Role of macrophytes as microhabitats for zooplankton community in lentic freshwater ecosystems of South Korea

Ecological Informatics 24 (2014) 177–185

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Role of macrophytes as microhabitats for zooplankton community in lentic freshwater ecosystems of South Korea Jong-Yun Choi a, Kwang-Seuk Jeong a,b, Seong-Ki Kim a, Geung-Hwan La c, Kwang-Hyeon Chang d, Gea-Jae Joo a,⁎ a

Department of Biological Sciences, Pusan National University, Busan 609–735, South Korea Institute of Environmental Technology & Industry, Pusan National University, Busan 609–735, South Korea Department of Environmental Education, Sunchon National University, Suncheon, Jeonnam 540-742, South Korea d Department of Environmental Science and Engineering, Kyung-Hee University, Yongin, Gyeongii 445-701, South Korea b c

a r t i c l e

i n f o

Article history: Received 12 December 2013 Received in revised form 1 September 2014 Accepted 2 September 2014 Available online 16 September 2014 Keywords: Macrophytes Zooplankton community Self-organizing map (SOM) Lentic freshwater ecosystem Unsupervised learning

a b s t r a c t Zooplankton community distribution depends largely on the microhabitat characteristics of the water body. It has been reported that macrophytes provide microhabitats for zooplankton (e.g., space and food resources). To date, studies have focused on the overall influence of macrophytes on zooplankton (e.g., positive relationships with zooplankton diversity); however, the morphological characteristics of macrophytes have not been intensively studied. To fill this gap in knowledge, we investigated zooplankton abundance and diversity, macrophyte characteristics (types, dry weight, and species number), and physicochemical parameters (water temperature, dissolved oxygen, pH, conductivity, and chlorophyll a) by using the 1 × 1 m quadrat method. We surveyed 164 wetlands in South Korea during spring (May to June), prior to the summer monsoon. Patterning zooplankton distribution was accomplished using a Self-organizing map (SOM). We used 34 input variables (zooplankton genera) to train the model. The distribution of five plant habit parameters (no plant, emergent, free-floating, floating-leaved, and submerged) was investigated with a trained SOM plane, by environment data masking. Based on a U-matrix, three clusters were identified from the model. Zooplankton assemblages were positively related to macrophyte characteristics (i.e., dry weight, species number, and plant type). In particular, free-floating plants supported rotifers, such as Testudinella, and cladocerans, such as Alona, Chydorus, Diaphanosoma, and Ilyocryptus (mostly epiphytic). Submerged plants were associated with planktonic rotifers, such as Filinia, Ploesoma, Synchaeta, cladocerans, such as Daphnia, and copepods, such as Eucyclops and Macrocyclops. On the basis of these results, we suggest that the microhabitat structure, created by macrophytes, is an important factor in determining the diversity and abundance of zooplankton communities, because the different species compositions of macrophytes support diverse zooplankton genera in these habitats. The results indicate that macrophytes are the key components of lentic freshwater ecosystem heterogeneity, and the inclusion of diverse plant species in wetland construction or restoration schemes will result in ecologically healthy food webs. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Microhabitat characteristics are the determining factor of the fundamental structure of zooplankton assemblages in lentic freshwater systems. Ecosystems provide heterogeneous microhabitats with diverse structural complexity, based on a mosaic of different habitats (Chick and McIvor, 1994). The heterogeneity of microhabitats can be characterized not only by abiotic factors, such as water depth, wave action, turbulence, water temperature, and bottom substrates (Gasith and Gafny, 1990; ⁎ Corresponding author at: Department of Biological Sciences, Pusan National University, Jang-Jeon Dong, Geum-Jeong Gu, Busan 609–735, South Korea. Tel.: +82 51 510 2258; fax: +82 51 583 0172. E-mail address: [email protected] (G.-J. Joo).

http://dx.doi.org/10.1016/j.ecoinf.2014.09.002 1574-9541/© 2014 Elsevier B.V. All rights reserved.

