Cyanobacterial blooms modify food web structure and interactions in western Lake Erie

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Cyanobacterial blooms modify food web structure and interactions in western Lake Erie Ruth D. Brilanda, , Joshua P. Stonea,1, Manjunath Manubolua,b, Jiyoung Leeb,c, Stuart A. Ludsina ⁎

a

Aquatic Ecology Laboratory, Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, 1314 Kinnear Rd., Columbus, OH, 43212, USA Division of Environmental Health Sciences, College of Public Health, The Ohio State University, 1841 Neil Avenue, Columbus, OH, USA c Department of Food Science & Technology, The Ohio State University, 2015 Fyffe Road, Columbus, OH, USA b

ARTICLE INFO

ABSTRACT

Keywords: Harmful algal bloom Agricultural runoff Nutrient pollution Great lakes Gizzard shad Yellow perch

With anthropogenic eutrophication and climate change causing an increase in cyanobacterial blooms worldwide, the need to understand the consequences of these blooms on aquatic ecosystems is paramount. Key questions remain unanswered with respect to how cyanobacteria blooms affect the structure of aquatic food webs, the foraging abilities of higher consumers, and the potential for cyanotoxins (e.g., microcystins [MCs]) to accumulate in fish. Toward addressing these uncertainties, physicochemical attributes, water (for MCs), phytoplankton, zooplankton, and epipelagic and benthic age-0 fish were sampled at 75 sites (44 sites for fish) of varying cyanobacteria concentration (0.1–44 μg/L) in western Lake Erie during the cyanobacteria bloom season, 2013–2014. Sites with high cyanobacteria biomass were characterized by Microcystis spp. (84–100% of biomass), detectible levels of MCs (maximum = 10.8 μg/L), and low water transparency (minimum = 0.25 m). Counter to expectations, strong positive relationships were found between cyanobacteria concentration and the biomass of several herbivorous zooplankton taxa (e.g., Daphnia, Diaphanosoma spp., Bosmina (formerly Eubosmina) coregoni, and Calanoida spp.). Expectations regarding fish were partly supported (e.g., diet selectivity varied across a cyanobacteria gradient) and partly not (e.g., consumption of zooplankton did not differ between bloom and nonbloom sites). These findings show that cyanobacterial blooms can strongly affect the distribution, composition, and interactions of zooplankton and fish, sometimes in surprising ways, highlighting the need to further explore their impact on aquatic food webs.

1. Introduction

Posch et al., 2012), as well as by increasing the frequency and magnitude of precipitation events that cause nutrient runoff (Kunkel et al., 1999; Greig et al., 2012). The resultant intensification in the magnitude, extent, and frequency of cyanoblooms is problematic because reduced water clarity and cyanotoxin production can reduce tourism and local property values (Dodds et al., 2009), reduce access to clean, safe water for drinking and recreation (e.g., swimming, boating; Qin et al., 2010; Westrick et al., 2010), reduce fisheries production potential (Colby et al., 1972; Lee and Jones, 1991), and threaten human health through the accumulation of cyanotoxins in edible fish tissues (Cheung et al., 2013; Carmichael and Boyer, 2016). Lake Erie is an ideal study system to examine the effects of cyanoblooms because large ones have begun to recur regularly during the past decade, owing to the recent re-eutrophication of Lake Erie (Michalak et al., 2013; Kane et al., 2014; Watson et al., 2016). Similar

Cyanobacterial blooms (hereafter cyanoblooms) have become a common feature in freshwater, estuarine, and coastal marine ecosystems on a global scale, owing to human-induced environmental change (Bricker et al., 2008; Smith and Schindler, 2009; O’Neil et al., 2012; Stumpf et al., 2012; Paerl and Otten, 2013). This increase in cyanobloom occurrence and severity is primarily due to increases in agriculture and urbanization in the surrounding watershed, which have led to heightened inputs of limiting nutrients (e.g., nitrogen, phosphorus) that then accumulate in downstream, receiving water bodies (Vitousek et al., 1997; Carpenter et al., 1998; Kane et al., 2014; Stow et al., 2015). In addition, climate change has promoted cyanoblooms, especially in temperate ecosystems, by increasing water temperature and the longevity of water-column stratification (Paerl and Huisman, 2008, 2009;

⁎ Corresponding author. Current address: Division of Drinking and Ground Waters, Ohio Environmental Protection Agency, 50 W Town St, Columbus, OH, 43215, USA. E-mail address: [email protected]",0,0,2 >[email protected] (R.D. Briland). 1 Current: Department of Biological Sciences, University of South Carolina, 700 Sumter Street, Columbia, SC, 29208, USA

https://doi.org/10.1016/j.hal.2019.03.004 Received 11 June 2018; Received in revised form 9 February 2019; Accepted 10 March 2019 1568-9883/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Ruth D. Briland, et al., Harmful Algae, https://doi.org/10.1016/j.hal.2019.03.004

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to other aquatic water bodies worldwide, non-point source runoff of soluble reactive phosphorus has been implicated in the formation of these cyanoblooms (Stumpf et al., 2012; Scavia et al., 2014; Bertani et al., 2016), with climate change also suspected to have played a role (Bosch et al., 2014; Bullerjahn et al., 2016). These cyanoblooms, which occur during summer through early fall, have been traditionally dominated by Microcystis spp. (e.g., M. aeruginosa; Kützing 1846; Müller et al., 2017) that are known to also produce highly toxic forms of the liver toxin microcystin (e.g., MC-LR and MC-RR; Dyble et al., 2008; Rinta-Kanto et al., 2009). The numerous negative impacts of these cyanoblooms will increase in the future, if significant mitigation efforts are not taken, as Microcystis blooms are predicted to intensify in Lake Erie under current management scenarios (Bullerjahn et al., 2016). While the cyanoblooms in Lake Erie have caused economic damage and even a public health scare in the region (Dodds et al., 2009; Steffen et al., 2014), an understanding of how they affect the structure, function, and dynamics of aquatic food webs and higher consumers such as zooplankton and fish remains incomplete (see Wilson et al., 2006; Gobler et al., 2007; Davis et al., 2012; Wituszynski et al., 2017). This information gap is in part due to the many ways in which cyanobacteria can influence and interact with the aquatic realm. For example, cyanoblooms have been shown to affect zooplankton population growth (Wilson et al., 2006) by reducing feeding rates for both generalist filterfeeding taxa (e.g., via mechanical interference; DeMott, 1999; Yang et al., 2006; Bednarska et al., 2014) and visual predators (e.g., via reduced light; Jiang et al., 2014; Chen et al., 2005). Moreover, the toxins that are produced by cyanobacteria (e.g., microcystins, MCs), which can accumulate in higher consumers (Magalhães et al., 2001; Xie et al., 2005), could differentially affect the short-term and long-term development, growth, and survival of biota (Wilson et al., 2006; Pohnert et al., 2007; Ger et al., 2016). Additionally, while some research has shown that cyanobacteria may increase food availability to higher consumers by promoting microbial food web components (Vaqué et al., 1992; Kamjunke et al., 1997; Mrdjen et al., 2018), cyanoblooms generally are expected to reduce the quality of food available to higher consumers because cyanobacteria have only minimal amounts of essential fatty acids (Von Elert et al., 2003). Many of these negative effects on zooplankton are especially likely in blooms of Microcystis, the dominant cyanobacterial genus in western Lake Erie (Rinta-Kanto et al., 2005; Chaffin et al., 2011) and other freshwater ecosystems experiencing cultural eutrophication (Chen et al., 2013). While negative effects of cyanoblooms on zooplankton populations have been shown in laboratory studies, natural populations may be more resilient than laboratory studies predict, as some zooplankton taxa can develop resistance to cyanotoxins with chronic exposure (Ger et al., 2016). Owing to (1) the heavy use of laboratorybased approaches in cyanobacteria-zooplankton studies (Wilson et al., 2006; Ger et al., 2014), (2) the many ways in which cyanoblooms can affect habitat for zooplankton, and (3) the fact that selective-grazing of edible phytoplankton by zooplankton (e.g., Calanoida spp.) can actually cause a positive relationship between cyanobacteria and zooplankton (e.g., Leitão et al., 2018), multi-level, food web studies in natural settings are needed, if cyanobacteria-zooplankton interactions are to be fully understood and predicted. Cyanobacteria also hold the potential to influence fish consumers by altering physical habitat and zooplankton prey availability. Acting much like an ecosystem engineer (sensu Jones et al., 1994), cyanoblooms could alter the horizontal and vertical distribution of fish, and hence overlap of predators and their prey, by reducing light intensity in the water column through shading (Berke, 2010). Reduced light penetration would be expected to hamper foraging (and subsequently growth) of visual predators, including planktivores (Engström-Öst et al., 2006; Wellington et al., 2010; Manning et al., 2014) and especially piscivores (De Robertis et al., 2003). In fact, a laboratory experiment showed that simulated algal turbidity could reduce foraging of planktivorous larval and juvenile yellow perch (Perca flavescens;

