass of the major taxa groups (cladocerans, copepods, rotifers, nauplii, and veligers) and six rotifer genera comprising ≥5% of the total microzooplankton across all 81 sample pairs. We tested for differences in mean density and biomass between the two methods for all taxa groups except cladocerans and copepods, which were not counted from microzooplankton samples. We considered mean density or biomass for taxa to differ significantly between the methods if there was no overlap in 95% confidence intervals. We used this conservative measure to avoid errors associated with running multiple ANOVAs and/or paired t-test. We also calculated percent composition for the major taxa groups by density and biomass for both methods. Simple linear regressions were run to examine the relationships between density estimates of the two sampling methods using all 81 sample pairs (Table 2). We expected more accurate microzooplankton density data from the microzooplankton method; therefore, density estimates from this method were used as the independent variable in regressions for rotifers, copepod nauplii, and veligers. Estimates derived from the macrozooplankton method were used as the dependent variable. Differences in density estimates between the two methods would result in slopes significantly different from one and slopes b 1 indicated underestimation by the macrozooplankton method. We also ran simple linear regressions, as well as paired sample t-tests, to compare the accuracy of microzooplankton counts between low power (25 ×)

and high power (100×) magnification for the macrozooplankton samples. Density was (log10(x + 1)) transformed and density at 25× was the independent variable and 100× the dependent variable. Results A total of 81 paired samples from 43 separate sites were collected during the 2012 field season. In samples collected with the macrozooplankton method, rotifers (all genera) were most abundant, followed by veligers and cladocerans. Polyarthra was the most abundant rotifer taxa, with a mean density almost four times higher than Keratella and Synchaeta. In microzooplankton method samples, rotifers were also dominant, and Polyarthra was again four times higher than the next most abundant rotifer taxa: Keratella and Synchaeta (Table 2). Veligers were also relatively abundant in the microzooplankton samples, while copepod nauplii density and biomass were among the lowest of the most common taxa. Mean lengths of microzooplankton collected with the microzooplankton method ranged from a low of 106 ± 4 μm for Polyarthra to a high of 198 ± 9 μm for Synchaeta (Table 2). Mean lengths of veligers were similar between methods, whereas nauplii lengths were significantly larger in macrozooplankton method samples compared to microzooplankton method samples. As expected, sampling zooplankton with the macrozooplankton method greatly underestimated the density of microzooplankton taxa. Significant regression relationships for density (p b 0.001) were found between the two sampling methods when counted under a compound scope for all rotifers (R2 = 0.61), veligers (R2 = 0.62), and (nauplii R2 = 0.76) (Fig. 2). All slopes were less than one and y intercepts were negative, indicating the microzooplankton method was more efficient at collecting individuals (Table 2). Nauplii were closer to a 1:1 relationship compared to the other two taxa, which fell further below that line likely as a result of passing through the 64-μm mesh more often. The macrozooplankton method significantly underestimated total rotifer density by an order of magnitude, veliger density by nearly an order of magnitude, and copepod nauplii density by three fold (Table 2). This also translated to significantly higher microzooplankton biomass estimates using the microzooplankton method. The largest difference was for all rotifers combined, with biomass almost nine times higher using the microzooplankton method. Of the six most abundant rotifer taxa, all showed significantly greater density and biomass when using the microzooplankton sampling method compared to the macrozooplankton net. The most striking difference was for Polyarthra, whose estimated density and biomass was approximately two orders of magnitude higher in microzooplankton samples (Table 2). Density and biomass of both Keratella and Synchaeta were approximately seven times higher in microzooplankton samples. Tylotrocha was detected in only two macrozooplankton samples (Table 3), yet it was found in 36 microzooplankton samples and was

Table 2 Mean body length, density, and dry biomass for rotifers, copepod nauplii, cladocerans, and adult and juvenile copepods estimated from the macrozooplankton (64 μm) and microzooplankton (20 μm) methods used to sample zooplankton from Lake Michigan. Cladocerans and copepods were not quantified or measured using the microzooplankton method. In addition, rotifers were not measured in macrozooplankton samples. Also shown is the slope of the log-log regression of density between sampling methods. Numbers in parentheses show standard error of the mean or slope. *Only two macrozooplankton method samples contained Tylotrocha. Mean length (μm)

Mean density (# L−1)

Mean biomass (μg L−1)

Taxa

Macro

Micro

Macro

Micro

Macro

Micro

Slope log-log

Dreissena spp. veligers Copepod nauplii All rotifers Polyarthra spp. Keratella spp. Synchaeta spp. Trichocerca spp. Tylotrocha spp. Ploesoma spp. All cladocerans All adult & juv. copepods

144 (6) 199 (2)

136 (7) 140 (7)

15.1 (2.9) 5.9 (1.3) 39.3 (7.7) 5.1 (2.9) 10.2 (2.1) 9.6 (1.5) 1.8 (1.1) b0.01 1.9 (0.5) 8.6 (3.5) 2.3 (0.5)

97.7 (17.0) 23.2 (4.5) 579.9 (89.3) 329.6 (58.1) 71.7 (25.1) 71.5 (13.4) 40.3 (32.7) 11.8 (5.4) 10.9 (4.9)

3.4 (0.6) 1.0 (0.2) 5.1 (1.3) 0.4 (0.2) 0.7 (0.1) 0.7 (0.1) 0.1 (0.05) b0.01 0.1 (0.03) 15.6 (10.1) 6.7 (2.4)

18.9 (3.3) 2.4 (0.5) 44.8 (7.5) 23.1 (4.1) 5.0 (1.8) 5.0 (0.9) 2.0 (1.6) 0.6 (0.3) 0.8 (0.3)

0.713 (0.062) 0.769 (0.048) 0.680 (0.060) 0.248 (0.043) 0.535 (0.058) 0.452 (0.044) 0.303 (0.047) Not applicable* 0.442 (0.045)

106 (4) 161 (7) 198 (9) 187 (4) 121 (5) 179 (13) 496 (9) 564 (4)

Please cite this article as: Thomas, S.M., et al., Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations, J. Great Lakes Res. (2016), http://dx.doi.org/10.1016/j.jglr.2016.11.002

S.M. Thomas et al. / Journal of Great Lakes Research xxx (2016) xxx–xxx

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Table 3 Rotifer genera present in 2012 sampling using both the micro- and macro-methods, along with the number of samples out of 81 they were found in. Nauplii were found in all 81 samples for each method. Bolded taxa are those found in at least 50% of all microzooplankton method samples.