Sebens, 1991), but also by biotic structures, such as trees, wood debris, and the composition and abundance of macrophytes (Angermeier and Karr, 1984; Benke and Wallace, 2003; Padial et al., 2009). This heterogeneity affects both environmental factors (e.g., physicochemical parameters, or nutrient state) and biological factors (e.g., plankton, micro/macroinvertebrates, and juvenile fish; Baattrup-Pedersen et al., 2003; Barko et al., 1986). Among the variety of microhabitat structures, macrophytes play an important role in the structuring of the ecosystem in freshwater shallow lakes and floodplains, and the space within macrophyte assemblages is known to provide favorable habitats for zooplankton communities (Keast, 1984; Manatunge et al., 2000). Several studies have characterized the large-scale spatial distribution of zooplankton (i.e., plant-attached species), and one of them focused on plant distributions. Macrophytes create patches of various

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sizes in the water body, thereby providing different microhabitats to zooplankton (Folt and Burns, 1999; Padial et al., 2009). Little evidence has been found on the role of the coexistence of different macrophytes in increasing microhabitat complexity. In particular, submerged macrophytes alter microhabitat complexity, as well as physical conditions, and consequently affect the abiotic and biotic characteristics of the system. Field observations and experimental investigations confirm the occurrence of high zooplankton densities in the presence of submerged macrophytes (e.g., Jeppesen et al., 1994; Schriver et al., 1995). However, Cazzanelli et al. (2008) suggested that free-floating plants were also important as microhabitats for epiphytic microinvertebrates in the water body, where submerged plants were scarce. The macrophytes' architecture has a significant bearing upon zooplankton food source, due to detritus tapping (Rooke, 1984), and the growth of periphytic microalgae (Dudley, 1988), consequently leading to distinct zooplankton communities (particularly epiphytic zooplankton species) on different types of macrophytes. The effects on phytoplankton may subsequently affect higher trophic levels, such as zooplankton and macroinvertebrates. Moreover, zooplankton improve light availability by grazing on phytoplankton (Timms and Moss, 1984), that may result in periods of high water clarity, allowing submerged macrophytes to establish (Hanson and Butler, 1994). However, few experimental studies have investigated macrophyte structure and its effects on the associated zooplankton communities (Kuczyńska-Kippen and Nagengast, 2006; Meerhoff et al., 2007; Padial et al., 2009). Moreover, previous studies have investigated only two or three plant species. This is partly due to the practical difficulty in measuring different structures of macrophyte species and comparing them across different sets of organisms. We assume that structurally complex habitats may provide more opportunities for zooplankton to exploit resources, and thus increase their species diversity. Studies on the influence of habitat heterogeneity caused by macrophytes on zooplankton distributions need to consider several sources of environmental data simultaneously; however, this process is often hindered by the complexity of data. When the target organisms are diversified (i.e., at genus or species level), it is almost impossible to use an experimental approach to ascertain the influence of habitat heterogeneity. This can be overcome by using data from surveys (see Li et al., 2012). Often the collected data may be too poor in quality and/or quantity to be used without any treatment, and the knowledge used to discover any relationships may be inaccurate, or more likely, may not use the most suitable abstractions (Shan et al., 2006). Fielding (1999) stated that it was often difficult to extract clear patterns from such data using conventional analyses; the recent evolution of soft computing techniques for ecological analysis may provide an opportunity to overcome this problem. Among the various algorithms extant, the Self-organizing map (SOM) is one of the most popular non-linear data ordination processes available; it extracts information from multi-dimensional data and maps it onto a reduced dimensional space (Kohonen, 1997). In recent ecological research, the SOM has been recognized as one of the most powerful and applicable methods available, because it is easy to visually interpret (Chon et al., 1996), compared to principal component and correspondence analyses (Giraudel and Lek, 2001). The aim of this study was to elucidate the relative importance of microhabitat heterogeneity caused by different plant types. To test this objective, we surveyed 164 lentic ecosystems in South Korea, and included physicochemical parameters of water, macrophytes, and zooplankton assemblages in the SOM model to identify any relationships of interest. We expected that a different structure of plant types would determine microhabitat structure in freshwater ecosystems, and strongly influenced the abundance of zooplankton and species composition of the community. Based on SOM results, we discussed ecological relevance associated with the interaction between aquatic macrophytes and zooplankton community.