Mitchill 1814) more than sediment turbidity at similar concentrations (Wellington et al., 2010), likely because algal cells selectively absorb photosynthetically active radiation (PAR; Gallegos et al., 1990) and algal colonies form larger particles than sediments do (Yang and Kong, 2012). In addition, cyanobacterial blooms can provide small-bodied fish a predation refuge from piscivorous fish (Engström-Öst et al., 2006; Engström-Öst and Mattila, 2008), which could offer a short-term survival benefit that is offset by a cost in long-term growth. Using a modeling approach, Manning et al. (2013) showed that this trade-off might exist for age-0 yellow perch in western Lake Erie (USA-Canada) as fish in areas of high algal turbidity were smaller but more abundant than those in non-bloom areas. Because small, poor-condition fish are expected to experience lower over-winter survival and be more vulnerable to size-selective predation than their larger counterparts (Post and Evans, 1989; Ludsin and DeVries, 1997; Lundvall et al., 1999), the extent to which these short-term benefits to survival lead to increases in long-term growth and recruitment to older life stages remains unclear. Toward better understanding the impacts of cyanoblooms on the structure, function, and dynamics of aquatic food webs in the wild, field sampling was conducted in western Lake Erie during 2013–2014 to quantify how physicochemical conditions (e.g., light availability, MC concentration), phytoplankton communities, zooplankton biomass, and fish foraging vary across a gradient in cyanobacterial concentration. Cyanoblooms were hypothesized to: 1) create habitats characterized by high Microcystis dominance, reduced “edible” phytoplankton biomass (via competition for nutrients and light' sensu Huisman et al., 2004; Carey et al., 2012; and allelopathic mechanisms, Leao et al., 2009), and conditions that could impair foraging by higher consumers (i.e., reduced water clarity/light, high MC levels); 2) support zooplankton that selectively graze on phytoplankton (e.g., small-bodied cladocerans and calanoid copepods; Lampert, 1987; Cyr and Curtis, 1999; Leitão et al., 2018) with generalist, filterfeeding, herbivorous zooplankton either being negatively affected or perhaps showing no change (owing to the negative effects of MCs and poor food quality being offset by reduced predation pressure from zooplanktivorous fish); and 3) be negatively related to fish abundance, owing to low light conditions and MC exposure leading to reduced availability and consumption of zooplankton (Wellington et al., 2010) and high MC accumulation in the body, especially in epipelagic fishes relative to more benthic-oriented ones. By measuring the responses of habitat, plankton, and fish in situ across a cyanobacterial gradient, this study sought to test these hypotheses so as to better understand how the ecology of aquatic food webs are affected by cyanoblooms, which are predicted to worsen in the face of continued climate change (Michalak et al., 2013). 2. Methods 2.1. Study system and site selection Lake Erie, which is the 10th largest lake in the world (25,700 km2) and drains the largest watershed in the North American Great Lakes basin, is an excellent system to study cyanoblooms. These blooms, which occur seasonally and span an area as great as 6,000 km2 (Stumpf et al., 2012), primarily initiate in the shallow (mean depth of 7.4 m) western basin of Lake Erie (Fig. 1), owing to high inputs of nutrients from the Maumee and Sandusky rivers (Scavia et al., 2014). Given these inputs from these southshore tributaries, as well as larger inflows of less-productive, cold water from the Detroit River from the north, cyanoblooms are generally most concentrated in the southern portion of the west basin, although winddriven water circulation can cause them to extend throughout the western basin during August (Wynne and Stumpf, 2015). 2

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Fig. 1. Map of sampling locations in western Lake Erie during 2013 and 2014. Sites where fish were captured and analyzed for microcystins are indicated by site name (i.e., B15, B16, B28, N12). Major rivers entering the western basin are noted: Detroit River; Maumee River; and Sandusky River. The inset map is of the Laurentian Great Lakes region with a box indicating study location.

surface to 1 m off the lake bottom. Water transparency was measured using a Secchi disk (nearest 1 cm) that was lowered on the shaded side of the R/V Carmen. Integrated water samples (n = 2 per site) were collected from the surface to twice the Secchi depth using a 2.5 cm diameter tube sampler. These samples were used to quantify primary producer biomass, nutrients, chlorophyll a, and total MCs. The samples used to identify and count phytoplankton were immediately preserved with Lugol’s iodine and stored in the dark. All other water samples were kept on ice after collection until laboratory processing. The wholewater nutrient and MC samples were stored frozen at -20 ℃ within 24 h of collection.

Because Lake Erie’s cyanoblooms are remotely monitored on a daily basis by satellites (Kutser et al., 2006; Wynne and Stumpf, 2015), satellite-derived maps of cyanobacteria intensity (Tim Wynne, National Oceanic and Atmospheric Administration, Ann Arbor, MI) and truecolor MODIS satellite images (https://coastwatch.glerl.noaa.gov/ modis/; when NOAA maps were not available) were used to guide sampling. After designating regions as bloom or non-bloom based on satellite imagery, sites from inside and outside of cyanoblooms were randomly sampled to achieve a balance of high, moderate, and low cyanobacteria concentrations. All selected sites were > 5 m depth to avoid nearshore effects associated with sediment runoff. Upon arrival at each potential site, cyanobacterial abundance was measured using a FluoroProbe (bbe Moldaenke, Schwentinental, Germany) that was lowered vertically from the surface to 1 m off the lake bottom. In total, 75 sites were sampled during the 2013 and 2014 cyanobloom seasons: 17 sites during August 9 to September 9, 2013; and 58 sites during August 8 to September 16, 2014 (Fig. 1). At all sites, water quality attributes, phytoplankton, and zooplankton were sampled. Due to logistical constraints, fish sampling was more limited, with only 13 sites being conducted during 2013 and 44 sites being sampled during 2014. These collections are described in fuller detail below.