a. Y = -0.398 + 0.680 X R2=0.61; P < 0.001

3

2

1

-1

Macrozooplankton Method Density (log10 # L +1)

0 4

0

1

2

3

4

b. Y = -0.172 + 0.768 X R2=0.76; P < 0.001

3

2

1

0 0 4

1

2

3

4

c. Y = -0.321 + 0.713 X R2=0.62; P < 0.001

3

2

1

0 0

1

2

5

3

4 -1

Microzooplankton Method Density (log10 # L +1) Fig. 2. Relationship of log10 density + 1 (# L−1) for a. rotifers, b. copepod nauplii, and c. Dreissena spp. veligers quantified using the microzooplankton method (independent variable) and the macrozooplankton method (dependent variable) indicated by the dashed line. Solid line is a 1:1 relationship. All samples were counted under a compound microscope at 100× for each sample pair.

the fifth most abundant rotifer taxon we collected (Table 2). For the other five most abundant taxa, all had a significant correlation between the two sampling methods, although R2 values varied between 0.29 and 0.55 (Fig. 3). The weakest relationship (R2 = 0.29) was for Polyarthra with a slope value of 0.248, indicating that the macrozooplankton

Genera

# of microzooplankton method samples

# of macrozooplankton method samples

Anuraeopsis Ascomorpha Asplanchna Bdelloid Brachionus Cephalodella Collotheca Colurella Conochilus Encentrum Euchlanis Filinia Gastropus Kellicottia Keratella Lecane/Monostyla Lepadella Notholca Platyias Ploesoma Polyarthra Pompholyx Synchaeta Testudinella Trichocerca Trichotria Tylotrocha Dreissena sp. veliger

7 59 32 10 11 8 29 24 45 6 2 2 49 31 77 21 8 3 1 67 81 3 79 2 60 17 36 81

2 45 55 9 16 1 31 1 59 0 4 2 53 46 77 4 0 5 0 72 70 2 77 1 52 27 2 80

method is very ineffectual at sampling these smallest of the rotifer taxa. Slope values for larger taxa (Ploesoma, Synchaeta, and Keratella) ranged from 0.44 to 0.54, indicating that the microzooplankton method caught approximately ten individuals for every one captured by the macrozooplankton method for these taxa (Fig. 3). The two sampling methods differed not only in total density and biomass of rotifers, but also by frequency of occurrence for rotifer genera. A total of 27 different rotifer genera were found in our samples using the microzooplankton method; three were rare taxa not detected in macrozooplankton samples (Table 3). Polyarthra, veligers, and copepod nauplii were found in every microzooplankton sample collected, and an additional 7 rotifer taxa were found in N 50% of samples. The three most frequently encountered rotifer genera were the same regardless of sample method. For the macrozooplankton method, only copepod nauplii were found in every sample, with an additional nine taxa found in N50% of samples (Table 3). There were no genera collected in macrozooplankton samples that were not found in microzooplankton samples. Frequency of occurrence was higher for sixteen taxa in microzooplankton samples; of these, Tylotrocha, Collurella, and Lecane differed the most between methods, being collected in 42%, 28% and 21% fewer samples, respectively, using the macrozooplankton method. Ten taxa had higher frequency of occurrence in macrozooplankton samples compared to the microzooplankton samples; Asplanchna was found in 28% fewer microzooplankton samples and Kellicottia was detected in 19% fewer microzooplankton samples. Total density and biomass of all zooplankton (crustaceans, rotifers, and veligers) were calculated using estimates from only the macrozooplankton net and a combination method using macrozooplankton method estimates for cladocerans and copepods, and estimates of rotifers, nauplii, and veligers from the microzooplankton method samples. Across all our sampling locations, total zooplankton density was an order of magnitude higher when using the combined method (711.6 ± 99.6 L−1) compared to macrozooplankton method only (71.9 ± 11.8 L−1). Cladocerans and copepods accounted for 15% of

Please cite this article as: Thomas, S.M., et al., Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations, J. Great Lakes Res. (2016), http://dx.doi.org/10.1016/j.jglr.2016.11.002

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Polyarthra spp.

Keratella spp. 3

Y = -0.128 + 0.248 X R 2 = 0.29; P < 0.001

3

Y = 0.018 + 0.535 X R 2= 0.51; P < 0.001

2 2

1

-1 Macrozooplankton Method Density (log10 # L +1)

1

0

0 0

1

2

3

4

0

Synchaeta spp. 3

1

2

3

Trichocerca spp. 3

Y = 0.044 + 0.452 X R 2 = 0.31; P < 0.001

2

2

1

1

Y = -0.036 + 0.303 X R 2 = 0.30; P < 0.001

0

0 0

1

2

0

3 3

0.4

1

2

3

Ploesoma spp.

Tylotrocha spp.

Y = 0.046 + 0.442 X R 2=0.55; P < 0.001

0.3 2 0.2 1 0.1

0

0.0 0

1

2

3

0

1

2

3

-1

Microzooplankton Method Density (log10 # L +1) Fig. 3. Relationship of log10 density + 1 (# L−1) for the six most abundant rotifer taxa quantified using the microzooplankton method (independent variable) and the macrozooplankton method (dependent variable). All samples were counted under a compound microscope at 100×.

total density using the macrozooplankton method samples, but were b2% of the total density when using the combined method (Fig. 4). Rotifers and veligers together accounted for 76% of zooplankton density using the macrozooplankton method, but accounted for 95% of zooplankton density with the combined method. Mean biomass was 31.8 ± 12.9 μg L−1 from macrozooplankton method samples and 88.4 ± 17.6 μg L−1 for the dual method (Table 4). Cladocerans contributed 49% to total zooplankton biomass in macrozooplankton samples, while rotifers accounted for only 16% (Fig. 4). In contrast, when using the combined results, rotifer contribution to biomass increased to 51%, while cladoceran composition declined to 18% and copepods decreased from 21% to 8%. Nauplii percent composition changed b1% between the two methods. In addition to the field methods, laboratory methods also led to differences in estimation of microzooplankton abundance. Calculated density of both rotifers and copepod nauplii were significantly greater when

counted under a compound scope at 100 × power compared to a dissecting scope at 25× power (Table 4). This difference was largest for rotifers, with mean density 70% higher when using 100× power. All three taxa groups had significant linear relationships between the log10 transformed density from 25 × and 100 × counts, with adjusted R2 values ranging from a low of 0.76 for veligers to 0.95 for nauplii (Table 4). The adjusted R2 was lowest for veligers, and mean density did not differ between laboratory methods. For nauplii, there was a very strong relationship with an intercept closes to zero and a slope very close to one. Therefore, the major increase in accuracy from using 100 × power to enumerate microzooplankton appears to be for rotifers, with less of an advantage for the other two taxa. Habitat related patterns in abundance were apparent regardless of sample methodology. Because harbor sampling was concentrated in July, we compared open water and harbor samples collected only