2. Methods 2.1. Study sites South Korea is located in East Asia and has a temperate climate. Four distinct seasons lead to the dynamic succession of biological communities in the freshwater ecosystems of Korea. Annual mean rainfall is ca. 1150 mm, and more than 60% of annual rainfall occurs from June to early September (Choi et al., 2011; Jeong et al., 2007). The lentic freshwater ecosystems included in this study are located in the southeast of South Korea, around the middle and lower reaches of the Nakdong River. Historically, there were numerous riverine wetlands in the river basin (Son and Jeon, 2002), however, large areas of wetland have vanished, due to the expansion of human society (Burkett and Kusler, 2000). Agricultural land dominates areas surrounding the remaining wetlands in the river basin, and nonpoint source (e.g. nutrient) continuously influences the wetland ecosystems. We investigated 164 lentic systems (wetlands and reservoirs; see Fig. 1). The wetlands are dominated by various plant types, such as emergent, free-floating, floating-leaved, and submerged plants; however, the development and growth of macrophytes is inhibited in the reservoirs due to their impermeable floors. In addition, some wetlands support only a few plant species because of high water levels and low nutrient concentrations. Therefore, the study sites encompassed a wide range of environmental characteristics in terms of microhabitats (i.e., different types of lentic systems and different patterns of their constituent plant communities). 2.2. Monitoring strategy We monitored the study sites from May to June, before the summer monsoons and typhoons. This was to avoid flooding disturbance (Park et al., 2002), and to obtain data under stable conditions. We established three to five sampling locations in littoral areas at each site. At each sampling point, three quadrats (1 m × 1 m) were used to measure physicochemical parameters, zooplankton abundance, and the presence of macrophytes. Water temperature, dissolved oxygen, conductivity, pH, turbidity and Chlorophyll a were measured at each site. Water samples were collected at a depth of 0.5 m. We used a DO meter (YSI DO meter; Model 58) to measure water temperature and dissolved oxygen; conductivity was measured using a conductivity meter (Fisher Conductivity Meter; model 152). The chlorophyll a concentration and turbidity were measured in the laboratory. Turbidity was measured using a turbidimeter (Model 100B). The water samples were filtered through a Mixed Cellulose Ester (MCE) membrane filter (Advantech; Model No., A045A047A; pore size, 0.45 μm), and chlorophyll a concentration was detected based on Wetzel and Likens (2000). We took an additional 10 L of water for zooplankton collection from the surface layer (to a depth of 0.5 m), using a 10 L column sampler. This water was filtered through a plankton net (68-μm mesh size), and the filtrate was preserved in formaldehyde (final concentration: ca. 5%). The zooplankton were identified and counted using a microscope (ZEISS, Model Axioskop 40; ×200 magnification), based on the classification key by Mizuno and Takahashi (1999). We identified all species of macrophytes within each quadrat. After species identification, different parts of the plants were taken in order to estimate dry weight; only the submerged parts of the plant were used for the dry weight measurement. For emergent plants, stalks above the water surface were removed. The entire mass of freefloating, or submerged plants, was used for dry weight (gram dry weight, gdw) estimation, but if free-floating plants had above-water organs, such as flowers, they were also removed. This sampling strategy was also applied to floating-leaved species. The collected macrophyte samples were dried at 60 °C for 48 h, and weighed using an electronic microbalance (Mettler, AE 240, Switzerland). The plant species were

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divided into four types: emergent, free-floating, floating-leaved, and submerged. 2.3. Self-organizing map The Self-organizing map (SOM) stems from the Kohonen network (Kohonen, 1982, 1997), which is an unsupervised learning algorithm (an artificial neural network). This network mimics the intellectual functioning of higher animal brains. The SOM is widely used as a tool for mapping high-dimensional data into a two-dimensional representational space (Kohonen, 1982). This mapping effectively retains the relationship between the inputted data, thus describing a topologypreserving representation of input similarities, in terms of distances in the out space (Fig. 2). It is therefore possible to visually identify clusters on the map. The main advantage of such mapping is the ease with which a user can interpret the structure of the data. The possibility of using such neural networks in ecosystem simulations was first suggested by Odum (1994), based on the hypothesis that it may be useful in understanding life systems, including many aspects of ecology (Chon et al., 1996). The SOM network is a competitive system in which the neurons (i.e., sample units) in Euclidean map space compete with one another, converting non-linear relationships into simple geometric relationships. This algorithm is effective in clustering and visualizing essential features of complex data, and has a unique structure which allows multivariate data to be projected non-

