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Urbanization increases biotic homogenization of zooplankton communities in tropical reservoirs
Ecological Indicators 110 (2020) 105899
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Original Articles
Urbanization increases biotic homogenization of zooplankton communities in tropical reservoirs Ping Liu, Shaolin Xu, Jianhao Lin, Huiming Li, Qiuqi Lin, Bo-Ping Han
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Department of Ecology, Jinan University, Guangzhou 510632, People’s Republic of China
A R T I C LE I N FO
A B S T R A C T
Keywords: Urbanization Zooplankton Homogenization Spatial structure Seasonal dynamics Reservoir
Under rapid urbanization, many reservoirs built in rural areas are being embraced into by nearby expanding cities. Anthropogenic activities in urban areas can strongly affect aquatic biodiversity by modifying aquatic landscape and reduce habitat heterogeneity. As one of the key components in reservoir ecosystems, zooplankton is sensitive to such habitat changes. Within the metacommunity framework, we predict that in an urbanized landscape: (1) species sorting becomes the most important process in determining the variation of zooplankton communities; (2) urbanization will result in low beta-diversity and biotic homogenization of zooplankton communities and such homogenization will not significantly vary across time. To test these predictions, we investigated twenty five permanent reservoirs over three hydrological seasons in a well-developed and rapidly expanding city in tropical China. The reservoirs were grouped into two location classes and three storage size classes. Our results suggested that the structure of zooplankton communities were strongly homogenized in the studied urban reservoirs as being reflected in the low beta-diversity between the two location classes. However, high beta-diversity was found within each storage size class, mainly derived from species turnover (βsim). The high beta-diversity indicated that the variation of zooplankton community is a consequence of environmental heterogeneity within each storage size class. Variation partitioning revealed a weak spatial structure for zooplankton communities at the large scale, while only Chla as an indicator of food supply significantly explained high community variation among reservoirs in the dry season, but not in wet and transition seasons despite apparent species seasonal succession.
1. Introduction Metacommunity ecology integrates regional and local dynamics through the study of discrete local communities connected by dispersal of interacting species assemblages over landscapes (Leibold et al., 2004, 2010). Four major paradigms of metacommunity have been proposed to explain regional factors and processes influencing community dynamics and to incorporate spatial scales and landscape configuration into community structure. The relative importance of regional (dispersal, landscape and species pool) and local (abiotic and biotic) factors is the key to understanding the mechanisms of community assembly (Stoch et al., 2016; Viana et al., 2016). Dispersal limitation and environmental filtering are considered to be two major processes structuring aquatic communities, and species usually have different potentials for dispersal (Rahel, 2002; Beisner et al., 2006; De Bie et al., 2012). The extent to which community composition is shaped by dispersal over increasing spatial scales depends on the dispersal capacity of composing species (Juračka et al., 2016).
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Zooplankton is a central component of aquatic food webs and their diversity thus warrant further characterization (Dumont and Negrea, 2002; Mimouni et al., 2015; Xiong et al., 2017). Most zooplankton have a strong dispersal potential, and disperse passively with their resting eggs using multiple vectors such as water flow, birds, fish and winds. Overland and watercourse dispersal pathways are equally important to zooplankton, as they allow the organisms to track environmental changes (Declerck et al., 2011; Verbeek et al., 2018). At the intermediate regional scale where dispersal is not spatially limited, most species are able to reach suitable sites and local environmental factors would be vital in shaping community composition as predicted by the species sorting metacommunity model (Heino et al., 2015). In the tropics where water bodies are more or less connected either temporarily or permanently, fish are efficient and often highly selective predators with the capacity to shape species composition and size distribution of zooplankton communities (Lemmens et al., 2015). Many studies have focused on the pattern of regional diversity (i.e. beta-diversity) and on the variation of zooplankton community through
Corresponding author. E-mail address: [email protected] (B.-P. Han).
https://doi.org/10.1016/j.ecolind.2019.105899 Received 23 May 2019; Received in revised form 29 October 2019; Accepted 4 November 2019 1470-160X/ © 2019 Published by Elsevier Ltd.
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with a river network, while the east is more or less rural. Most of the reservoirs are eutrophic due to extensive land use and fish culture (Lei et al., 2014). Variation of zooplankton species composition between the reservoirs in the downtown and rural area is expected to be small due to the rapid urbanization. In the present study, we investigated the spatial variation and seasonal dynamics of reservoir zooplankton over an urban landscape at local and regional scales. The aim is to detect the potential effects of urbanization on zooplankton community. In the framework of metacommunity, we predict in an urbanized landscape that: (1) where dispersal becomes less limited, species sorting (or environmental filtering) becomes the most important process in determining the spatial structure of zooplankton communities; (2) urbanization results in low betadiversity and high biotic homogenization of zooplankton communities; and (3) such homogenization will not significantly vary with zooplankton seasonal dynamics.