2.3. Phytoplankton The phytoplankton community was measured in several ways. Using a bench-top FluoroProbe (bbe Moldaenke, Schwentinental, Germany), the in vivo concentration (nearest 0.01 μg/L as chlorophyll a equivalents) of four groups of primary producers (Cyanobacteria, Chlorophyta, Cryptophyta, and diatoms [Bacillarophyceae]) was measured by analyzing three 25 mL aliquots from an integrated water sample collected at each site (per above). During recent years, several studies have shown the validity of using a FluoroProbe to quantify the algal community in Lake Erie (Ghadouani and Smith, 2005; Twiss, 2011; Bridgeman et al., 2012). To estimate chlorophyll a, which has historically been used as a proxy of total phytoplankton biomass in Lake Erie (Rockwell et al., 2005), whole-water samples were filtered from each site through 0.7 μm pore-size filter papers (GF/C Whatman). Filters were stored in the dark at -20 ℃ until analysis, which always occurred within 30 d of collection. Chlorophyll a was extracted manually

2.2. Physicochemical attributes and water collection Several physicochemical attributes were measured at each site. Temperature (nearest 0.1 ℃), turbidity (nearest 0.01 FNU), fluorescence (nearest 0.01 RFU), and pH (nearest 0.1) were measured at 1 m intervals using a multisensor probe (EXO2 sonde, Yellow Springs Instruments, Inc., Yellow Springs, OH) that was lowered from the water 3

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with 90% aqueous acetone and its concentration was determined with a spectrophotometer (UV-1800, Shimadzu Corp; Lorenzen, 1967; USEPA, 1997). Samples were also manually processed to determine the composition and biomass of primary producers, following methods established for Lake Erie (Kane, 2004). Briefly, whole-water samples preserved with Lugol’s were condensed by pouring a mixed sample into a 250 mL graduated cylinder. After these samples were allowed to settle (minimum of 5 d) the top 220 mL was removed. An aliquot of the concentrated sample was weighed and then spread onto a tared Utermohl counting chamber for visual identification with an inverted microscope (Vert A1, Zeiss, Gottingen, Germany). Large primary producers (e.g., Microcystis colonies and dinoflagellates) were counted at a magnification of 200x, whereas all other taxa were identified and counted at 400x magnification. A minimum of two transects and 400 individual units (e.g., cells, colonies, or filaments) were examined. The first 40 Microcystis colonies encountered with a digital camera were photographed (Rebel Eos T3i, Canon Inc.) and, for each colony, the equivalent spherical diameter (nearest 0.1 μm), average cell diameter (nearest 0.01 μm), and total number of cells were measured using image analysis software (Zen lite, Zeiss, Gottingen, Germany). For all other taxa, the first 20 phytoplankton units of each taxon were measured with an ocular micrometer (nearest 0.01 μm). Biomass for each taxon was estimated by calculating bio-volume, using appropriate geometric shapes (see Kane, 2004) and assuming their density was equal to water (i.e., 1 g cm−3). Phytoplankton and cyanobacteria can vary widely in terms of size, fatty acid content, and toxin content, which can affect their selectivity by zooplankton consumers (Fulton and Paerl, 1987; DeMott and Moxter, 1991; Martin-Creuzburg and von Elert, 2009; Ger et al., 2016). Thus, to understand how the availability of high-quality food for zooplankton varied with cyanobacteria biomass, phytoplankton and cyanobacteria taxa were classified as “edible” or “inedible”, based on previously defined criteria (see Kane, 2004) using cell-count information. The inedible group included all cyanobacteria taxa, large taxa (e.g., filaments, Closterium spp., Aulacoseira spp.), and taxa with spines (i.e., Micractiniumspp.). All others were considered edible.

(M5 Wild Heerbrugg, Switzerland). Lengths of the first 20 individuals for each taxonomic group were measured and taxon-specific length-dry mass regression equations were used to estimate biomass (Culver et al., 1985). Herein, only trends in total crustacean biomass and major zooplankton groups—Daphnia spp.; smaller cladocerans (comprising four genera: Bosmina [formerly Eubosmina], Ceriodaphnia, Chydorus, and Diaphanosoma); cyclopoid copepods; calanoid copepods; and the predatory cladoceran, Leptodora kindtii (Focke 1944)—are reported. 2.6. Fish Fish were sampled during 2013 and 2014, although sampling was more limited than other aspects of the food web. During 2013, epipelagic fish were sampled using a metered, 1 x 2 m aluminum-framed neuston net with 1800 μm mesh (Sea Gear Corporation, Melbourne, FL) that was towed horizontally at the water surface (tow durations = 5–10 min). These tows were conducted at 13 sites in both bloom (n = 7) and non-bloom (n = 6) conditions in day/night pairs, with each day/night pair occurring within one 24 h period. Because these tows primarily only captured emerald shiners (Notropis atherinoides, Rafinesque 1818), two different sampling gears were employed the following year. During 2014, both pelagic and benthic fish were targeted, especially age-classes that are considered prey for adult walleye, Sander vitreus (Mitchill, 1818), and yellow perch (Knight and Vondracek, 1993; Knight et al., 1984). All 2014 sampling occurred during daylight hours at a subset of sites (n = 44). To capture pelagic fishes (e.g., adult shiners, Notropis spp., Rafinesque 1818; age-0 gizzard shad, Dorosoma cepedianum, Lesueur 1818), an epipelagic Mamou net (9 m head rope and 4 m deep; 44 mm mesh with 12 mm liner; Innovative Net Systems, Milton, LA) was towed at the water surface. To target benthic fishes (e.g., age-0 yellow perch; age-0 white perch, Morone americana, Gmelin 1789; all ages of round goby, Neogobius melanstomus, Pallas 1814), a bottom skate trawl (5 m head rope, 38 mm mesh with 12 mm liner; Innovative Net Systems, Milton, LA) was used. All trawls were towed at ˜ 1 m s−1 for 5–10 min. Fish from each net tow were sorted, counted, and immediately frozen at -20 ℃ for later diet and MC analyses.

2.4. Microcystin content in water

2.7. Fish consumption and prey selectivity

Total (intracellular plus extracellular) MCs in water were measured using enzyme-linked immunosorbent assay (ELISA) kits (96-well format) for microcystins/nodularins (ADDA, Abraxis). Samples were exposed to two freeze/thaw cycles to rupture cells and release the toxins, which were then analyzed with a plate photometer (Dynex Technologies MRX TC Revelation, Chantilly, Virginia) following manufacturer instructions. Each sample and six standards were analyzed in duplicate. Plate-specific standard regression curves that spanned 0.01–5 μg L−1 were used to determine the concentration for each sample. When samples exceeded the 5 μg L−1 standard, serial dilutions were performed and samples were reanalyzed to ensure accuracy.

Diet analyses were conducted to quantify consumption rates and prey selectivity. Diet analyses were conducted on both age-0 white perch and age-0 yellow perch (n = 5 per site per species), but only at sites where both species co-occurred in bottom trawls (n = 20 sites). Adult emerald shiner diets, collected with the epipelagic trawl, were also analyzed at 15 of these sites. Prior to analysis, each individual was thawed and either the differentiated stomach (white perch and yellow perch) or the intestinal tract at the first bend (emerald shiner) was removed. If an individual did not have prey in its gut, an additional individual (when available) was analyzed until a minimum of five nonempty stomachs for each taxon at each site was processed. All prey items and material from the stomach or gut segment was removed and examined at 50x magnification on a dissecting microscope (M5 Wild, Heerbrugg, Switzerland). Prey items were identified to the lowest taxonomic resolution possible and only heads of partial prey items were counted. Afterwards, all stomach contents and fish were dried in separate, pre-weighed aluminum tins at 18 ℃ until tins maintained a constant weight (˜48 h). For each individual fish, its mass-specific consumption (C; g/g) was estimated:

2.5. Zooplankton At each site, zooplankton was sampled with a vertically towed 64 μm mesh, 0.5 m diameter net that was fitted with a flowmeter (2013: General Oceanics Inc. model 2030R; 2014: Sea-Gear Corp. model MF315; average sampled lake volume = 1.3 m3; range 0.5-3.7 m3). All zooplankton tows occurred during daylight hours and included the entire water-column to remove the potential influence of diel, vertical migration behavior for some taxa. Zooplankton samples were immediately filtered into 0.5 L bottles and preserved with a 4% sugarformalin solution (Haney and Hall, 1973). In the laboratory, the samples were diluted (to 0.5–3.0 L) and at least two 3–10 ml subsamples were transferred to a plankton counting wheel where individuals were identified and counted at 50x magnification on a dissecting microscope

C=

g prey dry mass g fish dry mass

(1)

Chesson’s α statistic (Chesson, 1983) was used to determine if fish were actively selecting prey relative to the prey’s availability in the environment. Selectivity of fish k for prey type i is defined as: 4

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i,k

=

ri,k /pi m r /p i= 1 i,k i

from previous studies of Lake Erie cyanoblooms (Kane et al., 2014; Bridgeman et al., 2012), manual counts showed Microcystis was the dominant cyanobacteria genus with lesser amounts of other coccoid cyanobacteria genera (i.e., Pseudanabaena, Merismopedia, and Chroococcus) also present at sites with high cyanobacteria concentration. Overall, Microcystis accounted for 84–100% of cyanobacteria biomass (estimated from manual cell counts) across the study sites.

(2)

where ri,k is the proportion of prey type i in the gut of fish k and pi is the proportion of prey type i at the site for m different prey types. Owing to lack of abundance information on benthic macroinvertebrates, only zooplankton prey were included in these analyses. At two sites, daphnids were absent in the environment (zooplankton sample) but present in fish diets; thus, the value of one-half of the detection limit, determined as one individual per volume of sample examined, was used for the environmental density.

3.2. Phytoplankton Strong, significant relationships were found between cyanobacteria concentration and most of the other physicochemical and primary producer attributes that were measured (Table 1, Fig. 2). Extracted chlorophyll a concentration, which has historically been used as a proxy of total phytoplankton biomass (Rockwell et al., 2005), was positively related to fluorometric cyanobacteria concentration (Table 1, Fig. 2A), suggesting that cyanobacteria dominated primary producer biomass across study sites. Likewise, water-column total MC concentration (Table 1, Fig. 2B) was positively related to fluorometric cyanobacteria concentration, with some values exceeding Ohio’s threshold for recreational contact for small children (6 μg L−1total MC, OEPA 2016). In addition, as expected, water transparency (as measured by Secchi depth) was negatively related to cyanobacteria concentration, with transparency declining from a maximum depth of ˜4 m to less than 0.5 m across the concentration gradient (Table 1, Fig. 2C). The composition of the phytoplankton community also varied with cyanobacteria concentration. While total edible phytoplankton biomass was unrelated to cyanobacteria concentration (Table 1, Fig. 2D), a result that ran counter to expectations (of a negative relationship), a decline in percent composition of the three major phytoplankton groups measured by the FluoroProbe (i.e., chlorophytes, diatoms, and cryptophytes) was found (Fig. 3), as expected. In addition, the fluorometric concentration of two groups of phytoplankton, diatoms and chlorophytes, which include many taxa considered “edible” (< 35 μm) to most herbivorous zooplankton (Cyr and Curtis, 1999), decreased with increasing cyanobacteria concentration (Table 1, Fig. 4A-B). By contrast, cryptophyte concentration was unexpectedly positively correlated with cyanobacteria concentration (Table 1, Fig. 4C).

2.8. Microcystin content in fish To assess whether prey-fish residing in cyanoblooms were accumulating cyanotoxins, MCs were measured in both pelagic (adult emerald shiner and age-0 gizzard shad) and benthic (age-0 yellow perch and white perch) fish. Individuals (n = 3–5 per species) were chosen from four sites that had high fish catches and either a high or low level of cyanobacterial concentration (2–22 μg L−1) and total MCs (0.6–11 μg L−1). Two MC variants, MC-LR and MC-RR, which are both highly toxic and abundant in Lake Erie during the Microcystis bloom season (Dyble et al., 2008) were quantified. To quantify MC concentrations, an optimized extraction and purification procedure that results in MC-LR and MC-RR recovery rates in excess of 93% was used (Manubolu et al., 2019). Briefly, whole individual fish were homogenized and MCs were extracted from 1 g fish tissue using methanol:water:butanol (75:20:5) and eluted them with a hydrophilic–lipophilic balance solid-phase extraction column (HLB, Oasis PRiME, part #186,008,718). The eluted samples were then analyzed using a UPLC chromatograph (1200 SL series, Agilent Technologies, Santa Clara, CA) that was interfaced with a triple quadrupole mass spectrometer (QTrap 5500, ABSciex, Concord, Canada). Both congeners could be measured with a high degree of confidence, as their average ( ± 1 standard deviation, SD) Instrument Detection limits were 0.038 and 0.024 ƞg for MC-LR and MC-RR, respectively. 2.9. Statistical analyses Regression analysis was used to determine the relationship between cyanobacterial concentration and each response variable (Y; e.g., phytoplankton biomass, zooplankton biomass, fish abundance), with data pooled between years. Mixed-effects linear regression models of the form, Y= 0 + 1 cyano+ Z+ , were used to partition the variance among the predictor variable (cyano = cyanobacteria concentration based on fluorometry) and sampling parameters (Z = sampling week and year), which were treated as random effects. Due to the hierarchical structure of the data, degrees of freedom could not be determined, and hence, calculating a p-value statistic was not attempted (Baayen et al., 2008). Therefore, the regression coefficient, β, with 95% confidence intervals (CIs) is presented instead and significance was assessed by determining if zero was encompassed within the CIs. All data were square-root transformed to improve model fit before being analyzed in R (R Core Team, Version 3.3.2). All mixed-model regression analyses were computed using the LMER function and CIs with a bootstrapping method, CONFINT function, in the lme4 package (Bates et al., 2015). To further examine fish distribution with cyanobacteria concentration, a paired t-test was used to determine if 1+ emerald shiner catches differed between day and night tows during 2013.

3.3. Zooplankton Cladocerans were the dominant crustacean zooplankton at all sampled sites. Small cladocerans represented the largest proportion of the biomass (mean = 49%) of the four major zooplankton groups (Daphnia spp., small cladocerans, calanoid copepods, cyclopoid copepods), with the genus Bosmina (including former Eubosmia spp.) being the dominant taxa within this group. Daphnids comprised an average of 15% of the total zooplankton biomass, with Daphnia retrocurva (Forbes 1882) being the dominant species of Daphnia at 90% of the sites. Copepods were the next dominant zooplankton group. Calanoid copepods comprised 22% of total zooplankton biomass, and typically were dominated by immature copepodites unable to be identified to species. Cyclopoid copepods were the smallest group in total biomass (mean = 9%) and were predominately represented by Mesocyclops edax (Forbes 1890). All four of these major zooplankton groups and total crustacean zooplankton biomass showed a positive response to cyanobacterial concentration (Table 1, Fig. 5A-E), at least from low to intermediate concentrations. This finding counters the expectation that only smallbodied zooplankton taxa would increase. Two other general trends were noted for total crustacean biomass and all four major groups: 1) their biomass began to approach zero as cyanobacterial concentrations declined to near zero; and 2) each group’s biomass appeared to show a non-linear (asymptotic to unimodal) relationship with cyanobacteria concentration (Fig. 5A-D), indicating that zooplankton biomass per unit of cyanobacteria declined at high cyanobacterial concentrations.