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linearly onto a rectangular grid layout with a rectangular or hexagonal lattice (Fig. 3). In this study, the zooplankton groups identified were used as input variables in the SOM. Zooplankton species that accounted for more than 5% of the total zooplankton abundance were included. During the training process, the number of nodes that the SOM plane consisted of was determined as being adjacent to 5 × n (n indicates the number of samples, i.e., number of sites in this study; Vesanto and Alhoniemi, 2000). From a variety of map structures of different sizes, we selected the optimal structure based on the minimal values for quantization (QE) and topographic errors (TE) (Cèrèghino and Park, 2009; Uriarte and Martín, 2005). After selection of the optimal SOM structure, each parameter was projected onto the two-dimensional SOM plane with a gray scale gradient; they were then clustered according to the calculated U-matrix. Gradient range was determined using the mean abundance of zooplankton. We used MATLAB 6.1 (MathWorks, Inc, Natick, MA, USA) and the SOM Toolbox (Helsinki University of Technology, Helsinki) for the development of the SOM model. The trained SOM plane using zooplankton data was masked using the physicochemical characteristics of water, and macrophyte characteristics. The distribution pattern of zooplankton data on the map was compared with the masked environmental data, and the influence of different plant types (emergent, free-floating, floating-leaved, and submerged) was investigated.

Fig. 1. Map of the study sites. The study sites are indicated as solid circles (●), and located in the southeast South Korea. The small map in the upper right-hand corner shows the Korean Peninsula.

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Fig. 2. Basic structure of the Self-organizing map.

3. Results 3.1. Biological factors and regression analysis There was relatively little difference in the physicochemical characteristics of water among the study sites (Table 1). Although there were some study sites that had exceptionally high or low values, the coefficients of variation (CV; standard deviation/mean × 100%) were lower than 100%. Conductivity had the highest CV, but the variation was only approximately 68%. There were differences in both macrophyte species composition and dry weight between study sites. Phragmites australis dominated most of the study sites; a total of 11 species of macrophytes were found (Phragmites communis, Picris hieracioides, Zizania latifolia, Scirpus tabernaemontani, Spirodela polyrhiza, Salvinia natans, Hydrocharis dubia, Trapa japonica, Nymphoides indica, Potamogeton

franchetii, Hydrilla verticillata, Ceratophyllum demersum, Vallisneria natans, and Potamogeton crispus). A total of 151 species of zooplankton were identified (101 rotifers, 39 cladocerans, and 11 copepods; see Table S1). The highest abundance of zooplankton recorded was 4497 ind. L−1, followed by 4113 ind. L−1. Monostyla, Brachionus, and Lecane were recorded frequently.

3.2. Classification of variable features by SOM The SOM model was adaptively fitted to the input data (Quantization error = 0.193; Topographic error = 0.01) onto a 164 hexagonal cellular plane (10 × 6 matrix; Fig. 3). For ease of interpretation, the 34 groups of zooplankton were displayed separately, and the cluster matrix was compared to each component plane; possible characteristics were imposed on each cluster.

Fig. 3. Clustering through data learning by the Self-organizing map. (a) U-matrix, (b) clustering result, (c) hierarchical dendrogram.

J.-Y. Choi et al. / Ecological Informatics 24 (2014) 177–185 Table 1 Mean macrophyte dry weights and physicochemical parameters measured at the study sites. APdryW, Macrophyte dry weight; SD, standard deviation; CV, coefficient of variation. Variable

Units

Max

Min

Mean ± SD

CV (%)