Please cite this article as: Thomas, S.M., et al., Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations, J. Great Lakes Res. (2016), http://dx.doi.org/10.1016/j.jglr.2016.11.002

S.M. Thomas et al. / Journal of Great Lakes Research xxx (2016) xxx–xxx

Density

100%

Copepod

90%

Cladoceran

80%

Nauplii 70%

Veliger

60%

Rotifer

50% 40% 30% 20% 10% 0% Macro only

dual

Biomass 100% Copepod

90%

Cladoceran

80%

Nauplii

70%

Veliger Rotifer

60% 50% 40% 30% 20% 10% 0% Macro only

dual

Fig. 4. Percent composition by density (top panel) and biomass (bottom panel) for 5 major taxonomic groups using estimates from only the macrozooplankton method (64-μm mesh plankton net) and the dual method, which uses crustacean zooplankton estimates from the macrozooplankton method and rotifer, veliger, and nauplii estimates from the microzooplankton (20-μm mesh) method.

during this month in addition to overall seasonal means. In both July and annually, density and biomass of all taxa was higher in harbors and drowned river mouths compared to nearshore open water habitats (Table 5). Rotifer abundance was 2–5 times higher and nauplii 4 times greater in harbors compared with open lake sites (Table 5). During July rotifer density in microzooplankton samples ranged from 137 to 2610 L− 1 in open water and 109 to 4295 L− 1 in harbor areas. For macrozooplankton, cladocerans had the largest difference in abundance between habitats. Discussion Our study demonstrates that the traditional one-size fits all sampling approach used in the majority of zooplankton monitoring studies in the Great Lakes significantly underestimates microzooplankton abundance. As expected, regression results indicated that a 64-μm mesh plankton net does not efficiently sample rotifers, nauplii, and veligers. Microzooplankton density and biomass estimates were up to an order of magnitude higher with the sampling scheme directed specifically at small organisms rather than the larger mesh sampling approach

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that has been used most often. Our results were similar to those in other systems that suggest sampling with meshes larger than 26–48 μm gives doubtful quantitative measures of rotifer and nauplii density and thus zooplankton biomass and production (Bottrell et al., 1976; Chick et al., 2010; Likens and Gilbert, 1970; Makarewicz and Likens, 1979; Orcutt and Pace, 1984). In Lake Ontario, estimated rotifer abundances were 50–175% higher using a Niskin bottle and 30-μm mesh screen compared to sampling with a 64-μm mesh net (Mazumder et al., 1992). Our study also documents that dreissenid mussel veligers, which were not yet present when these earlier studies were performed (exception Chick et al., 2010), are underestimated six fold for both density and biomass with a 64-μm net. Unfortunately, despite previous recommendations of the need for different sampling approaches for microzooplankton, researchers sampling zooplankton communities have continued to use large mesh sizes to document abundance of all taxa, including studies focusing specifically on rotifers (Barbiero and Warren, 2011; Stemberger and Evans, 1984). Our comparison of results from the same macrozooplankton method sample counted under both a compound and dissecting scope indicate that in addition to proper field methodology, enumerating rotifers using a compound scope also improves accuracy of rotifer abundance data. This study also suggests the ecological importance of rotifers and other microzooplankton within Great Lakes zooplankton communities likely has been underestimated due to widespread use of only macrozooplankton sampling methods. The general assumption is that rotifers dominate numerically, as they did in both our macrozooplankton samples (56%) and when using the dual method approach (82%), but contribute little to overall zooplankton biomass (Cooke and Hill, 2010; Davis, 1969; Sprules and Goyke, 1994). However, we estimated that rotifers contributed 51% to total zooplankton biomass when using the dual sampling method, while cladocerans accounted for only 18%. When all three taxa were combined, microzooplankton account for most (75%) of the zooplankton biomass in our samples. Researchers using appropriate microzooplankton sampling methods in other systems also found a much higher contribution by rotifers to total biomass (Taylor et al., 1987). Orcutt and Pace (1984) documented rotifers accounted for 65–85% of zooplankton biomass during summer in Lake Oglethorpe, a warm monomictic lake in Georgia, USA, while Mazumder et al. (1992) estimated rotifers accounted for up to 75% of total zooplankton biomass in Lake Ontario. These results and ours are in contrast to studies using only the macrozooplankton method; percent of total zooplankton biomass attributed to rotifers during spring and summer was reported as 5.6% in Lake Michigan 1983–1992 (Makarewicz et al., 1995), 6% in Lake St. Clair 1984 (Sprules and Munawar, 1991), 16.4% in Lake Erie 1983–1987 (Makarewicz, 1993), 28% in nearshore Lake Ontario 1986–1987 (Makarewicz, 1991), and 2–17% in all five Great Lakes during summer 2006 (Barbiero and Warren, 2011). In this study's macrozooplankton samples, cladocerans dominated total zooplankton biomass at 49%, followed by copepods at 21%, and rotifers at only 16%, similar to previous studies using this method. In addition to field sampling methods, several other factors could have contributed to our higher proportion of rotifers to total biomass. For example, current rotifer percent contribution to total zooplankton biomass may be higher than in the past due to the decline of crustacean zooplankton throughout Lake Michigan and other Great

Table 4 Mean density (# L−1) estimates of nauplii, veligers, and rotifers calculated from standard macrozooplankton method (64 μm) samples counted under both a dissecting (25×) and compound microscope (100×). Numbers in parentheses show standard error of the mean. Results of paired sample t-test and simple linear regression on (log10 (density + 1)) with 25× as the dependent variable and 100× as the independent variable are also presented. Taxa

25× density

100× density

Paired t-test

Regression equation (log10)

Adj R2, p-value

Rotifer

23.2 (3.7)

39.5 (7.7)

Y = 0.463 + 0.874×

R2 = 0.91, p b 0.001

Nauplii

5.9 (1.3)

7.3 (1.7)

Y = 0.166 + 0.959×

R2 = 0.95, p b 0.001

Veliger

14.5 (3.0)