3. Results 3.1. Cyanobacteria Physicochemical and food web attributes were sampled across a wide range of cyanobacteria concentration. Detectable levels of cyanobacteria, as measured by the FluoroProbe, were found at all sites, with concentrations ranging from 0.1–44 μg L−1 (as chlorophyll a equivalents). Similar to results 5

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Table 1 Relationship between cyanobacteria concentration (μg L−1 chlorophyll a equivalents) and limnological attributes, phytoplankton, zooplankton, and fish abundances in western Lake Erie during the cyanobloom season, 2013–2014. Reported are the coefficients for cyanobacteria concentration (β Cyano; fixed effect) and sampling year (β Year; random effect) of each linear mixed model and its corresponding bootstrapped 95% confidence interval (CI). A relationship was considered statistically significant when the CI estimate aroundits coefficient excludes zero (indicated with bold-face font). Fish were only sampled during 2014; thus, sampling year was not included in those two models.

of effect was reflected in total benthic trawl catch, which was also unrelated to cyanobacterial concentration (β = -0.4 ± 0.7, Table 1). In paired (day/night), epipelagic tows conducted during 2013, total catch (primarily emerald shiners) was significantly higher during day than night when towing within a bloom (paired t-test, p = 0.024; Fig. 7). While non-bloom sites showed the same trend in total catch, with elevated day catches, this difference was not significant (paired ttest, p = 0.33; Fig. 7).

Finally, the predatory cladoceran, L. kindtii, showed a negative relationship with cyanobacterial concentration (Table 1) and was not present at sites with high, > 20 μg L−1, cyanobacterial concentration (Fig. 5F). 3.4. Fish abundance Epipelagic and benthic fishes showed different relationships with cyanobacterial concentration. The surface trawls captured a variety of fishes, including age-0 white perch, age 1+ brook silverside (Labidesthes sicculus, Cope 1865), age-0 smallmouth bass (Micropterus dolomieu, Lacepède 1802), age 1+ rainbow smelt (Osmerus mordax, Mitchill 1814), and age-0 walleye. These trawls, however, were dominated by only two species: emerald shiners, which occurred at 90% of the sites and averaged 89% of the total catch (by density); and gizzard shad, which were captured at 30% of the sites and averaged 32% of the total catch at those sites. While total catch in surface trawls was positively related to cyanobacterial concentration (Table 1), the response of individual species varied (Fig. 6). For example, age 1+ emerald shiner abundance was unrelated to cyanobacterial concentration (Table 1, Fig. 6C), whereas age-0 gizzard shad abundance showed a positive relationship (Table 1, Fig. 6A). In comparison to the surface trawls, the bottom trawls captured a more diverse fish assemblage, which included age 1+ round goby, age 1+ trout-perch (Percopsis omiscomaycus, Walbaum 1792), age 1+ logperch (Percina caprodes, Rafinesque 1818), age-0 freshwater drum (Aplodinotus grunniens, Rafinesque 1819), age-0 smallmouth bass, age 1+ rainbow smelt, age-0 catfish (Ictalurus spp.), age-0 walleye, age 0+ yellow perch, and age 0+ white perch. Age-0 white perch were typically the most abundant taxon in bottom trawls, being caught at 70% of sites and averaging 48% of the total catch across sites. Age-0 yellow perch were also captured in 59% of the sites; however, they were less abundant than white perch, averaging 9% of the total catch (based on density). Unlike the two dominant surfacedwelling species (emerald shiners and gizzard shad), neither age-0 yellow perch nor age-0 white perch abundance was related to cyanobacterial concentration (Table 1, Fig. 6B and D, respectively). This lack

3.5. Fish consumption and prey electivity How foraging by the most abundant fish species captured in surface trawls (age 1+ emerald shiner) and bottom trawls (age-0 white perch and age-0 yellow perch) varied with cyanobacteria conditions was investigated. Three general results were found. First, mass-specific consumption did not vary predictably with cyanobacteria concentration for any of the species (i.e., consumption did not differ between bloom vs. non-bloom sites; Table 2). Second, zooplankton was the dominant prey type (by count) in emerald shiners and white perch, representing 48%–88% of the diet in bloom sites and 91%–98% in non-bloom sites (Table 2; Fig. 8). Third, the proportion of the diet that was comprised of benthic invertebrates was higher in cyanobloom sites than non-cyanobloom sites for all three species (Table 2; Fig. 8). Consumption of benthic invertebrates (i.e., Chironomidae larvae, Amphipoda, Dreissena spp.) was especially high in age-0 yellow perch within the bloom, where benthic invertebrates accounted for up to 52% of the diet. High variability in selectivity of zooplankton prey by fish was evident among focal fishes. Age 1+ emerald shiners showed the strongest preference for small cladocerans, with 37% of individuals showing positive selection (i.e., α > 0.25, the null or reference condition). Age0 white perch also showed a preference for small cladocerans, as well as cyclopoid copepods, with 55% and 45% of individuals showing positive selectivity for these taxa, respectively. By contrast, age-0 yellow perch appeared to strongly prefer cyclopoid and calanoid copepods, with 58% and 45% of individuals showing positive selection for these taxa, respectively. While cyanobacterial concentration did not appear to 6

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Fig. 2. Relationships between limnological attributes and fluorometry-derived cyanobacteria concentration (μg L−1 chlorophyll a equivalents) in western Lake Erie during August through September, 2013–2014: A) chlorophyll a; B) microcystins; C) Secchi depth; and D) edible phytoplankton biomass (determined by a cell-counting method). Each point represents a sampling site with icon shape representing a year (▲ = 2013, ● = 2014). All relationships that are considered statistically significant (Table 1) include a least-squares linear regression line. Note, all data were square-root transformed.

61% of the MC burden in 27 of the 29 MC-positive individuals. Microcystins were found in all age-0 gizzard shad and age-0 yellow perch analyzed, whereas it was only found in 64% of age 1+ emerald shiners and 38% of the age-0 white perch tested (Table 3). Mean total MC levels were relatively low in emerald shiners (1.7 ƞg g−1) and white perch (0.5 ƞg g−1). By contrast, they were higher in both yellow perch (10ƞg g−1) and gizzard shad (275ƞg g−1). The maximum whole-body concentration found in the two “high-MC” species were 36 ƞg g−1 for yellow perch and ∼1,000 ƞg g−1 for gizzard shad. The highest MC levels in fish were found in gizzard shad at the site (B16) with highest Microcystis concentrations in the water (Table 3).

influence emerald shiner prey selectivity, white perch and yellow perch did show strong differences in prey-selection along a cyanobacterial concentration gradient (Fig. 9). White perch selectivity increased with cyanobacterial concentration for small cladocerans (i.e., significantly positive βcyano), decreased with cyanobacterial concentration for Daphnia spp., and showed no trend for cyclopoid or calanoid copepods (Fig. 9). While white perch did not select for calanoid copepods overall, they did select for two large calanoid species (Eurytemora affinis, Poppe 1880; and Epischura lacustris, Forbes 1882) at sites with low cyanobacterial concentration (i.e., non-bloom sites). Positive selection for cyclopoid copepods (predominately Mesocyclops edax) by yellow perch increased with increasing cyanobacterial concentration, whereas selection for calanoid copepods decreased with cyanobacterial concentration (Fig. 9).

4. Discussion 4.1. Overview

3.6. Fish microcystin levels

This field investigation, which was conducted during two cyanobloom seasons (2013–2014) in western Lake Erie, revealed numerous important insights. First, as expected, cyanoblooms created distinct habitats in western Lake Erie characterized by high primary producer

Microcystins were detected in individuals of all four fish species tested (Table 3). Microcystins were found in 74% (29 of 39) of the individuals sampled. MC-LR was the more abundant congener, with it comprising >

Fig. 3. Primary producer community composition in western Lake Erie during August-September, 2013–2014. Data were derived via fluorometry at samples collected in the photic 756 zone. Percent concentration for each site (n = 75) with samples ordered from lowest to highest cyanobacteria concentration. To facilitate interpretation, vertical dashed lines were added to indicate arbitrary breakpoints for cyanobacteria concentrations (μg L−1 chlorophyll a equivalents).