APdryW Water temperature DO saturation Conductivity pH Chlorophyll a

gdw °C % μs cm−1 – μg L−1

114.7 31.1 217.2 746.0 10.8 43.5

0 17.2 8.5 111.6 6.3 1.2

50.4 24.1 88.6 183.4 7.9 26.5

49.8 10.6 37.4 68.4 9.6 33.8

± ± ± ± ± ±

25.1 2.6 33.9 125.4 0.8 8.9

The U-matrix identified three distinctive clusters, based on the gray color gradient of Euclidian distance (Fig. 3a). Using the clustering map (Fig. 3b) and dendrogram (Fig. 3c), features of all the variables were distinguished between the upper (cluster 1) and the lower parts (clusters 2 and 3) of the map. The characteristics of each cluster extracted from the SOM are displayed in Table S1, which presents mean values of each variable. Each zooplankton group exhibited different shapes and gradients on the map plane, but most zooplankton groups were distributed in the lower parts of the map (i.e., clusters 2 and 3; Fig. 4). Some zooplankton species were frequently observed only in cluster 3. For example, a large proportion of epiphytic species, such as Colurella, Lecane, Lepadella, Monostlyla, Mytilina, Testudinella, Alona, Chydorus, Ilyocryptus, and

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Pleuroxus, were concentrated in cluster 3. Planktonic rotifer species such as Brachionus, Keratella, Polyarthra, and Nauplii were distributed sporadically on the map. Particularly Keratella and Polyarthra were concentrated on the right-hand side of the map. After environmental data masking was applied, the physicochemical parameters did not show any apparent relationship with the distribution of zooplankton. Water temperature, conductivity, and pH did not exhibit gradients on the map. However, dissolved oxygen, chlorophyll a, and turbidity had a directional distribution. In contrast to the physicochemical parameters, macrophyte types were clearly separated on the map plane. When we masked the macrophyte presence or absence data over the trained SOM plane, a distinguishable distribution pattern of plant types was observed. The study sites without macrophytes (indicated as ‘no plant’), or with only emergent plants, belonged in cluster 1, whereas the sites with free-floating and submerged plants belonged in clusters 2 and 3. Floating-leaved plants appeared over a wide range of the map. 3.3. Zooplankton community and environmental parameters Almost all zooplankton groups were associated with macrophytes. The sites with higher dry weights of macrophytes were aggregated in cluster 3 (Fig. 5), as were the zooplankton groups (Figs. 4 and 5). Overlapping the zooplankton maps onto the macrophyte maps (i.e., Fig. 4)

Fig. 4. Component map of 34 zooplankton groups. Each band shows the individual number, transformed by natural logarithm.

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Fig. 5. Clear segmentation of the data between species number of plant type and physicochemical parameters. The distribution is partitioned according to macrophyte type. APdryW, Macrophyte dry weight.

revealed that the abundance of epiphytic zooplankton groups tended to be greater when in association with free-floating or submerged plants. Some planktonic rotifer species (Platyias, Ploesoma, and Synchaeta) were also frequently observed in clusters 2 and 3. In addition, copepoda groups (Cyclops, Eucyclops, and Macrocyclops) were only abundant in cluster 3. Auraeopsis, Filinia, Lecane, Platyias, Ploesoma, Synchaeta, Daphnia, and Moina were abundant, and were associated with many submerged plant species; this is shown on the right-hand side of the map. In contrast, Testudinella, Trichotria, Alona, Chydorus, Diaphanosoma, Pleuroxus, Eucyclops, and Macrocyclops were associated with freefloating plants, on the left-hand side of the map. Colurella, Lepadella, Monostyla, Mytilina, and Platyias were more associated with the dry weight, rather than the species number, of free-floating or submerged plants. As a result, rotifers (mainly planktonic) preferred submerged plants, but small cladocerans (mainly epiphytic) were associated with free-floating plants. 3.4. Influence of macrophyte type The abundance and species number of each zooplankton group was influenced by a combination of macrophytes, based on the SOM clustering results (Fig. 6). The greater the number of macrophyte types present, the higher the zooplankton abundance and species number. The study sites dominated by ‘no plants’ or ‘emergent plants’ had low zooplankton abundance and species number. In contrast, the sites with two or more macrophyte species supported higher zooplankton abundance and species number. The study sites containing three different plant types (free-floating, floating-leaved, and submerged) had the largest zooplankton assemblages. In particular, a higher abundance of