15.1 (2.9)

t = 3.24, p b 0.001 t = 3.49, p b 0.002 t = 0.65, p b 0.30

Y = 0.591 + 0.728×

R2 = 0.76, p b 0.001

Please cite this article as: Thomas, S.M., et al., Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations, J. Great Lakes Res. (2016), http://dx.doi.org/10.1016/j.jglr.2016.11.002

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S.M. Thomas et al. / Journal of Great Lakes Research xxx (2016) xxx–xxx

Table 5 Mean July and annual (May–October) density and biomass of zooplankton collected in nearshore open waters of Lake Michigan (Open, July n = 22; Annual n = 66) compared to samples collected in harbors and drowned river mouths (Harbor, July n = 9; Annual n = 15). Numbers in parentheses show standard error of the mean. Density (# L−1)

Biomass (μg L−1)

Time

Habitat

Taxa

Macro-

Micro-

Macro-

Micro-

July

Harbor Harbor Harbor Harbor Harbor Open Open Open Open Open Harbor Harbor Harbor Harbor Harbor Open Open Open Open Open

Rotifers Veligers Nauplii Cladocerans Adult & juv. copepods Rotifers Veligers Nauplii Cladocerans Adult & juv. copepods Rotifers Veligers Nauplii Cladocerans Adult & juv. copepods Rotifers Veligers Nauplii Cladocerans Adult & juv. copepods

189.5 (45.8) 25.6 (11.8) 24.9 (9.3) 54.9 (27.5) 7.7 (3.7) 63.9 (12.5) 20.2 (7.9) 5.8 (1.9) 4.6 (1.5) 3.1 (1.0) 114.7 (35.6) 29.0 (12.3) 18.0 (6.0) 34.1 (17.5) 5.2 (2.3) 23.0 (2.4) 11.9 (2.1) 3.2 (0.5) 2.8 (0.4) 1.7 (0.3)

1732.5 (110.3) 183.3 (112.3) 94.0 (30.3)

21.3 (7.1) 5.7 (2.6) 4.0 (1.5) 122.3 (86.7) 27.1 (20.1) 4.2 (0.4) 4.5 (0.8) 0.9 (0.1) 4.4 (0.2) 6.8 (2.2) 17.7 (5.8) 6.5 (2.7) 2.9 (1.0) 74.4 (53.2) 17.1 (12.2) 2.2 (0.3) 2.7 (0.5) 0.6 (0.1) 2.3 (0.4) 4.4 (1.0)

131.2 (40.6) 35.4 (21.7) 9.9 (3.2)

July

Annual

Annual

Lakes (Vanderploeg et al., 2012). In addition, individual dry mass estimates of rotifers vary greatly depending on method used. However, if we used a lower mean individual dry weight compared to ours, such as in Bowen and Johannsson (2011), microzooplankton biomass still accounts for 64% of total biomass using dual method results. This indicates that rotifers are important to include in lower trophic level sampling to gain a fuller understanding of ecosystem dynamics in the Great Lakes under current and future conditions. The estimates from our dual sampling approach present some of the first accurate information on the abundance of rotifers, veligers, and nauplii in Lake Michigan and other Great Lakes. For example, our annual mean rotifer density for the microzooplankton method in nearshore open waters was at least two times higher and the July mean was 3.75 times greater compared to one of the few other nearshore studies in Lake Michigan, which found monthly mean density of 70–190 L− 1 using a 76-μm mesh net (Stemberger and Evans, 1984). Compared to past studies in Lake Michigan offshore waters during August using macrozooplankton methods, our open water microzooplankton mean rotifer density in August (322 L−1) and annually (396 L−1) was on the upper end or higher than those found by Makarewicz et al. (1995), which ranged from 60 to 330 L−1 during 1983–1992, and approximately three to four times greater than the mean rotifer density of 105 L−1 during 2006 (Barbiero and Warren, 2011). Looking beyond Lake Michigan, mean rotifer density was only 51 L− 1 over 4 years during 2003–2013 in offshore waters of Lake Ontario using a 63-μm net (Makarewicz and Lewis, 2015). The high density of rotifers observed in our microzooplankton samples is even more noteworthy in comparison to the studies from the 1980s, because rotifers have often declined after dreissenid mussel invasions (David et al., 2009; Pace et al., 1998). We recommend that paired sampling be conducted in other Great Lakes systems to see if the large differences we observed will be repeated at deeper depths, varying levels of eutrophication, and variation in abundance and composition of invasive species. One source of potential error to be kept in mind when reviewing our results is that we did not have metered net tows. Makarewicz et al. (1989) reported average filtering efficiency of 93.3% using a 62-μm mesh, 0.5 m diameter plankton net in Lake Michigan during 1985, when chlorophyll a biomass, an indicator of phytoplankton abundance, was three times as high as in 2007–2011 (Madenjian et al., 2015). We would expect an even higher filtering efficiency given the current low phytoplankton levels in Lake Michigan. Assuming 100% net efficiency may bias the macrozooplankton data toward lower density and biomass

714.4 (211.3) 100.1 (36.3) 21.2 (5.7)

1386.5 (346.5) 170.1 (72.6) 70.3 (19.8)

396.4 (58.1) 81.3 (12.5) 12.6 (1.5)

52.2 (4.0) 19.3 (3.4) 2.2 (0.3)

114.9 (30.6) 32.8 (14.0) 7.4 (2.1)

28.8 (4.1) 15.7 (2.4) 1.3 (0.2)

values, contributing to some of the differences seen between the two methods. However, the differences between densities of the two methods were so high that net efficiency for the macrozooplankton method samples would have to be below 25%, 15% and 5% for nauplii, veliger and rotifer density, respectively, to obtain densities as high as those found with the microzooplankton method. Regardless of any potential issues with reduced net efficiency, our major findings remain true, most strikingly for rotifers; and underestimating the contribution of microzooplankton to zooplankton communities by using only macrozooplankton methods can have large implications on our understanding of trophic ecology in the Great Lakes. Rotifers, and perhaps veligers, have a major role in energy transfer in lakes (Arndt, 1993). Rotifers also serve as both preferred prey and as a competitor of copepods (Brandl, 2005). This is particularly important in the current Lake Michigan and Lake Huron ecosystems, where abundances of Daphnia and Diacyclops have greatly declined and the crustacean zooplankton community is now dominated by calanoid copepods (Bunnell et al., 2012; Vanderploeg et al., 2012). Previous studies have shown rotifer biomass has an inverse relationship with Daphnia populations (Pollard et al., 1998; Ronneberger et al., 1993; Stelzer, 1998) and release from predation and competition by Daphnia and cyclopoids should contribute to increases in rotifer abundances in Lake Michigan. This could be one explanation for why despite introduction of dreissenids, we are still seeing relatively high numbers of rotifers. Declines in rotifer and copepod nauplii abundance and changes in rotifer community composition in Lake Ontario have also coincided with establishment of C. pengoi in Lake Michigan and Lake Ontario (Warner et al., 2006; Witt et al., 2005; Makarewicz and Lewis, 2015). Although it may be suggested that predation by C. pengoi contributed to these changes (Warner et al., 2006), it is hard to untangle from other factors, and the large mesh sizes used in these studies (73 & 153 μm) complicates matters even further. The invasive B. longimanus also consumes copepod nauplii and rotifers, in addition to small cladocerans (Branstrator, 1995; Schulz and Yurista, 1999). Thus the microzooplankton community is involved in many steps of the Great Lakes food web, including interactions with invasive species, and accurate assessments of its abundance are necessary. After sampling with a microzooplankton method, Mazumder et al. (1992) cautioned that past food web models for Lake Ontario that excluded rotifers are questionable, and we emphasize these concerns. Modeling efforts on the Lake Michigan food web are likely to be less informative without accurate density, biomass and composition data on rotifers, copepod nauplii,