time and space in natural ponds and lakes (La Store et al., 2014; Azevêdo et al., 2015; MacLeod et al., 2018; Wærvågen and Andersen, 2018). However, few studies have investigated the zooplankton diversity of water bodies in urban landscapes (Pinel-Alloul and Mimouni, 2013; Mimouni et al., 2015, Suski et al., 2018). Urban landscapes are highly modified and managed, and suffer from human-accelerated habitat changes (McDonald et al., 2008; McDonnell and Hahs, 2013; Knop, 2016; Rocha and Fellowes, 2018). Rapid urban expansion can intensively change the distribution, abundance and composition dynamics of organisms (Suski et al., 2018; Hanashiro et al., 2019). Human activities may also enhance the connectivity of water bodies scattered in urban landscapes (Grimmond, 2007; Silver et al., 2018). Consequently, urbanization may lead to a loss of diversity (Magura et al., 2010) and biotic homogenization (i.e. composition similarity), thereby causing low beta-diversity for regional aquatic communities (Pulliam, 1988; Holt et al., 2003; McKinney, 2006; Hill et al., 2018). The classic urban–rural gradient concept assumes a decline in land-use intensity from an intensively developed urban core to residential suburbs, culminating in lightly developed rural areas (McDonnell and Pickett, 1990; McDonnell and Hahs, 2008; Trentanovi et al., 2013). With higher biotic homogenization from urbanization activities, the reduced gradient no longer necessarily indicates shifts in habitat quality (Okuda et al., 2017; Warren et al., 2018). Reservoirs built for flood control and irrigation during the 1950s are abundant in tropical China where natural lakes are rare (Han and Liu, 2012). Reservoirs were initially constructed far from towns and cities to avoid flooding risk. However, urban expansion is creating more reservoirs to be located into urbanized areas. Understanding the mechanisms that shape species distribution under urbanization is especially challenging in aquatic ecosystem management, because temporal changes of local ecological conditions and habitat connectivity are associated with strong human activity (Han and Dumont, 2011). Reservoirs are mainly composed of pelagic zone, which is a much stable habitat with fewer zooplankton species than the littoral zone, and its ecological function is more sensitive to loss of species diversity. Pelagic zooplankton richness and species composition have therefore significant implications for the management of reservoir ecosystems (Walseng et al., 2006; Pinel-Alloul and Mimouni, 2013; dos Santos et al., 2018). Reservoirs zooplankton assemblages in an urban landscape at the regional scale constitute a metacommunity. Spatial structure of the zooplankton metacommunity depends on landscape and environmental factors such as spatial, hydrological and climatic variables (Cottenie et al., 2003; Cottenie and De Meester, 2004; Juračka et al., 2016; Zhao et al., 2018). In the tropics where water bodies, especially reservoirs, are more or less connected to each other temporarily or permanently, zooplankton can easily disperse with clonal individuals or resting eggs through the waterway from networks of rivers (Havel and Shurin, 2004). Zooplankton in such reservoirs are expected to be spatially more homogeneous in structure. Precipitation is a key environmental factor that imposes a strong influence on the seasonal dynamics of zooplankton metacommunity. In the wet season, connectivity between reservoirs tends to be stronger, facilitating passive dispersal of zooplankton and weakening environmental filtering (i.e. species sorting) (Shmida and Wilson, 1985). In the dry season, the connectivity becomes weaker, and environmental filtering increases over time (Bozelli et al., 2015; Chaparro et al., 2018). Reservoir storage size also affects richness and species composition of zooplankton at both local and regional scales. A small shallow reservoir becomes more turbid by suspending sediment and high phytoplankton biomass than a large deep reservoir (Gayosso-Morales et al., 2017; Stephan et al., 2017). Dongguan is one of the most important industrial cities in the Pearl River Delta where a large number of reservoirs were built mainly for drinking water supply and irrigation. Many of these reservoirs were built in the 1980s, thereafter the city has been expanding rapidly. The city has a distinct spatial character, with the center located to the west
2. Material and methods 2.1. Study area Dongguan is located within the Pearl River basin near the tropic of Cancer. It has an average annual precipitation of 1802 mm, which includes a long wet season that runs from April to September, which accounts for about 40% of the annual precipitation (Zheng et al., 2017). The average annual temperature is 23.3 °C. Precipitation is known to be the key abiotic factor affecting the connectivity of water bodies and regional diversity of zooplankton community structure (Primo et al., 2015). We investigated 25 permanent reservoirs covering a total area of 30 km (north to south) × 40 km (west to east) (Fig. 1). Physical coordinate ranges from 22.6752°N to 23.0631°N latitude and 113.6853°E to 114.2101°E longitude. Altitude ranges from 2 m to 105 m. The larger reservoirs are primarily used for drinking water supply and each of them is relatively well protected and managed by an organized team, while the smaller reservoirs were mainly used for irrigation and fishing, as well as for backup drinking water supply during the dry season. The reservoirs are physically scattered and isolated from each other, and the degree of connectivity depends on their spatial distance. 2.2. Sampling and laboratory work Sampling and field measurements for water quality, phytoplankton and spatial information of the 25 reservoirs were conducted in the middle of the wet season (July 2011), transition season (December 2011), and dry season (March 2012). A 5L sampler was used to collect 50L of subsurface water by collecting vertically along the water column in the pelagic area of each reservoir and filtering the sampling using a 38-µm mesh net. Samples were preserved using a final concentration of 4% formalin solution for further analysis. With the exception of copepodites and copepod nauplii, specimens were identified to the species level under a dissecting microscope according “Guides to the identification of the microinvertebrates of the continental waters of the world” (Dumont and Negrea, 2002). Zooplankton abundance was determined by counting at least 10% of the concentrated sample in a SedgewickRafter counting chamber, with a minimum of 200 counted individuals. Zooplankton biomass was estimated by the abundance data and size measurements of specimens following Dumont et al. (1975) and Zhang and Huang (1991). Zooplankton samples from two reservoirs in July and three reservoirs in December were lost during repacking and moving to the lab. Phytoplankton samples were preserved with Lugol’s solution and identified to the lowest possible taxonomic level. The samples were analyzed under an inverted microscope in 10-ml sedimentation chambers (Utermöhl, 1958) and specimens were identified at genus level because the morphological features of the organisms did not allow to certainly assign them to know “species”. At least 400 algal individuals 2
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Fig. 1. Twenty-five investigated reservoirs in the Pearl River basin located within an area of 30 km (north to south) × 40 km (west to east). Red star: the municipal center of Dongguan; light blue circles: small storage size reservoirs; green circles: medium storage size reservoirs; dark blue: large storage size reservoirs.