zooplankton was supported by submerged plants in mixed vegetation areas compared to combinations of other plant types. 4. Discussion 4.1. Characterization of clusters A significant influence of microhabitat (by macrophyte) on zooplankton assemblage has been suggested in previous studies, but those studies were limited in their resolution of zooplankton groups (i.e., total zooplankton, or only three groups: rotifers, cladocerans, and copepods). The present study clustered the field-surveyed data relatively well by using a SOM model, and the results suggest a relationship between microhabitat characteristics and zooplankton genus. The characteristics of the clusters were as follows: • Cluster 1: the sites with emergent plants, some floating-leaved plants, or without macrophytes (indicated as ‘absence’), resulting in a very low dry weight of macrophytes, and a very low abundance of zooplankton groups. The highest dissolved oxygen and chlorophyll a was observed in this cluster. • Cluster 2: no specific pattern. We suggest that this is a transitional category, because most variables were intermediate in both numerical scale and topological position on the map. The abundance of zooplankton was the second greatest in this cluster. In particular, Anuraeopsis and Nauplii were dominant. • Cluster 3: the sites belonging to this cluster were dominated by freefloating and submerged plants. The abundance of zooplankton was highest among the clusters. Dissolved oxygen and chlorophyll a were lower than those in the other clusters. Based on the above points, we suggest that both macrophyte biomass and type affect the abundance of zooplankton. In particular, free-floating and submerged plants are important in sustaining high abundances of zooplankton. 4.2. Macrophyte type and zooplankton

Fig. 6. Zooplankton abundance and species number between habitat types by macrophyte. A, no plant; B, emergent plant; C, floating and floating-leaved plant; D, floating and submerged plant; E, floating, floating-leaved, and submerged plant.

The study sites with diverse macrophyte types supported a high abundance and species diversity of zooplankton. In freshwater ecosystems, macrophytes are known to provide suitable microhabitats for zooplankton (Kuczyńska-Kippen, 2007; Sagrario et al., 2009). Moreover, their high biomass provides complexity to the habitat structure. We found that the greater the macrophyte dry weight, the more abundant the zooplankton community. This was particularly true for epiphytic zooplankton species. Epiphytic species require substrates such as underwater stems or leaf surfaces for attachment (Phiri et al., 2011), and have an advantage over planktonic species owing to their small size and low activity, which make them less likely to be detected by

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predators. This explains the high abundance of epiphytic zooplankton where large macrophyte biomass was found (i.e., cluster 3). In contrast, planktonic species are less competitive, because they can be easily detected by predators due to their continuous movement, particularly the hopping motion exhibited by cladoceran species (Jeppesen et al., 2004; Williams and Moss, 2003). Furthermore, high plant abundance can interfere with the swimming and feeding behavior of planktonic zooplankton (Manatunge et al., 2000). Therefore, vegetated areas are dominated by epiphytic zooplankton. Not only presence or absence of macrophytes (represented as dry weight) but also the composition and type of macrophytes were crucial to zooplankton distribution. We found four types of macrophyte (emergent, free-floating, floating-leaved, and submerged) at the study sites, and each macrophyte group had a different pattern of space occupation in the water, which is an important factor in determining microhabitat structure. For example, only the stem of emergent plants is submerged, and this has a simple structure. Also, submerged parts (mainly stem) of floating-leaved plants have a similar structure to emergent plants. In contrast, free-floating or submerged plants usually have a mixture of stem and leaves below the water surface; hence, they provide a relatively complex substrate, as well as pore-like spaces, that allow zooplankton to inhabit. Submerged plants provide a more complex habitat in the water, and may support a high abundance of zooplankton (see Fig. 6; Blindow et al., 2000; Schriver et al., 1995). We found the most complex zooplankton assemblages in mixtures of submerged plants than in mixtures of other plants (e.g., free-floating or floating-leaved plants), or in single plant types (e.g., emergent plants). Based on these results, we suggest that macrophyte morphology influences the species composition of zooplankton, and the greater surface area and space provided by a mixture of macrophytes enable zooplankton to make use of them as microhabitats. Complex microhabitats contain more niches, and diverse ways of exploiting the available resources, which consequently increase species diversity (Bazzaz, 1975). In this study, we found large abundance of epiphytic zooplankton species such as Lecane, Monostyla, and Trichocerca in the study ecosystems (see Table S1). Previous studies also reported that these epiphytic species often were dominant in freshwater ecosystems where macrophytes were abundant (Choi et al., 2014a; Gyllström et al., 2005). Pelagic species such as Brachionus and Polyarthra also utilized macrophytes as refuge, avoiding fish predation, and we observed a similar distribution pattern of the pelagic species in our results. In SOM results, the cluster 3 collected data samples with large abundance of zooplankton, and this cluster was clearly associated with free-floating- and submerged plants. Based on zooplankton genus-wise comparisons with environmental data masking, free-floating plants mainly supported rotifers, such as Testudinella, and cladocerans, such as Alona, Chydorus, Diaphanosoma, and Ilyocryptus. However, submerged plants were associated with high abundances of rotifers, such as Filinia, Ploesoma, and Synchaeta, cladocerans, such as Daphnia, and copepods, such as Eucyclops and Macrocyclops. However, Ceridaphnia and Cyclops were abundant in both free-floating and submerged plants. This difference can be explained by food quality and habitat suitability. The zooplankton groups found in free-floating plants were strongly epiphytic. In general, submerged plants are more easily agitated by wind and water currents than are free-floating plants (Vermaat et al., 2000), so they are less suitable for the attachment of epiphytic zooplankton. Although previous studies have reported that epiphytic zooplankton often inhabit stands of floating-leaved plants with high biomasses (Moss et al., 1998), the floating-leaved plants in our study did not support a high abundance of zooplankton, because of their simple structure in early succession. In contrast, free-floating plants often hold particles that could be used as a food source for zooplankton, and the refuge space provided by this type of plant is insufficient for large zooplankton (Meerhoff et al., 2003; Van Donk and Van de Bund, 2002). These observations may explain the positive relationship found in this study between epiphytic zooplankton