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mechanism to help understand MC accumulation in top predators (e.g., adult walleye and yellow perch) in Lake Erie (Poste et al., 2011; Wituszynski et al., 2017). Collectively, these findings demonstrate that the recent increase in cyanoblooms in Lake Erie holds great potential to alter food web interactions and, perhaps, the production and safety of this ecosystem’s most valued fisheries. Below, this perspective is explained in more detail and areas for continued research are highlighted, which can help agencies better understand and predict how continued increases in cyanoblooms might impact the ecology of aquatic ecosystems and the services that they provide. 4.2. Zooplankton At the outset of this study, cyanoblooms were expected to differentially affect the various zooplankton taxa. Specifically, large-bodied, non-selective filter feeders such as Daphnia spp. were expected to be lower in areas of high versus low cyanobacteria concentration, owing to (i) mechanical interference associated with cyanobacteria that form dense colonies (Gliwicz and Lampert, 1990; Deng et al., 2008), (ii) the production of cyanotoxins (e.g., MC) that have been shown to impair zooplankton development, growth, and survival (DeMott and Moxter, 1991; da Costa et al., 2013), and (iii) the poor nutritional value (e.g., low essential fatty acids) of these “inedible” species (Gulati and DeMott, 1997). Or, at best, Daphnia spp. were expected to show no relationship with cyanobacterial concentration, owing to the above-mentioned negative effects being offset by reduced predation by visual-feeding planktivores (O’Brien, 1979). Smaller cladocerans (e.g., Bosmina spp., including former Eubosmina spp.) also were expected to increase, as their selective-feeding strategy is less likely to be inhibited cyanobacteria (Deng et al., 2008; Tõnno et al., 2016). Small cladocerans also were expected to benefit from the reduction of large-bodied daphnids, which are considered superior competitors in warm, productive growing conditions (Lampert, 1987; Cyr and Curtis, 1999). Thus, the finding that both large (Daphnia spp.) and small cladocerans (Bosmina spp.) increased with increasing cyanobacterial biomass was surprising. Multiple factors may have contributed to the positive relationships found between all four major groups of crustacean zooplankton and cyanobacterial biomass. Below, four explanations are offered: 1 Bottom-up effects. Even though Microcystis clearly dominated phytoplankton biomass at high cyanobacterial concentrations, edible phytoplankton was still present at these sites. Thus, sufficient food resources may have existed that could sustain the zooplankton community, especially highly selective grazers such as small cladocerans (DeMott and Moxter, 1991), which utilize both filter and raptorial feeding (Cyr and Curtis, 1999). In support of this notion, cryptophytes significantly increased in concentration with increasing cyanobacteria, thus possibly providing ample, highly nutritious food resources (Brett et al., 2009; Taipale et al., 2009) despite the loss of diatoms and chrysophytes. Solis et al. (2018) confirmed that small-bodied cladocerans show a clear preference for cryptophytes along with smaller diatoms and chlorophytes that are less than 30 um, and that the small cladoceran Bosmina (formerly Eubosmina) coregoni (Baird 1857) had more cryptophytes in its diet tracts during and after cyanobacterial blooms than before their occurrence. 2 Microbial grazing. Cyanobacterial species may provide additional food resources to zooplankton. Indeed, zooplankton have been shown to consume heterotrophic bacteria and protozoa in other eutrophic, cyanobacteria-rich lakes (Work and Havens, 2003). The dense colonies formed by Microcystis that were observed in this study may also have harbored a high biomass of heterotrophic bacteria (de Kluijver et al., 2012), or have led to an increase in ciliate production (Mrdjen et al., 2018), thus providing an alternative energy source for zooplankton (Vaqué et al., 1992; Kamjunke et al., 1997). In these ways, cyanobacteria may have benefited

Fig. 4. Relationships between fluorometric measurements of cyanobacteria (μg L−1 chlorophyll a equivalents) and three major phytoplankton groups in western Lake Erie during August-September, 2013–2014. Panels indicate relationships between cyanobacteria and A) chlorophytes, B) diatoms, and C) cryptophytes. Each point represents a sampling site and each icon shape represents a year (▲ = 2013, ● = 2014). All relationships are considered statistically significant (Table 1), with the least-squares linear regression line plotted. Note, all presented data were square-root transformed.

biomass, reduced water clarity, and high ambient microcystin (MC) concentrations. Unexpectedly, these blooms also supported a high biomass of ‘edible’ phytoplankton. Second, contrary to expectations, the biomass of all major crustacean zooplankton taxa (i.e., Daphnia spp., small cladocerans, cyclopoids, and calanoids) increased with increasing cyanobacterial concentration, suggesting that zooplankton benefit from cyanoblooms more than they are hampered by them. Third, despite the presence of high concentrations of total MCs, as well as high biomass of colonial Microcystis, several species of prey-fish, including age 1+ emerald shiners and age-0 yellow perch, white perch, and gizzard shad, used the cyanoblooms to forage. Fourth, although no detectible differences in mass-specific consumption rates were found for any of these fishes between sites of high and low cyanobacteria concentration, their diet composition did vary between sites, perhaps as a response to cyanobacteria re-structuring zooplankton communities. Finally, MCs were detected in all four of the prey-fish species sampled, with the highest levels (˜1,000 ƞg g−1) being found in age-0 gizzard, thus providing a 8

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Fig. 5. Dry weight biomass of major crustacean zooplankton groups plotted against fluorometric measurements of cyanobacterial concentration (μg L−1 chlorophyll a equivalents) in western Lake Erie during August-September, 2013–2014: A) daphnids; B) small cladocerans; C) calanoid copepods; D) cyclopoid copepods; E) total crustacean zooplankton, and F) Leptodora kindtii. Each point represents a sampling site with icon shape representing year (▲ = 2013, ● = 2014). All relationships considered statistically significant (Table 1) are indicated with a locally weighted smoothing line (shaded area ± 1 standard error). Note,all presented data were square-root transformed.

zooplankton grazers by offering an alternative way for zooplankton to obtain energy (carbon), highlighting the need for future studies that quantify the role of microbes (e.g., heterotrophic protists) in regulating energy flow to higher consumers during the cyanobloom season. An important consideration is that, while feeding on suboptimal food resources (e.g., cyanobacteria and heterotrophic bacteria) may sustain crustacean zooplankton communities in the shortterm, doing so may have long-term, negative consequences. A growing body of research has shown that consuming cyanobacteria can be harmful to future development, growth, survival, and fitness of zooplankton, owing to their deficiency in essential fatty acids such as DHA and EPA (Lürling, 2003; MartinCreuzburg and von Elert, 2009). This negative effect would be magnified, if the cyanobacteria were laden with cyanotoxins (e.g., MCs), which has been shown to lead to similar reductions in

future performance and fitness (Ger et al., 2010; Sarnelle et al., 2010; Bednarska et al., 2014). 3 Refuge from predation. Reduced predation risk offered by lowtransparency blooms may also have benefited zooplankton, especially Daphnia spp. In support of this hypothesis, significantly reduced selection of Daphnia spp. by both age-0 yellow perch and age0 white perch was found in sites with high cyanobacteria concentration. This finding supports previous laboratory research, whichshowed that foraging by zooplanktivorous age-0 yellow perch declines in the presence of simulated phytoplankton turbidity (Wellington et al., 2010). Nieman and Gray (2019) also showed that reaction distance (a behavioral proxy for measures of visual acuity) of emerald shiners declined by ˜50% in the presence of algal turbidity. Additionally, a predatory zooplankter, Leptodora kindtii, concentrated at lower cyanobacteria biomass than all other 9

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Fig. 6. Fish catch per unit effort (CPUE, # individuals per 5 min trawl) plotted against cyanobacteria concentration (μg L−1 chlorophyll a equivalents) in western Lake Erie during August-September 2014: A) age-0 gizzard shad (surface trawl); B) age-0 yellow perch (bottom trawl); C) adult (age 1+ emerald shiners (surface trawl); and D) age-0 white perch (bottom trawl). Regression coefficients (β) ± 95% confidence intervals are given in Table 1.