(Lin et al., 2001). The species occurrence dataset was used for species richness estimation. Eight estimators of species richness were used. Log transformation was used for all estimators so that the lower values of the resulting interval were at least equal to the number of observed species (Chao, 1987). The lowest ratio of variance/estimators was used as the best estimation (Dumont and Segers, 1996). Only the lowest variance/ estimator of the first order jackknife was used to estimate regional species richness (Palmer, 1990; Pinel-Alloul et al., 2013). True Simpson diversity index was used to determine the species diversity for each group (Jost, 2007). Principal component analysis (PCA) was performed on all quantitative environmental variables to explore the change from wet season to dry season. Sampling sites were scaled (scaling 1) in proportion to eigenvalues. We performed the nonmetric multidimensional scaling (nMDS) based on zooplankton’s abundance Bray-Curtis distance matrices to reveal the ordination relationship between species abundance and sampling seasons. A two-way permutational multivariate analysis of variance (PERMANOVA) was conducted to test for the overall significant differences in the zooplankton community structure among the three classes of reservoir storage size and between the three seasons. A separate PERMANOVA was performed for downtown and rural reservoirs to test the effect of urbanization on community structure of zooplankton in reservoirs. We also calculated Bray-Curtis distance among downtown reservoirs and among rural reservoirs, as well as the Bray-Curtis distance of any pair zooplankton communities between one downtown reservoirs and one rural reservoir, and tested the difference in these distances with unpaired t-test. Beta-diversity, the spatial variation of the species composition of sites, has two components: species turnover and nestedness (Baselga, 2010). These components are the result of two processes: species replacement and species loss (Baselga, 2010). The overall beta-diversity (ßsor) and its components, turnover (ßsim) and nestedness (ßsne), were estimated using Sorensen index adapted for multiple sites. Components of beta-diversity and species contribution to beta-diversity (SCBD) were calculated with species richness (Baselga, 2013; Dray et al., 2017). As phytoplankton could affect the structure of zooplankton community, we performed a Mantel test on the zooplankton and phytoplankton Bray-
were counted from each sample and the size of each algal cell was measured. Phytoplankton biomass was calculated using an estimated wet weight density of 1 g/cm3 (Hillebrand et al., 1999). 2.3. Spatial and environmental variables Geographical coordinates and altitude were recorded using a handheld GPS unit. Distance from the municipal center to each reservoir was calculated using Google Earth. Water chemistry data were measured using a portable analyzer (YSI: PRO-PLUS), including water temperature (T), pH, dissolved oxygen (DO), and conductivity (Cond). Reservoir storage and dead water level was obtained from the local hydrographic bureau. A Secchi disk was used to determine water transparency (Tran). Total nitrogen (TN) and total phosphorus (TP) were measured by the potassium persulfate oxidation method. Chlorophyll a (Chla) was analyzed using a simplified method by Lin et al. (2003). Nitrate (NO3−), nitrite (NO2−), and phosphate (PO43−) were all included as environmental variables. A Beacon Cylindrospermopsin Plate kit method was used to detect Microcystis toxins (MT). 2.4. Statistical analyses We used the distance of each reservoir to nearest town as a proxy to measure a general urbanization density imposed on the reservoirs. We grouped the investigated reservoirs in two ways: (1) two location classes of downtown and rural reservoirs based on the distance from the city center to each reservoir, i.e. downtown > 25 km and adjacent rural < 25 km; and (2) three size classes according to their storage size, namely large (> 10 × 106 m3, eight), medium (5 × 106 ~ 10 × 106 m3, nine), and small reservoirs (< 5 × 106 m3, eight). Euclidean distance matrices for all environmental variables were calculated to compare characteristics of the different classes of reservoirs. Eutrophication is a process of nutrient enrichment of anthropogenic or natural origin, and trophic state categories have been proposed for lakes, rivers and reservoirs (Cigagna et al., 2016). The trophic state index (TSI) was used to classify the trophic status of the investigated reservoirs. Total phosphorus (TP), total nitrate (TN), chlorophyll a (Chla) and secchi disk depth (SD) were used to determine the average TSI according to the empirical equations for reservoirs in the province 3
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reservoir size classes (R2 = 0.036, p = 0.046), and a small but significant difference between downtown and rural reservoirs (R2 = 0.025, p = 0.017) (Table 1). Except in wet season (p = 0.0161), the averaged Bray-Curtis distance among downtown reservoirs was smaller that the distance and among rural reservoirs (dry season: p = 0.0404; transition season: p = 0.145) (Fig. S1). This means that the zooplankton have more similar species composition among downtown reservoirs than among rural reservoirs. The mean Bray-Curtis distance of a pair zooplankton communities between one downtown reservoir and one rural reservoir was significantly greater than 0.5 (p < 0.0001), e.g., 0.666 in the transition season, 0.589 in the wet season and 0.589 in the dry season.
Curtis dissimilarity matrices of abundance and biomass data. Distance-based Moran's eigenvector maps (dbMEMs, Dray et al., 2006; Borcard et al., 2011) were used to explore the spatial structure of zooplankton in the landscape of reservoirs. This approach allows a spectral decomposition of the spatial structure by producing orthogonal eigenvectors that can be used to represent spatial relationships among reservoirs. The first few eigenvectors (MEM1) describe the large scale spatial structure. The last few eigenvectors (MEMn), with the lower eigenvalues, describe fine spatial structure (Griffith and Peres-Neto, 2006; Peres-Neto and Legendre, 2010). Forward selection was applied to dbMEMs spatial predictors to pick out significant Moran’s indices. Variation partitioning was carried out to reveal the effect of pure environment variables (E), of pure spatial variables (S), as well as of their joint effect (E + S) on community composition (Wang et al., 2012). Combined season datasets and single season datasets were used in the spatial analysis. All statistical analyses were performed using R 3.3.3 (R Core Team, 2013), and the packages being “spacemakeR v0.0-5’, “packfor v0.0-8”, “AEM v0.6”, “PCNM v2.1-2”, “adespatial v0.1-0”, “betapart v1.5.0”, “vegan v2.4-5”, “ade4 v1.7-10”, “spdep v0.7-4”, “ape v5.0”, “ellipse v0.4.1”, “MASS v7.3-50”.