Please cite this article as: Thomas, S.M., et al., Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations, J. Great Lakes Res. (2016), http://dx.doi.org/10.1016/j.jglr.2016.11.002

S.M. Thomas et al. / Journal of Great Lakes Research xxx (2016) xxx–xxx

and veligers. Inaccurate information on Great Lakes zooplankton community composition has ramifications beyond a basic understanding of Great Lakes food webs. The lack of accurate data on abundance of microzooplankton suggests that prey resources available to Asian carp in Lake Michigan have been greatly underestimated (i.e., Cooke and Hill, 2010), along with the likelihood these invasive species could become established. The rapid generation times of rotifers compared to copepods and cladocerans (Makarewicz and Likens, 1979) may provide a ready prey source for Asian carp and other planktivores in a food web with low density of large zooplankton such as Lake Michigan. In the Mississippi and Illinois rivers, rotifers and copepod nauplii are both important food items for bighead and silver carp (Sampson et al., 2009). Because both species of Asian carp are passive filter feeders once they reach about 250 mm total length and efficiently filter particles ≥ 20 μm, there is no reason that veligers would also not be consumed by both species. Densities of rotifers and nauplii in our study fall between those in the Ohio (404 L−1), Missouri (413 L− 1) and Mississippi (923 L− 1) rivers where Asian carp are thriving (data from 2004 to 2006; Bukaveckas et al., 2011). Bioenergetics modeling indicated that most open waters of the Great Lakes would not support growth of Asian carp; however, our data strongly suggest that because of a lack of accurate microzooplankton biomass data, Cooke and Hill (2010) substantially underestimated the prey base available to Asian carp in Lake Michigan. This issue needs to be reevaluated with more accurate data on microzooplankton. The high microzooplankton densities we found in harbors and drowned river mouths, which are often ignored in lower trophic level sampling programs, are most worrisome, because it is these areas that likely have the most preferential thermal regime for Asian carp. Finally, we demonstrated that sampling methodology can also play a large role in perceived community composition of microzooplankton. We found 98% more Polyarthra and 83% more Keratella in our 20-μm samples compared to the 64-μm samples, which was an even more striking difference than past researchers noted. Likens and Gilbert (1970) looked specifically at soft bodied Polyarthra and loricated Keratella filtered through 35-, 48- and 75-μm mesh and found that the smallest mesh collected 61% more Polyarthra and 23% more Keratella compared to the 75-μm mesh. They hypothesized that soft bodied rotifers are more easily able to squeeze through mesh openings than loricated forms. The mean length of Polyarthra in this study was 106 μm in contrast to Conochilus, a large, colonial species that may be up to 450 μm in length (Stemberger, 1979) and Kellicottia, which has long spines that give it a length of up to 860 μm. Conochilus and Kellicottia were not among our six most abundant rotifer taxa, but they were dominant in past studies in Lake Superior (Johnson et al., 2004) and Lake Michigan (Barbiero et al., 2012; Barbiero and Warren, 2011; Makarewicz et al., 1995). While some of the differences between these previous studies and ours may come from nearshore versus offshore habitat differences and temporal changes, it is possible that the majority of the smaller species, which made up the largest portion of our 20-μm catches, passed through their nets giving an inaccurate representation of the rotifer community. Two species, Asplanchna and Kellicottia, were collected in a noticeably greater number of samples using the macrozooplankton method in this study. It may be that, similar to crustaceans, a large enough volume of water is not filtered in the microzooplankton method to capture these less abundant, but very large rotifer species. This will need to be examined in further samples and additional habitats/systems. The results of this study strongly suggest that the dual sampling approach should be evaluated under current ecosystem changes in other Great Lakes and become the norm rather than the exception for freshwater zooplankton research. We found that density and biomass estimates were 10 and 3.8 times higher, respectively, using the combined approach that included the microzooplankton method. A dual sampling strategy conducted at multiple points throughout the sampling season