4 Species compositional shifts. A range in tolerance to cyanoblooms may exist within zooplankton communities, and species that are more tolerant of (or better competitors in) cyanobacteria should replace those that are less tolerant (or inferior competitors). Such species replacement, which has been shown to occur in communities that have been subjected to changes in habitat/productivity (Kirk and Gilbert, 1992; Hansson et al., 2007; Soares et al., 2009), theoretically could help explain the lack of negative effects of cyanoblooms on total zooplankton biomass. For example, in the current study, within the small-cladoceran group, Bosmina longirostris (Müller 1776) represented 58% of the small-bodied cladocerans at low (< 2 μg L−1) cyanobacteria biomass, whereas B. coregoni biomass increased with cyanobacteria concentrations and comprised nearly half the small-bodied cladoceran biomass at cyanobacteria concentrations > 2 ug L−1. Similarly, Solis et al. (2018) found the same species replacement in response to MC-producing cyanobacteria blooms, suggesting that B. longirostris has a taste discrimination for algal foods (Kerfoot and Kirk, 1991), which may reduce feeding within cyanobacterial blooms. In addition to feeding differences, some species or populations of zooplankton may be more tolerant of MCs than others.

Fig. 7. Differences in mean total catch of fish between epipelagic neuston net tows conducted during daytime and nighttime in western Lake Erie, AugustSeptember 2013. Paired t-tests showed a significant difference between day and night catch for bloom sites (p = 0.02) but not non-bloom sites (p = 0.33). Standard errors (bars) and sample sizes (n) are shown.

zooplankton and was absent inside of cyanoblooms, further supporting the hypothesis that cyanoblooms can offer refuge from predation pressure.

Unfortunately, it is unclear as to whether one or more of these four mechanisms is responsible for the shifts that were observed in the

Table 2 Age 1+ emerald shiner, age-0 white perch, and age-0 yellow perch diet information in areas with and without cyanobacteria in western Lake Erie during AugustSeptember 2014. Reported are fish size information (standard length and wet mass), mass-specific consumption of fish in bloom versus non-bloom areas, the coefficients for cyanobacteria concentration (β Cyano; fixed effect) of each linear mixed model and its corresponding bootstrapped 95% confidence interval (CI), and the percentage of the diet (by count) comprised of zooplankton (ZP) prey. None of these relationships were considered statistically significant as the 95% CI estimate encompassed zero. Sample sizes (nN) are reported in parentheses in the consumption column. Species

Length (mm ± 1 SE)

Mass (g ± 1 SE)

β Cyano (95% CI)

Consumption g prey∙g fish−1 (N) Bloom

Non-bloom

Emerald shiner

53 ± 1.2

2.0 ± 0.1

0.018 (52)

0.019 (22)

White perch

56 ± 0.6

4.1 ± 0.1

0.021 (64)

0.022 (35)

Yellow perch

63 ± 0.6

4.5 ± 0.1

0.017 (64)

0.014 (34)

10

−0.0041 (−0.0170, 0.0079) −0.0005 (-0.0073, 0.0070) 0.0063 (-0.0037, 0.0149)

Prey Percentage (ZP, Non-ZP) Bloom

Non-bloom

84%, 16

97%, 3%

88%, 12%

98%, 2%

48%, 52%

91%, 10%

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Fig. 8. Proportion of prey items (by count) for age 1+ emerald shiners, age-0 white perch, and age-0 yellow perch captured in cyanobloom versus non-cyanobloom sites in western Lake Erie during August-September 2014. Sample sizes are reported in parentheses below the bloom or non-bloom category.

zooplankton community. While many interesting questions remain unanswered, studies that could help tease apart the relative importance of bottom-up versus top-down effects in driving zooplankton community structure and dynamics are especially encouraged. Towards this end, more investigation into how zooplankton grazing on the microbial food web varies across a cyanobacterial gradient, and the degree to which the microbial food web supports zooplankton production, is needed. Likewise, studies that quantify fish abundance and consumption over a 24 -h period (unlike the snapshot approach used herein), both inside and outside of cyanoblooms, would be valuable by allowing for the calculation of daily ration, and in turn, total consumption of zooplankton by the resident fish population (sensu Pothoven et al., 2012). In addition, a similar diel study conducted before, during, and after the cyanobloom season could help better elucidate the relative role of cyanobacteria versus fish predation in driving zooplankton community structure and dynamics. While linear mixed-effects modeling did identify positive relationships between cyanobacteria concentration and the biomass of all four major crustacean zooplankton groups (Daphnia spp., small cladocerans, cyclopoid copepods, and calanoid copepods), a closer inspection of these relationships indicates some non-linearity. The biomass of each zooplankton taxon reached an asymptote above a cyanobacterial biomass of ˜10 μg L−1 and seemed to decline after 20 μg L−1 (see Fig. 5). This notion of highest zooplankton production at an intermediate level of cyanobacterial concentration fits with general ecological theory considering habitat needs (Shelford’s Law of Tolerance; Shelford, 1931), as well as recent empirical research, which has shown zooplankton to increase with Microcystis biomass at low to moderate Microcystis levels but then decline at higher levels (Ghadouani et al., 2003; Chen et al., 2005). Despite a long history of study, the effect of cyanobacteria on zooplankton populations and communities remains inadequate and contradictory (Ger et al., 2014; Wilson et al., 2006). While the present study presents further evidence for multiple effects of Microcystisdominated cyanoblooms on zooplankton biomass and community composition, the need exists for future studies that measure the response of zooplankton communities to blooms of other cyanobacteria, to better identify the generality of these findings. For example, the filamentous cyanobacteria Planktothrix has come to dominate nearshore areas of Lake Erie (e.g., Sandusky Bay and Maumee River, OH, USA; Conroy et al., 2007; Kutovaya et al., 2012), small, inland lakes (e.g., Grand Lake St. Marys and other lakes, OH, USA; Steffen et al., 2014; Mrdjen et al., 2018), and numerous other water bodies around the world (e.g., Paerl and Huisman, 2009). Owing to their different

Fig. 9. A) Index of prey selectivity (Chesson's α) and B) regression coefficients between cyanobacterial concentration (βcyano) and prey selectivity for three fish species (age 1+ emerald shiner, age-0 white perch, and age-0 yellow perch) collected in western Lake Erie during August-September 2014. Selectivities were calculated for four major groups of zooplankton: small cladocerans; daphnids; cyclopoid copepods; and calanoid copepods. Each symbol indicates the regression coefficient and error bars indicate 95% confidence intervals (CIs) that were derived from linear mixed models. Regression coefficients with a CI range that does not include zero (dashed horizontal lines) indicate a significant effect of cyanobacteria concentration on fish selectivity (also denoted by an asterisk). Chessen’s α values above 0.25 are considered significant.