3.3. Beta-diversity and its seasonal dynamics A high beta-diversity was detected across all the investigated reservoirs (βsor = 0.96), mainly originating from species turnover (βsim = 0.93), and only negligibly from nestedness (βsne = 0.03). No significantly seasonal difference in regional beta-diversity among reservoirs was found (βsor in wet: 0.893, in transition: 0.894, in dry: 0.889). Beta-diversity within each reservoir class was higher than that between the reservoir classes (Fig. 4). Species contributing to beta-diversity (SCBD) varied over the three seasons, showing apparent seasonal dynamics. Numbers of dominant species contributing to beta-diversity declined from wet season to dry season. In the wet season, two species of rotifer (Brachionus calyciflorus, Asplanchna priodonta), one species of cladocera (Bosminopsis deitersi), and one species of copepod apart from nauplius (Mesocyclops thermocyclopoides) significantly contributed to regional beta-diversity. No species in the transition season significantly contributed to regional beta-diversity, whereas dominant species in the dry season contributed evenly to regional beta-diversity. Species with a SCBD value larger than 0.02 numbered 19, 22 and 24 species for each of the three seasons (Fig. 5).
3. Results 3.1. Environmental condition and trophic status Measured environmental variables significantly varied for each reservoir classes in the three seasons. The smaller reservoirs had higher Chla and TP than the larger reservoirs did in all seasons (p < 0.05). Chla and Cond for downtown reservoirs were higher than rural reservoirs (p < 0.01). A higher TP was recorded in downtown reservoirs than in rural reservoirs in the transition season and dry season but not in the flooding season (p < 0.05) (Fig. 2). Large reservoirs were more homogenous with a relatively lower Euclidean distance compared to medium and small reservoirs, but no significant differences were detected between downtown reservoirs and rural reservoirs, although error bars of those indicators in the same type of reservoirs were so large, especially in dry season. The reservoirs were classified into either mesotrophic (30 < TSI < 50) and eutrophic (TSI > 50), while no oligotrophic reservoirs (TSI < 30) were detected. The trophic state was dynamic or fluctuating for many reservoirs; with three reservoirs in the wet season, five reservoirs in the transition season, and eight reservoirs in the dry season stayed in a mesotrophic state. Among all investigated twenty five reservoirs, only two reservoirs stayed at the mesotrophic state in all three seasons. The others were presumed to have undergone eutrophication.
3.4. Variation of zooplankton community and explaining variables Main environmental conditions for the downtown reservoirs (R1R12) were seasonally different from those of the rural reservoirs (R13R25) (see Appendix: Fig. S2). Zooplankton communities showed a clear succession from wet season to dry season (see Appendix: Fig. S2). Cladocerans and copepods were more likely to appear in the wet season, whereas most rotifers appeared in transition and dry seasons. nMDS analysis did not detect clear ordination differentiation between downtown and rural reservoirs. Mantel test showed significant correlations of both zooplankton abundance (p = 0.044) and zooplankton biomass (p = 0.022) with phytoplankton in the wet season, but only zooplankton abundance (p = 0.013) in the dry season (see Appendix: Table S1). Variation partitioning was conducted for both species presence-absence data and abundance data. Only the dry season zooplankton community in the presence-absence data was significant in the model test for spatial analysis. The first (MEM1) of the 8 positive MEM variables significantly explained the low spatial variation (3.7%), suggesting a broad-scale process affecting zooplankton occurrence in the dry season, corresponding to a low beta-diversity between downtown and rural reservoirs. However, environment variables (Chla) explained high part (10.8%) of the total variance in dry seasons, but not in the other two seasons.
3.2. Zooplankton richness and its seasonal dynamics In total, we identified 46 species of rotifers, 10 species of cladocerans, and 16 species of copepods from all the samples collected in the three seasons. The most common species in observed were: Polyarthra vulgaris (39), Brachionus calyciflorus (34), and Asplanchna priodonia (31) in rotifers; Bosmina sp. (21) and Bosminopsis deitersi (22) in cladocera; and Mesocyclops thermocyclopoides (27) in copepods. Species richness increased from the wet season to transition season and to dry season in each reservoir class. Simpson diversity increased for large and rural reservoirs in the transition season, and decreased in the dry season (Fig. 3). Most of the eight used estimators of species richness provided a higher predicted richness than the actual observation in estimating regional species richness. Species richness confidence interval in the investigated region ranged from 80 to 91 in these reservoirs. No significant difference was found between the three size classes or between the two location classes. PERMANOVA revealed significant differences in the structure of zooplankton community among the three seasons (R2 = 0.129, p = 0.001) and among three
4. Discussion Low beta-diversity between downtown and rural reservoirs regardless of seasonal dynamics suggests strong biotic homogenization under urbanization. The relatively higher beta-diversity within each reservoir class indicates that environmental filtering is the most 4
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Fig. 2. Characteristics of environmental variables for two groups of reservoir classes based on reservoir size and its distance to Dongguan City. LWS: large reservoirs in wet season, LTS: large reservoirs in transition season, LDS: large reservoirs in dry season. MWS: medium reservoirs in wet season, MTS: medium reservoirs in transition season, MDS: medium reservoirs in dry season. SWS small reservoirs in wet season, STS: small reservoirs in transition season, SDS: small reservoirs in dry season. UWS: downtown reservoirs in wet season, UTS: downtown reservoirs in transition season, UDS: downtown reservoirs in dry season. RWS: rural reservoirs in wet season, RTS: rural reservoirs in transition season, RDS: rural reservoirs in dry season.