9

will greatly improve our understanding of the Lake Michigan zooplankton community, food web, and vulnerability to Asian carp. Because the microzooplankton method is not efficient for sampling larger zooplankton, the downside to collecting accurate rotifer, nauplii and veliger data is that it requires two sampling methodologies if macrozooplankton data is of interest as well. This is however, a small trade-off in return for more accurate abundance and biomass data. We found doing the additional microzooplankton sample added b 5 min in nearshore waters and ≤15 min in offshore waters when sampling down to 20 m. Offshore sampling time could be greatly reduced on research ships with an integrator. Filtering time was sometimes slower in the very productive drowned river mouths on the east side of Lake Michigan, but this was remedied by increasing the diameter of the sieve used; alternatively one could reduce the volume of water filtered. The largest increase in processing time would come from laboratory activities. Seasonal composite samples could be a way to reduce laboratory time, but success using this technique has been mixed (K. Bowen, Fisheries and Oceans Canada, 2016, personal communication). The additional effort of the dual approach proposed here would yield more accurate abundance and biomass data for rotifers, nauplii and veligers. This will better enable us to monitor temporal and spatial trends, community changes, and impacts of invasive species on food web and production models in general. Acknowledgments This project was funded by the Illinois Department of Natural Resources, grant number CA-FWS-74 under the Aquatic Nuisance Species program. We would like to thank the numerous technicians from the Lake Michigan Biological Station (LMBS) that assisted in collecting samples, particularly Erin Thayer and Caitlin Smoot who traveled for the Michigan samples. The Harvey Bootsma lab at the University of Wisconsin-Milwaukee, School of Freshwater Science, collected the Milwaukee Harbor samples. Caitlin Smoot (LMBS) and Kristopher Maxson (INHS) assisted with counting and identification of samples using the compound scope. Special thanks to Mary Balcer at University of Wisconsin, Superior for sharing her vast knowledge of rotifer taxonomy and assisting with identifications of tricky specimens, along with Joseph Makarewicz of SUNY Brockport. Thank you to Kelley Bowen of Fisheries and Oceans Canada for providing supplemental rotifer density information from several publications. Diane Wudi at LMBS provided administrative support. Two anonymous reviews provide helpful comments and direction on an earlier draft of this manuscript. References Arndt, H., 1993. Rotifers as predators on components of the microbial web (bacteria, heterotrophic flagellates, ciliates) – a review. Hydrobiologia 255/256, 231–246. Balcer, M.D., Korda, N.L., Dodson, S.I., 1984. Zooplankton of the Great Lakes: A Guide to the Identification and Ecology of the Common Crustacean Species. The University of Wisconsin Press, Madison, WI, USA. Barbiero, R.P., Warren, G.J., 2011. Rotifer communities in the Laurentian Great lakes, 1983-2006 and factors affecting their composition. J. Great Lakes Res. 37, 528–540. Barbiero, R.P., Lesht, B.M., Warren, G.J., 2012. Convergence of trophic state and the lower food web in Lakes Huron, Michigan and Superior. J. Great Lakes Res. 38, 368–380. Benoît, H.P., Johannsson, O.E., Warner, D.M., Sprules, W.G., Rudstam, L.G., 2002. Assessing the impact of a recent predatory invader: the population dynamics, vertical distribution, and potential prey of Cercopagis pengoi in Lake Ontario. Limnol. Oceanogr. 47, 626–635. Bottrell, H.H., Duncan, A., Gliwicz, Z.M., Grygierek, E., Herzig, A., Hillbricht-Ilkowski, A., Kurasawa, H., Larsson, P., Wegleńska, T., 1976. A review of some problems in zooplankton production studies. Nor. J. Zool. 24, 419–456. Bowen, K.L., Johannsson, O.E., 2011. Changes in zooplankton biomass in the Bay of Quinte with the arrival of the mussels, Dreissena polymorpha and D. rostiformis bugensis, and the predatory cladoceran, Cercopagis pengoi: 1975-2008. Aquat. Ecosyst. Health Manag. 14, 44–55. Brandl, Z., 2005. Freshwater copepods and rotifers: predators and their prey. Hydrobiologia 183, 475–489. Branstrator, D.K., 1995. Ecological interactions between Bythotrephes cederstroemi and Leptodora kindtii and the implications for species replacement in Lake Michigan. J. Great Lakes Res. 21, 670–679. Bukaveckas, P.A., MacDonald, A., Aufdenkampe, A., Chick, J.H., Havel, J.E., Schultz, R., Angradi, T.R., Bolgrien, D.W., Jicha, T.M., Tayler, D., 2011. Phytoplankton abundance

Please cite this article as: Thomas, S.M., et al., Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations, J. Great Lakes Res. (2016), http://dx.doi.org/10.1016/j.jglr.2016.11.002