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and free-floating plant distributions (see Fig. 4 and Table S1). Planktonic zooplankton moves continuously; hence, smaller spaces will hinder their movement. In addition, the ability to swim into deeper water (Schmid-Araya, 1998) allows the zooplankton to expand their habitat to submerged plants. Therefore, submerged plants that provide relatively larger refuge space can be utilized by planktonic zooplankton groups, as found in the current study. However, some rotifers, such as Colurella, Lepadella, Monostyla, Mytilina, Platyias, and Thrichocerca, were more associated with plant dry weight than with the morphological characteristics of the plant. According to Sakuma et al. (2002), these species are able to attach to substrates strongly, and substrate vulnerability (e.g., agitation) would not be a problem. In contrast, a larger biomass of macrophytes would attract zooplankton by providing more food sources, such as microalgae and organic matter attached to the plant. 4.3. Influence of physicochemical parameters on zooplankton distribution Water physicochemistry was not significantly related to the distribution of zooplankton. Even though the CV of each water environmental parameter was less than 100%, different water physicochemistry was recorded among the study sites. However, zooplankton distribution did not vary in accordance with the water physicochemistry. From the masking results, water temperature, conductivity, and pH were evenly distributed on the trained SOM plane, which may indicate that these parameters did not have any relationship with well-clustered zooplankton patterns. However, relatively high values of dissolved oxygen, chlorophyll a concentrations, and turbidity were found in cluster 3. This can be explained as competition between macrophytes and suspended phytoplankton. Phytoplankton compete with free-floating plants in surface area, especially for nutrients (Van Donk and Van de Bund, 2002), and numerous studies have reported the absence, or a lower density, of phytoplankton where macrophytes are abundant (shading effect, see a Sand-Jensen and Søndergaard, 1981). In areas where macrophyte biomass was large, a relatively higher turbidity, could be due to particulate organic matter or suspended solids derived from macrophytes (Lopez and Garcia, 1998). Therefore, decreased chlorophyll a concentrations and related environmental variation are not recognized as a forcing function for zooplankton distributions, but as an aftermath of excessive macrophyte growth. Water physicochemistry was not a significant factor in affecting zooplankton distributions, at least during the zooplankton growing season. Microhabitat structural complexity is more crucial than water characteristics for the diversity of zooplankton in freshwater lentic ecosystems. The current study is a ‘snapshot’ approach to elucidate the role of macrophytes as zooplankton microhabitats. Certainly, water physicochemical parameters can affect zooplankton seasonally. Previous studies have suggested a clear relationship between physicochemical parameters and zooplankton communities over time (Dejen et al., 2004; Garcia et al., 2002). In particular, water temperature and turbidity have significant effects on the population growth of zooplankton. As well as the water environment and zooplankton, macrophytes also have seasonality. In temperate regions, environmental parameters in the water body change with the season (Charkhabi and Sakizadeh, 2006; Jenkerson and Hickman, 1983). Moreover, some environmental factors have different values, depending on the microhabitat. Therefore, the seasonal variation of zooplankton-microhabitat patterns should be addressed in future studies. 4.4. Wetland management strategies to increase animal biodiversity Our study investigated the relationship between zooplankton communities and the role of macrophytes as microhabitats, using data from various water bodies having the same climatic conditions, to create a “snapshot” survey. This survey collected basic information on the protection and restoration values of macrophytes in relation to