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Fig. 3. Species richness and true Simpson diversity for two groups of reservoir classes. Hollow circles represent richness or diversity index in each reservoir, and triangles represent mean value in each class. The abbreviations of reservoirs are shown in Fig. 2.
abundance in reservoirs. Within some taxonomic groups, small-sized species are expected to dominate urban communities, as dispersal limitation increases with increasing body size in zooplankton (De Bie et al., 2012; Oertli and Parris, 2019). Small species were more diverse in more urbanized waterbodies in contrast to large-bodied species, which were more diverse in less urbanized systems (Gianuca et al., 2018). While eutrophication of reservoirs is beneficial for cosmopolitan species, oligotrophic species such as Daphnia galeata were absent from the present investigation. Regional species richness depends on biological taxa and spatial scale in investigation. Pelagic habitats of tropical reservoirs usually harbor fewer species under habitat homogenization. In our case, the total regional species richness was low in all three seasons, and rotifers contributed to gamma diversity far more than cladocerans and copepods. Urban water bodies are often eutrophic with frequent disturbance and strong predation pressure from high cultured fish density (Zhao et al., 2013, 2015). Thus, the zooplankton community tends to be composed of small species; for instance, the common rotifer species in our reservoirs are small sized and capable of escaping predators visually.
Table 1 Two-Way PERMANOVA for zooplankton community across three sampling seasons and three size classes and two distance classes. The three size classes are large (> 10 × 106 m3), medium (5 × 106 ~ 10 × 106 m3), and small reservoirs (< 5 × 106 m3). The two distance classes are downtown (< 25 km) and adjacent rural area (> 25 km). Df
Sums of Sqs
Mean Sqs
F.Model
R2
Pr (> F)
Season Storage Season × Size Residuals Total
2 2 4 61 69
3.686 1.018 1.664 22.263 28.630
1.843 0.509 0.416 0.365
5.050 1.395 1.140
0.129 0.036 0.058
0.001*** 0.046* 0.18
season distance season × distance Residuals Total
2 1 2 64 69
2.707 0.550 0.485 18.024 21.766
1.354 0.550 0.243 0.282
4.806 1.952 0.861
0.124 0.025 0.022
0.001*** 0.017* 0.718
Significance codes: * 0.05; ** 0.01; *** 0.001.
important process in determining the variation in zooplankton communities. The Chla data indicating food availability is important in structuring zooplankton communities, particularly in the dry season. Nevertheless, no significant difference in regional beta-diversity among reservoirs was detected between the three seasons investigated.
4.2. Beta-diversity and seasonal dynamics As expected, low beta-diversity was observed between the three reservoir size classes and between the two location classes, indicating that urbanization greatly homogenizes the environmental habitats, and a few “urban-adaptable” species may become increasingly widespread and locally abundant (McKinney, 2006). In the present study, spatial distribution pattern of reservoirs largely contributes to the low betadiversity among the reservoir size classes. Small and large reservoirs are scattered randomly, facilitating dispersal and migration of zooplankton between size classes at the local scale. However, beta-diversity within each reservoir size class was relatively higher than expected. The higher
4.1. Species richness and community composition Zooplankton communities in our investigated reservoirs composed mostly of common or tropical cosmopolitan species. Rotifers were dominated by small-bodied common species such as Polyarthra vulgaris, Brachionus calyciflorus, and Asplanchna priodonia. Small sized species such as Bosmina and Bosminopsis were dominant in Cladocera. Urbanization resulted a lower number of zooplankton species and lower 6
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Fig. 4. Spatial turnover and nestedness components of beta-diversity based on incidence distance matrices within three seasons and across each group. ßsne: value of the nestedness component, ßsim: value of the turnover component. WS: wet season, TS: transition season, DS: dry season. BWS: between distance classes in wet season, BTS: between distance classes in transition season, BDS: between distance classes in dry season. AWS: among three size classes in wet season, ATS: among three size classes in transition season, ADS: among three size classes in dry season.
communities and seasonal dynamics, and it was also found to be important in our investigated reservoirs. No significant environmental variables were detected to explain the variation of zooplankton communities in the wet season and the transition season, which was the same situation for the study of zooplankton in subtropical river systems (Zhao et al., 2017). The difference in environmental variables in the two seasons becomes much lower and insignificant because of strong disturbance by rainfall. Strong precipitation in the wet season may cause water overflow or flooding of reservoir, and may enhance the immigration and emigration of species between adjacent reservoirs (Kim et al., 2012). Stocking of bighead carp and silver carp is common in our investigated reservoirs, and an increase in fish density in the growth season could greatly affect the structure of zooplankton communities through predation, resulting in dramatic changes to their abundance and richness (Gardiner et al., 2014; Rocha and Fellowes, 2018). Fish predation could have a negative effect on zooplankton abundance and community structure (Zhang et al., 2013; Sato et al., 2018) and it accounts for the scarcity of largesized cladocerans during summer months in lakes in the subtropics (Havens et al., 2015). Our multivariate analysis explored bottom-up effect from phytoplankton represented by Chla, but no top-down effect from fish predation was explored because of a lack of fish data, despite its significance for regulating zooplankton composition.