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Table 3 Whole-body concentrations of microcystin (MC, (ƞg ∙ g-fish−1); sum of MC-LR and MC-RR) in fish species collected in western Lake Erie during August 2014. Fish were collected at four sites (see Fig. 1) of varying cyanobacteria concentration (μg L−1 chlorophyll a equivalents) and Microcystis biomass (mg L−1) by surface trawls (emerald shiners, gizzard shad) or bottom trawls (yellow perch, white perch). Fish with non-detectable levels of MC (< 0.1 ƞg MC/g wet fish mass) are indicated (ND). Sample sizes (n) are reported. Site

Water-column cyanobacterial concentration

Water-column Microcystis biomass

Species (N)

MC concentration mean (range)

N12

2.0

0.60

B15

12

3.7

B28

14

3.7

B16

22

11

Age 1+ emerald shiner (5) Age-0 white perch (4) Age-0 yellow perch (4) Age-0 gizzard shad (3) Age-0 yellow perch (3) Age 1+ emerald shiner (3) Age-0 gizzard shad (3) Age-0 white perch (2) Age-0 yellow perch (3) Age 1+ emerald shiner (3) Age-0 gizzard shad (3) Age-0 white perch (2)

1.5 (ND – 2.5) ND 1.3 (0.9 – 1.5) 42 (24 – 55) 32 (2 – 57) ND 6 (2 – 16) 0.9 (0.6 – 1.2) 0.7 (0.5 – 0.8) 3.8 (2.2 – 5.6) 780 (400 – 1000) 0.9 (ND – 1.7)

Ludsin and DeVries, 1997; Hurst, 2007) remains a critical, unanswered question. While fish may be foraging within the cyanobloom due to higher zooplankton prey biomass, the zooplankton present in the bloom may be of poor nutritional quality. Cyanobacteria are notoriously deficient in essential fatty acids and other macro-nutrients (Von Elert et al., 2003; Lürling, 2003; Martin-Creuzburg and von Elert, 2009), which may transfer up the food web to cause nutrient-deficient fish consumers, if cyanobacteria serve as a food source to their zooplankton prey (Brett and Müller-Navarra, 1997; Litzow et al., 2006). Additionally, an increased intake of small cladocerans, with a concurrent reduction in foraging on large-bodied prey, such as Daphnia, may increase foraging effort relative to caloric intake. This reduction in zooplankton prey quality within a bloom may explain the observed trend of fish (e.g., age-0 yellow perch) consuming more benthic prey in bloom sites than in non-blooms sites. Alternatively, this increase in benthic feeding in areas of blooms may also point towards increased demersal behavior within a bloom to avoid higher abundances of toxic Microcystis in the surface waters. Understanding how MCs enter and move up through the food web is critically important from both an ecological and human-health perspective. Given that cyanoblooms are prominent features of the water surface due to their gas vesicles, finding higher whole-body MC levels in epipelagic fishes than benthic species was not surprising (also see Pham and Utsumi, 2018; Jia et al., 2014; Xie et al., 2005). Finding higher concentrations in age-0 gizzard shad relative to the other prey-fishes also was not surprising, given that juvenile gizzard shad greater than 25 mm in total length are known to incorporate phytoplankton into their diet (Roseman et al., 1996; Drenner et al., 1982a, 1982b). Because MCs are known to be endocrine disrupters and have been shown to cause reproductive and developmental impairments (e.g., reduced body and gonad size; lesions on ovaries; and altered thyroid hormone levels) in other species of fish (Yan et al., 2012; Hou et al., 2016, 2017), the possibility exists that chronic consumption of MC-laden prey could have negative effects on prey-fish production. Additionally, if recreationally and commercially fished top predators such as adult yellow perch and walleye, which are heavy consumers of gizzard shad in Lake Erie (Knight et al., 1984), accumulate MCs through consumption of MCladen fish (or through gill uptake via bloom use), the accumulation of MCs in their muscle tissue could potentially pose a human health risk. Indeed, previous studies have documented MCs in the edible tissues of both species in Lake Erie (Poste et al., 2011; Wituszynski et al., 2017).

morphologies, the same concentrations of Microcystis and Planktothrix could yield different responses in water transparency, foraging ability (e.g., non-filamentous cyanobacteria such as Microcystis are more easily grazed than filamentous forms; Severiano et al., 2018), and toxic transfer (e.g., Planktothrix can carry neurotoxins such as anatoxin, which may more acutely detrimental than MCs; Pomati et al., 2000; Viaggiu et al., 2004). Only by filling gaps such as these will the ability be gained to both understand and predict the responses of planktonic food webs to cyanobloom formation, a phenomenon that is expected to only worsen with continued population growth and climate change (Paerl and Huisman, 2009; Paerl and Otten, 2013). 4.3. Fish Similar to the zooplankton community, evidence was presented to suggest that cyanoblooms offer some benefit to prey-fish species in western Lake Erie. Given the negative effects that phytoplankton turbidity appears to have on the foraging ability of visual zooplanktivores such as yellow perch (Wellington et al., 2010) and emerald shiners (Nieman et al., 2018) and the possible health effects associated with chronic exposure to MCs (Malbrouck and Kestemont, 2006), it was somewhat surprising to find numerous species of small-bodied preyfish, residing in cyanoblooms. Age-0 gizzard shad, age-0 white perch, age-0 yellow perch, and age- 1+ shiners were all present in the dense blooms during the daytime. While the possibility exists that bloom-induced reductions in water clarity are responsible for higher catch rates inside versus outside of blooms, the limited 2013 day/night catch data from epipelagic tows presented herein suggest that substantialt net avoidance is not occurring. In support of this notion, similar patterns in day/night abundance occurred inside and outside of cyanoblooms and total fish abundance in cyanoblooms significantly decreased from day to night. As to whether the use of cyanoblooms by shiners and other species is a behavioral response to lower the risk of predation (Engström-Öst et al., 2006; Engström-Öst and Mattila, 2008) and/or to take advantage of abundant zooplankton resources remains unknown. Ideally, future research would expand on the paired day/night approach that was employed, only using better sampling gear (e.g., surface and bottom trawls, fish acoustics) to tease apart the mechanisms. Regardless of knowing the reasons for cyanobloom use by fish, the fact that fish are residing inside the bloom at all suggests that the shortterm benefits gained by using the blooms (higher zooplankton prey biomass, reduced predation risk) outweigh the costs (reduced foraging efficiency, MC exposure). Whether residing in cyanoblooms will lead to long-term negative consequences in terms of reduced growth (as predicted by Manning et al., 2014) that cause reduced survival through size-selective overwinter mortality (sensu Johnson and Evans, 1991;

4.4. Summary & conclusions The findings presented herein from Lake Erie demonstrate that 12

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cyanoblooms can significantly alter key habitat characteristics in freshwater lakes (e.g., water clarity, phytoplankton composition), and those habitat changes can lead to a restructuring of zooplankton and fish communities and altered food web interactions. Contrary to expectations, edible phytoplankton, zooplankton, and fish were present at sites with high concentrations of Microcystis and microcystins (MCs), and several prey-fish species were found actively foraging in cyanoblooms with no apparent impact on mass-specific consumption. By contrast, significant differences in diet selection were evident between bloom and non-bloom sites, with preferences for small cladocerans and benthivory being more pronounced under bloom conditions. Further, bloom utilization by prey-fishes led to high levels of MC accumulation, especially in gizzard shad, offering a potential mechanism for transfer of MCs up to recreationally and commercially fished species (e.g., walleye and yellow perch). Because human-induced environmental change is expected to cause cyanoblooms to continue to increase in their extent and severity in temperate ecosystems (Paerl and Huisman, 2009; Paerl and Otten, 2013), continued monitoring and research that can advance understanding of the short-term and long-term impacts of cyanoblooms on aquatic ecosystems and the services they provide is encouraged.

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