10

S.M. Thomas et al. / Journal of Great Lakes Research xxx (2016) xxx–xxx

and contributions to suspended particulate matter in the Ohio, Upper Mississippi and Missouri rivers. Aquat. Sci. 73, 419–436. Bunnell, D.B., Keeler, K.M., Puchala, E.A., Davis, B.M., Pothoven, S.A., 2012. Comparing seasonal dynamics of the Lake Huron zooplankton community between 1983-1984 and 2007 and revisiting the impact of Bythotrephes planktivory. J. Great Lakes Res. 38, 451–462. Burkhardt, S., 1994. Seasonal size variation in the predatory cladoceran Bythotrephes cederstoemii in Lake Michigan. Freshw. Biol. 31, 97–108. Chick, J.H., Levchuk, A.P., Medley, K.A., Havel, J.H., 2010. Underestimation of rotifer abundance a much greater problem than previously appreciated. Limnol. Oceanogr. Methods 8, 79–87. Cooke, S.L., Hill, W.R., 2010. Can filter-feeding Asian carp invade the Laurentian Great Lakes? A bioenergetic modelling exercise. Freshw. Biol. 55, 2138–2152. Cremer, M.C., Smitherman, R.O., 1980. Food habits and growth of silver and bighead carp in cages and ponds. Aquaculture 20, 57–64. Creque, S.M., Czesny, S.J., 2012. Diet overlap of non-native alewife with native yellow perch and spottail shiner in nearshore waters of southwestern Lake Michigan, 2000-2007. Ecol. Freshw. Fish 21, 207–221. Culver, D.A., Boucherle, M.M., Bean, D.J., Fletcher, J.W., 1985. Biomass of freshwater crustacean zooplankton from length-weight regressions. Can. J. Fish. Aquat. Sci. 42, 1380–1390. David, K.A., Bruce, B.M., Hunter, R.D., 2009. Lake St. Clair zooplankton: evidence for postDreissena changes. J. Freshw. Ecol. 24, 199–209. Davis, C.C., 1969. Seasonal distribution, constitution, and abundance of zooplankton in Lake Erie. J. Fish. Res. Board Can. 26, 2459–2476. Dodson, S.L., Cáceres, C.E., Rogers, D.C., 2010. Cladocera and other branchiopoda. In: Thorp, J.H., Covich, A.P. (Eds.), Ecology and Classification of North American Freshwater Invertebrates. Elsevier, Burlington, MA, USA, pp. 773–827. Dong, S., Li, D., 1994. Comparative studies of the feeding selectivity of silver carp, Hypopthalmichthys molitrix, and bighead carp Aristichthys nobilis. J. Fish Biol. 44, 10–14. Downing, J.A., Rigler, F.H., 1984. A Manual on Methods for the Assessment of Secondary Productivity in Freshwaters. Blackwell Scientific Publications, Boston, MA, USA. Duffy, W.G., Liston, C.R., 1978. Seasonal abundance of planktonic rotifers in nearshore area of central Lake Michigan. J. Great Lakes Res. 4, 46–49. Dumont, H.J., Van De Velde, I., Dumont, S., 1975. The dry weight estimate of biomass in a selection of cladoceran, copepod and rotifer from the plankton, periphyton and benthos of continental waters. Oecologia 19, 75–97. Evans, M.S., 1986. Lake Huron rotifer and crustacean zooplankton, April-July, 1980. J. Great Lakes Res. 12, 281–292. Fahnenstiel, G.L., Krause, A.E., McCormick, M.J., Carrick, H.J., Schelske, C.L., 1998. The structure of the planktonic food-web in the St. Lawrence Great Lakes. J. Great Lakes Res. 24, 531–554. George, E.M., Roseman, E.F., Davis, B.M., O'Brien, T.P., 2013. Feeding ecology of pelagic larval burbot in northern Lake Huron, Michigan. Trans. Am. Fish. Soc. 142, 1716–1723. Grothe, D.W., Grothe, D.R., 1977. An illustrated key to planktonic rotifers of the Laurentian Great Lakes. US EPA, EPA 905378003, Region V, Chicago, IL, USA, p. 91. URL:. http:// nepis.epa.gov/Exe/ZyPDF.cgi/2000O4GO.PDF?Dockey=2000O4GO.PDF. Hawkins, B.E., Evans, M.E., 1979. Seasonal cycles of zooplankton biomass in southeastern Lake Michigan. J. Great Lakes Res. 5, 256–263. Heath, R.T., Hwang, S.J., Munawar, M., 2003. A hypothesis for the assessment of the importance of microbial food web linkages in nearshore and offshore habitats of the Laurentian Great Lakes. Aquat. Ecosyst. Health Manag. 6, 231–239. Johannsson, O.E., Dermott, R., Graham, D.M., Dahl, J.A., Millard, E.S., Myles, D.D., LeBlanc, J., 2000. Benthic and pelagic secondary production in Lake Erie after the invasion of Dreissena spp. with implications for fish production. J. Great Lakes Res. 26, 31–54. Johnson, T.B., Hoff, M.H., Trebitz, A.S., Bronte, C.R., Corry, T.D., Kitchell, J.F., Lozano, S.J., Mason, D.M., Scharold, J.V., Schram, S.T., Schreiner, D.R., 2004. Spatial patterns in assemblage structure of pelagic forage fish and zooplankton in Western Lake Superior. J. Great Lakes Res. 30 (Suppl. 1), 395–406. Lavrentyev, P.J., Vanderploeg, H.A., Franzé, G., Chacin, D.H., Liebig, J.R., Johengen, T.H., 2014. Microzooplankton distribution, dynamics, and trophic interactions relative to phytoplankton and quagga mussels in Saginaw Bay, Lake Huron. J. Great Lakes Res. 40, 95–105. Likens, G.E., Gilbert, J.J., 1970. Notes on quantitative sampling of natural populations of planktonic rotifers. Limnol. Oceanogr. 15, 816–820. MacIsaac, H.J., Lonnee, C.J., Leach, J.H., 1995. Suppression of microzooplankton by zebra mussels: importance of mussel size. Freshw. Biol. 34, 379–387. Madenjian, C.P., Fahnenstiel, G.L., Johengen, T.H., Nalepa, T.F., Vanderploeg, H.A., Fleischer, P.J., Benjamin, D.M., Smith, E.B., Bence, J.R., Rutherford, E.S., Lavis, D.S., Robertson, D.M., Jude, D.J., Ebener, M.P., 2002. Dynamics of the Lake Michigan food web, 19702000. Can. J. Fish. Aquat. Sci. 59, 736–753. Madenjian, C.P., Bunnel, D.B., Warner, D.M., Pothoven, S.A., Fahnenstiel, G.L., Nalepa, T.F., Vanderploeg, H.A., Tsehaye, I., Claramunt, R.M., Clark Jr., R.D., 2015. Changes in the Lake Michigan food web following dreissenid mussel invasions: a synthesis. J. Great Lakes Res. 41 (Suppl. 3), 217–231. Makarewicz, J.C., 1991. Feasibility of shoreside monitoring of the Great Lakes. J. Great Lakes Res. 17, 344–360. Makarewicz, J.C., 1993. A lakewide comparison of zooplankton biomass and its species composition in Lake Erie, 1983-1987. J. Great Lakes Res. 19, 275–290. Makarewicz, J.C., Lewis, T.W., 2015. Long-term changes in Lake Ontario rotifer abundance and composition: a response to Cercopagis predation? J. Great Lakes Res. 41, 192–199. Makarewicz, J.C., Likens, G.E., 1979. Structure and function of the zooplankton community of Mirror Lake, New Hampshire. Ecol. Monogr. 49, 109–127.