beta-diversity within each reservoir size class implies the local communities are distinct and heterogeneous. Therefore, urban water bodies potentially provide a diverse range of habitats for a mixture of common and rare macro-invertebrate taxa and represent a valuable biodiversity resource within anthropogenically dominated landscapes (Hill et al., 2015, 2017). The compositional variation among local communities (beta-diversity) may reflect environmental heterogeneity, but may also reflect reduced exchange of organisms between habitat patches (Leibold and Mikkelson, 2002; Legendre, 2014). In the present study in Dongguan, dubbed the “world factory” for its booming manufacturing industry, the influence of human activities on reservoirs changes with distance from the reservoirs to the downtown center and with reservoir size. Distinct eutrophication of reservoirs and alteration of hydrology may have shaped divergent zooplankton community structure, leading to relatively high beta-diversity within each reservoir size class. We found that species replacement rather than species nestedness was responsible for beta-diversity. Nestedness usually occurs in natural systems where a group of species maintains a stable coexistence in local communities (Rohr et al., 2014). In urban landscapes, human disturbance inevitably modifies natural patterns and reduces nestedness. Composition of species that contributed to beta-diversity (SCBD) exhibited seasonal dynamics in our investigated reservoirs. These species were similar in species composition, but the exact contribution by each species varied between seasons. Species contribution to beta-diversity was strongly related to species characteristics such as occupancy, abundance, niche position and niche breadth (Heino and Grönroos, 2017). Generalist species with broad niches contribute less to beta-diversity than species with small- or intermediate-sized niches because species with a small niche breadth may occur in environmentally restricted conditions (Slatyer et al., 2013). However, pelagic species of rotifer Brachionus calyciflorus and cladocrean Bosminopsis deitersi in our reservoirs were usually considered to be cosmopolitan and euryoecious species (Kotov, 1997; Xiang et al., 2010), but they still highly contributed to beta-diversity.
5. Conclusion Our results provide evidence that urbanization can influence the community structure and assembly processes of zooplankton communities in tropical reservoir systems. The general similarities in species composition reflected by low beta-diversity and weak spatial variation across the studied reservoirs indicate homogenization of the structure and composition of zooplankton communities. In contrast to phytoplankton, zooplankton are more sensitive to their food and predation through bottom-up and top-down effects and less sensitive to the physicochemical variables. Increasing eutrophication reduces food limitation for zooplankton, but enhances predation with high fish production. We only detected Chla as a significant variable explaining the high variation of zooplankton community in the dry season. This suggests we need collect fish data to better understand zooplankton community under urbanization in the future.
4.3. Environmental variables shaping community structure Chlorophyll-a as a proxy of food in our study has a vital role in shaping zooplankton community structure. Zooplankton composition varied with spatial and temporal variation of Chla (Bartrons et al., 2015), thus pelagic zooplankton community showed strong spatial structure and seasonal dynamics. Food availability and quality can significantly affect growth and reproduction of zooplankton (Dumont, 1977; Irigoien et al., 2015). Productivity of the phytoplankton community is a well-known driver of among-lakes variation in zooplankton
Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 31670460) and the Science and Technology Project of Guangdong Province, China (No. 2015B020235007). We 7
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Fig. 5. Species contribution to beta-diversity (SCBD) across three seasons. Broken red vertical lines group species into independent categories based on SCBD values, that is, > 0.05, 0.05 ~ 0.04, 0.04 ~ 0.03, and 0.03 ~ 0.02. Each group is classified according to rotifer (black bar), cladocerans (dark grey bar), and copepoda (light grey bar). For better visualization, only the species with SCBD values larger than 0.02 were shown.
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Original Articles
Urbanization increases biotic homogenization of zooplankton communities in tropical reservoirs Ping Liu, Shaolin Xu, Jianhao Lin, Huiming Li, Qiuqi Lin, Bo-Ping Han
T
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Department of Ecology, Jinan University, Guangzhou 510632, People’s Republic of China
A R T I C LE I N FO
A B S T R A C T
Keywords: Urbanization Zooplankton Homogenization Spatial structure Seasonal dynamics Reservoir
Under rapid urbanization, many reservoirs built in rural areas are being embraced into by nearby expanding cities. Anthropogenic activities in urban areas can strongly affect aquatic biodiversity by modifying aquatic landscape and reduce habitat heterogeneity. As one of the key components in reservoir ecosystems, zooplankton is sensitive to such habitat changes. Within the metacommunity framework, we predict that in an urbanized landscape: (1) species sorting becomes the most important process in determining the variation of zooplankton communities; (2) urbanization will result in low beta-diversity and biotic homogenization of zooplankton communities and such homogenization will not significantly vary across time. To test these predictions, we investigated twenty five permanent reservoirs over three hydrological seasons in a well-developed and rapidly expanding city in tropical China. The reservoirs were grouped into two location classes and three storage size classes. Our results suggested that the structure of zooplankton communities were strongly homogenized in the studied urban reservoirs as being reflected in the low beta-diversity between the two location classes. However, high beta-diversity was found within each storage size class, mainly derived from species turnover (βsim). The high beta-diversity indicated that the variation of zooplankton community is a consequence of environmental heterogeneity within each storage size class. Variation partitioning revealed a weak spatial structure for zooplankton communities at the large scale, while only Chla as an indicator of food supply significantly explained high community variation among reservoirs in the dry season, but not in wet and transition seasons despite apparent species seasonal succession.
1. Introduction Metacommunity ecology integrates regional and local dynamics through the study of discrete local communities connected by dispersal of interacting species assemblages over landscapes (Leibold et al., 2004, 2010). Four major paradigms of metacommunity have been proposed to explain regional factors and processes influencing community dynamics and to incorporate spatial scales and landscape configuration into community structure. The relative importance of regional (dispersal, landscape and species pool) and local (abiotic and biotic) factors is the key to understanding the mechanisms of community assembly (Stoch et al., 2016; Viana et al., 2016). Dispersal limitation and environmental filtering are considered to be two major processes structuring aquatic communities, and species usually have different potentials for dispersal (Rahel, 2002; Beisner et al., 2006; De Bie et al., 2012). The extent to which community composition is shaped by dispersal over increasing spatial scales depends on the dispersal capacity of composing species (Juračka et al., 2016).