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maintaining high zooplankton biodiversity and abundance. In this study, the diversity and abundance of zooplankton was closely related to macrophyte characteristics; this relationship is commonly found in other animals (Tews et al., 2004). The convergence of animal species number implies that it is necessary to discover optimal macrophytes species diversity to satisfy both aspects of esthetic and ecological function in wetlands restoration or management. Therefore, we suggested the introduction of proper macrophyte species for restoring or creating wetlands, in order to not only increase biodiversity in the wetland, but also to sustain an ecologically healthy food web. Based on our results, in particular, we recommend the introduction of free-floating or submerged plant species. Moreover, contemporaneous coexistence of free-floating or submerged plant can supported more abundance and species diversity of zooplankton (Choi et al., 2014b). Emergent plants are popularly used in wetland restoration process, due to their relatively strong viability and easy-to-manage characteristics (Ailstock et al., 2001). Introducing other macrophyte types will bring out diversified plant species in wetlands, and is expected to reinforce diversity as well as food web structure. 4.5. Conclusion Zooplankton community groups were successfully visualized onto the SOM, and demonstrated clear patterns of assemblage in relation to macrophytes. The SOM provided a new prototype of visualization for ecological data analysis, with discrete grouping of the complex dataset as a non-linear generalization. A total of 34 zooplankton community groups were clustered into three categories. The SOM result indicated clear zooplankton distribution patterns partitioned by the plant type (no plants, emergent, free-floating, floating-leaved, and submerged plants). In particular, epiphytic zooplankton groups exhibited a clear relationship with free-floating, floating and submerged plants. Because free-floating, floating or submerged plants have more complex structures than other plant types, they can support a high abundance of zooplankton. Consequently, we suggest that microhabitat structure by macrophytes is a crucial factor affecting zooplankton assemblages in lentic ecosystems, rather than water quality parameters. In particular, the species composition of macrophytes is an important factor in maintaining the diversity of organisms that live and feed on plants. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ecoinf.2014.09.002. Acknowledgments This research was fully supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, South Korea (grant number: NRF-2010-0024507; http://www.nrf.re.kr). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. References Ailstock, M.S., Norman, C.M., Bushmann, P.J., 2001. Common reed Phragmites australis: control and effects upon biodiversity in freshwater nontidal wetlands. Restor. Ecol. 9, 49–59. Angermeier, P.L., Karr, J.R., 1984. Relationships between woody debris and fish habitat in a small warm water stream. Trans. Am. Fish. Soc. 113, 716–726. Baattrup-Pedersen, A., Larsen, S.E., Riis, T., 2003. Composition and richness of macrophyte communities in small Danish streams-influence of environmental factors and weed sutting. Hydrobiologia 495, 171–179. Barko, J.W., Adams, M.S., Clesceri, N.S., 1986. Environmental factors and their consideration in the management of submersed aquatic vegetation: a review. J. Aquat. Plant Manag. 24, 1–10. Bazzaz, F.A., 1975. Plant species diversity in old field successional ecosystems in Southern Illinois. Ecology 56, 485–488. Benke, A.C., Wallace, J.B., 2003. Influence of wood on invertebrate communities in streams and rivers. In: Gregory, S.V., Boyer, K.L., Gurnell, A.M. (Eds.), The ecology and management of wood in world rivers. American Fisheries Society Symposium, Bethesda, Maryland, pp. 149–177.Beyst, B., Buysse, D., Dewicke, A., Mees, J., 2001. Surf zone

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