Makarewicz, J.C., Lewis, T.W., Bertram, P., 1989. Phytoplankton and Zooplankton Composition, Abundance and Distribution and Trophic Interactions: Offshore Region of Lake Erie, Lake Huron and Lake Michigan, 1985. USEPA, Great Lakes National Program Office, Vol. 1. Makarewicz, J.C., Betram, P., Lewis, T., Brown Jr., E.H., 1995. A decade of predatory control of zooplankton species composition of Lake Michigan. J. Great Lakes Res. 21, 620–640. Mazumder, A., Lean, D.R.S., Taylor, W.D., 1992. Dominance of small filter feeding zooplankton in the Lake Ontario foodweb. J. Great Lakes Res. 18, 456–466. Nauwerck, A., 1978. Notes on planktonic rotifers of Lake Ontario. Arch. Hydrobiol. 84, 269–301. O'Malley, B.P., Bunnell, D.B., 2014. Diet of Mysis diluviana reveals seasonal patterns of omnivory and consumption of invasive species in offshore Lake Michigan. J. Plankton Res. 36, 989–1002. Ojaveer, H., Kuhns, L.A., Barbiero, R.P., Tuchman, M.L., 2001. Distribution and population characteristics of Cercopagis pengoi in Lake Ontario. J. Great Lakes Res. 27, 10–18. Orcutt, J.D., Pace, M.L., 1984. Seasonal dynamics of rotifer and crustacean zooplankton populations in a eutrophic, monomictic lake with a note on rotifer sampling techniques. Hydrobiologia 119, 73–80. Pace, M.L., Orcutt, J.D., 1981. The relative importance of protozoans, rotifers, and crustaceans in a freshwater zooplankton community. Limnol. Oceanogr. 26, 822–830. Pace, M.L., Stuart, E.G., Findlay, G., Fischer, D., 1998. Effects of an invasive bivalve on the zooplankton community of the Hudson River. Freshw. Biol. 39, 103–116. Pollard, A.I., González, M.J., Vanni, M.J., Headworth, J.L., 1998. Effects of turbidity and biotic factors on the rotifer community in an Ohio reservoir. Hydrobiologia 378 (388), 215–223. Pothoven, S.A., Fahnenstiel, G.L., 2015. Spatial and temporal trends in zooplankton assemblages along a nearshore to offshore transect in southeastern Lake Michigan from 2007-2012. J. Great Lakes Res. 41 (Suppl. 3), 95–103. Reid, J.W., Williamson, C.E., 2010. Copepoda. In: Thorp, J.H., Covich, A.P. (Eds.), Ecology and Classification of North American Freshwater Invertebrates. Elsevier, Burlington, MA, USA, pp. 829–899. Ronneberger, D., Kasprzak, P., Krienitz, L., 1993. Long–term changes in the rotifer fauna after biomanipulation in Haussee (Feldberg, German, Mecklenburg-Vorpommern) and its relationship to the crustacean and phytoplankton communities. Hydrobiologia 255/256, 297–304. Sampson, S.J., Chick, J.H., Pegg, M.A., 2009. Diet overlap among two Asian carp and three native fishes in backwater lakes on the Illinois and Mississippi rivers. Biol. Invasions 11, 483–496. Schulz, K.L., Yurista, P.P., 1999. Implications of an invertebrate predator's (Bythotrephes cederstroemii) atypical effects on a pelagic zooplankton community. Hydrobiologia 380, 179–193. Segers, H., 2008. Global diversity of rotifers (Rotifera) in freshwater. Hydrobiologia 595, 49–59. Sieburth, J.M., Smetacek, V., 1978. Pelagic ecosystem structure: heterotrophic compartments of the plankton and their relationship to plankton size fraction. Limnol. Oceanogr. 23, 12–63. Sprules, W.G., Goyke, A.P., 1994. Size-based structure and production in the pelagia of Lakes Ontario and Michigan. Can. J. Fish. Aquat. Sci. 51, 2603–2611. Sprules, W.G., Munawar, M., 1991. Plankton community structure in Lake St. Clair, 1984. Hydrobiologia 219, 229–237. Sprung, M., 1993. The other life: an account of present knowledge of the larval phase of Dreissena polymorpha. In: Nalepa, T.F., Schloesser, D.W. (Eds.), Zebra Mussels: Biology, Impacts and Control. Lewis Publishers, Ann Arbor, MI, pp. 39–53. Stelzer, C.-P., 1998. Population growth in planktonic rotifers. Does temperature shift the competitive advantage for different species? Hydrobiologia 387/388, 349–353. Stemberger, R.S., 1974. Temporal and spatial distributions of planktonic rotifers in Milwaukee Harbor and adjacent Lake Michigan. Proceedings of the 17th Conference of Great Lakes Research. International Association of Great Lakes Research, pp. 120–134. Stemberger, R.S., 1979. A guide to rotifers of the Laurentian Great Lakes. EPA-600/4-79021, U.S Environmental Protection Agency, Cincinnati, OH, USA, p. 200. URL:. http:// nepis.epa.gov/Exe/ZyPDF.cgi/2000GT6R.PDF?Dockey=2000GT6R.PDF. Stemberger, R.S., 1995. The influence of mixing on rotifer assemblages of Michigan lakes. Hydrobiologia 297, 149–161. Stemberger, R.S., Evans, M.S., 1984. Rotifer seasonal succession and copepod predation in Lake Michigan. J. Great Lakes Res. 10, 417–428. Stemberger, R.S., Gilbert, J.J., 1987. Rotifer threshold food concentrations and the sizeefficiency hypothesis. Ecology 68, 181–187. Stemberger, R.S., Gannon, J.E., Bricker, F.J., 1979. Spatial and seasonal structure of rotifer communities in Lake Huron. EPA-600/3-79-085, US EPA, Region V, Chicago, IL, USA, p. 108. URL:. http://nepis.epa.gov/Exe/ZyPDF.cgi/2000HSKG.PDF?Dockey= 2000HSKG.PDF. Taylor, W.D., Fricker, H.-J., Lean, D.R.S., 1987. Zooplankton seasonal succession in Lake Ontario at Northshore, midlake, and southshore stations in 1982, and a comparison with 1970. Can. J. Fish. Aquat. Sci. 44, 2178–2184. Thomasen, S., Gilbert, J., Chow-Fraser, P., 2013. Wave exposure and hydrologic connectivity create diversity in habitat and zooplankton assemblages at nearshore Long Point Bay, Lake Erie. J. Great Lakes Res. 39, 56–65. Vanderploeg, H.A., Pothoven, S.A., Fahnenstiel, G.L., Cavaletto, J.F., Liebig, J.R., Stow, C.A., Nalepa, T.F., Madenjian, C.P., Bunnell, D.B., 2012. Seasonal zooplankton dynamics in Lake Michigan: disentangling impacts of resource limitation, ecosystem engineering, and predation during a critical ecosystem transition. J. Great Lakes Res. 38, 336–352. Wallace, R.L., Snell, T.W., 2010. Rotifera. In: Thorp, J.H., Covich, A.P. (Eds.), Ecology and Classification of North American Freshwater Invertebrates. Elsevier, Burlington, MA, USA, pp. 173–235.

Please cite this article as: Thomas, S.M., et al., Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations, J. Great Lakes Res. (2016), http://dx.doi.org/10.1016/j.jglr.2016.11.002

S.M. Thomas et al. / Journal of Great Lakes Research xxx (2016) xxx–xxx Warner, D., Rudstam, L.G., Benoît, Mills, E.L., Johannson, O., 2006. Changes in seasonal nearshore zooplankton abundance patterns in Lake Ontario following establishment of the exotic predator Cercopagis pengoi. J. Great Lakes Res. 32, 531–542. Wells, L., 1970. Effects of alewife predation on zooplankton populations in Lake Michigan. Limnol. Oceanogr. 15, 556–565.

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Williamson, C.J., Garvey, J.E., 2005. Growth, fecundity, and diets of newly established silver carp in the Middle Mississippi River. Trans. Am. Fish. Soc. 134, 1423–1430. Witt, A.M., Dettmers, J.M., Cáceres, C.E., 2005. Cercopagis pengoi in southwestern Lake Michigan for four years following invasion. J. Great Lakes Res. 31, 245–252.

Please cite this article as: Thomas, S.M., et al., Underestimation of microzooplankton is a macro problem: One size fits all zooplankton sampling needs alterations, J. Great Lakes Res. (2016), http://dx.doi.org/10.1016/j.jglr.2016.11.002