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Zooplankton is a central component of aquatic food webs and their diversity thus warrant further characterization (Dumont and Negrea, 2002; Mimouni et al., 2015; Xiong et al., 2017). Most zooplankton have a strong dispersal potential, and disperse passively with their resting eggs using multiple vectors such as water flow, birds, fish and winds. Overland and watercourse dispersal pathways are equally important to zooplankton, as they allow the organisms to track environmental changes (Declerck et al., 2011; Verbeek et al., 2018). At the intermediate regional scale where dispersal is not spatially limited, most species are able to reach suitable sites and local environmental factors would be vital in shaping community composition as predicted by the species sorting metacommunity model (Heino et al., 2015). In the tropics where water bodies are more or less connected either temporarily or permanently, fish are efficient and often highly selective predators with the capacity to shape species composition and size distribution of zooplankton communities (Lemmens et al., 2015). Many studies have focused on the pattern of regional diversity (i.e. beta-diversity) and on the variation of zooplankton community through
Corresponding author. E-mail address: [email protected] (B.-P. Han).
https://doi.org/10.1016/j.ecolind.2019.105899 Received 23 May 2019; Received in revised form 29 October 2019; Accepted 4 November 2019 1470-160X/ © 2019 Published by Elsevier Ltd.
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with a river network, while the east is more or less rural. Most of the reservoirs are eutrophic due to extensive land use and fish culture (Lei et al., 2014). Variation of zooplankton species composition between the reservoirs in the downtown and rural area is expected to be small due to the rapid urbanization. In the present study, we investigated the spatial variation and seasonal dynamics of reservoir zooplankton over an urban landscape at local and regional scales. The aim is to detect the potential effects of urbanization on zooplankton community. In the framework of metacommunity, we predict in an urbanized landscape that: (1) where dispersal becomes less limited, species sorting (or environmental filtering) becomes the most important process in determining the spatial structure of zooplankton communities; (2) urbanization results in low betadiversity and high biotic homogenization of zooplankton communities; and (3) such homogenization will not significantly vary with zooplankton seasonal dynamics.
time and space in natural ponds and lakes (La Store et al., 2014; Azevêdo et al., 2015; MacLeod et al., 2018; Wærvågen and Andersen, 2018). However, few studies have investigated the zooplankton diversity of water bodies in urban landscapes (Pinel-Alloul and Mimouni, 2013; Mimouni et al., 2015, Suski et al., 2018). Urban landscapes are highly modified and managed, and suffer from human-accelerated habitat changes (McDonald et al., 2008; McDonnell and Hahs, 2013; Knop, 2016; Rocha and Fellowes, 2018). Rapid urban expansion can intensively change the distribution, abundance and composition dynamics of organisms (Suski et al., 2018; Hanashiro et al., 2019). Human activities may also enhance the connectivity of water bodies scattered in urban landscapes (Grimmond, 2007; Silver et al., 2018). Consequently, urbanization may lead to a loss of diversity (Magura et al., 2010) and biotic homogenization (i.e. composition similarity), thereby causing low beta-diversity for regional aquatic communities (Pulliam, 1988; Holt et al., 2003; McKinney, 2006; Hill et al., 2018). The classic urban–rural gradient concept assumes a decline in land-use intensity from an intensively developed urban core to residential suburbs, culminating in lightly developed rural areas (McDonnell and Pickett, 1990; McDonnell and Hahs, 2008; Trentanovi et al., 2013). With higher biotic homogenization from urbanization activities, the reduced gradient no longer necessarily indicates shifts in habitat quality (Okuda et al., 2017; Warren et al., 2018). Reservoirs built for flood control and irrigation during the 1950s are abundant in tropical China where natural lakes are rare (Han and Liu, 2012). Reservoirs were initially constructed far from towns and cities to avoid flooding risk. However, urban expansion is creating more reservoirs to be located into urbanized areas. Understanding the mechanisms that shape species distribution under urbanization is especially challenging in aquatic ecosystem management, because temporal changes of local ecological conditions and habitat connectivity are associated with strong human activity (Han and Dumont, 2011). Reservoirs are mainly composed of pelagic zone, which is a much stable habitat with fewer zooplankton species than the littoral zone, and its ecological function is more sensitive to loss of species diversity. Pelagic zooplankton richness and species composition have therefore significant implications for the management of reservoir ecosystems (Walseng et al., 2006; Pinel-Alloul and Mimouni, 2013; dos Santos et al., 2018). Reservoirs zooplankton assemblages in an urban landscape at the regional scale constitute a metacommunity. Spatial structure of the zooplankton metacommunity depends on landscape and environmental factors such as spatial, hydrological and climatic variables (Cottenie et al., 2003; Cottenie and De Meester, 2004; Juračka et al., 2016; Zhao et al., 2018). In the tropics where water bodies, especially reservoirs, are more or less connected to each other temporarily or permanently, zooplankton can easily disperse with clonal individuals or resting eggs through the waterway from networks of rivers (Havel and Shurin, 2004). Zooplankton in such reservoirs are expected to be spatially more homogeneous in structure. Precipitation is a key environmental factor that imposes a strong influence on the seasonal dynamics of zooplankton metacommunity. In the wet season, connectivity between reservoirs tends to be stronger, facilitating passive dispersal of zooplankton and weakening environmental filtering (i.e. species sorting) (Shmida and Wilson, 1985). In the dry season, the connectivity becomes weaker, and environmental filtering increases over time (Bozelli et al., 2015; Chaparro et al., 2018). Reservoir storage size also affects richness and species composition of zooplankton at both local and regional scales. A small shallow reservoir becomes more turbid by suspending sediment and high phytoplankton biomass than a large deep reservoir (Gayosso-Morales et al., 2017; Stephan et al., 2017). Dongguan is one of the most important industrial cities in the Pearl River Delta where a large number of reservoirs were built mainly for drinking water supply and irrigation. Many of these reservoirs were built in the 1980s, thereafter the city has been expanding rapidly. The city has a distinct spatial character, with the center located to the west
2. Material and methods 2.1. Study area Dongguan is located within the Pearl River basin near the tropic of Cancer. It has an average annual precipitation of 1802 mm, which includes a long wet season that runs from April to September, which accounts for about 40% of the annual precipitation (Zheng et al., 2017). The average annual temperature is 23.3 °C. Precipitation is known to be the key abiotic factor affecting the connectivity of water bodies and regional diversity of zooplankton community structure (Primo et al., 2015). We investigated 25 permanent reservoirs covering a total area of 30 km (north to south) × 40 km (west to east) (Fig. 1). Physical coordinate ranges from 22.6752°N to 23.0631°N latitude and 113.6853°E to 114.2101°E longitude. Altitude ranges from 2 m to 105 m. The larger reservoirs are primarily used for drinking water supply and each of them is relatively well protected and managed by an organized team, while the smaller reservoirs were mainly used for irrigation and fishing, as well as for backup drinking water supply during the dry season. The reservoirs are physically scattered and isolated from each other, and the degree of connectivity depends on their spatial distance. 2.2. Sampling and laboratory work Sampling and field measurements for water quality, phytoplankton and spatial information of the 25 reservoirs were conducted in the middle of the wet season (July 2011), transition season (December 2011), and dry season (March 2012). A 5L sampler was used to collect 50L of subsurface water by collecting vertically along the water column in the pelagic area of each reservoir and filtering the sampling using a 38-µm mesh net. Samples were preserved using a final concentration of 4% formalin solution for further analysis. With the exception of copepodites and copepod nauplii, specimens were identified to the species level under a dissecting microscope according “Guides to the identification of the microinvertebrates of the continental waters of the world” (Dumont and Negrea, 2002). Zooplankton abundance was determined by counting at least 10% of the concentrated sample in a SedgewickRafter counting chamber, with a minimum of 200 counted individuals. Zooplankton biomass was estimated by the abundance data and size measurements of specimens following Dumont et al. (1975) and Zhang and Huang (1991). Zooplankton samples from two reservoirs in July and three reservoirs in December were lost during repacking and moving to the lab. Phytoplankton samples were preserved with Lugol’s solution and identified to the lowest possible taxonomic level. The samples were analyzed under an inverted microscope in 10-ml sedimentation chambers (Utermöhl, 1958) and specimens were identified at genus level because the morphological features of the organisms did not allow to certainly assign them to know “species”. At least 400 algal individuals 2
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Fig. 1. Twenty-five investigated reservoirs in the Pearl River basin located within an area of 30 km (north to south) × 40 km (west to east). Red star: the municipal center of Dongguan; light blue circles: small storage size reservoirs; green circles: medium storage size reservoirs; dark blue: large storage size reservoirs.
(Lin et al., 2001). The species occurrence dataset was used for species richness estimation. Eight estimators of species richness were used. Log transformation was used for all estimators so that the lower values of the resulting interval were at least equal to the number of observed species (Chao, 1987). The lowest ratio of variance/estimators was used as the best estimation (Dumont and Segers, 1996). Only the lowest variance/ estimator of the first order jackknife was used to estimate regional species richness (Palmer, 1990; Pinel-Alloul et al., 2013). True Simpson diversity index was used to determine the species diversity for each group (Jost, 2007). Principal component analysis (PCA) was performed on all quantitative environmental variables to explore the change from wet season to dry season. Sampling sites were scaled (scaling 1) in proportion to eigenvalues. We performed the nonmetric multidimensional scaling (nMDS) based on zooplankton’s abundance Bray-Curtis distance matrices to reveal the ordination relationship between species abundance and sampling seasons. A two-way permutational multivariate analysis of variance (PERMANOVA) was conducted to test for the overall significant differences in the zooplankton community structure among the three classes of reservoir storage size and between the three seasons. A separate PERMANOVA was performed for downtown and rural reservoirs to test the effect of urbanization on community structure of zooplankton in reservoirs. We also calculated Bray-Curtis distance among downtown reservoirs and among rural reservoirs, as well as the Bray-Curtis distance of any pair zooplankton communities between one downtown reservoirs and one rural reservoir, and tested the difference in these distances with unpaired t-test. Beta-diversity, the spatial variation of the species composition of sites, has two components: species turnover and nestedness (Baselga, 2010). These components are the result of two processes: species replacement and species loss (Baselga, 2010). The overall beta-diversity (ßsor) and its components, turnover (ßsim) and nestedness (ßsne), were estimated using Sorensen index adapted for multiple sites. Components of beta-diversity and species contribution to beta-diversity (SCBD) were calculated with species richness (Baselga, 2013; Dray et al., 2017). As phytoplankton could affect the structure of zooplankton community, we performed a Mantel test on the zooplankton and phytoplankton Bray-
were counted from each sample and the size of each algal cell was measured. Phytoplankton biomass was calculated using an estimated wet weight density of 1 g/cm3 (Hillebrand et al., 1999). 2.3. Spatial and environmental variables Geographical coordinates and altitude were recorded using a handheld GPS unit. Distance from the municipal center to each reservoir was calculated using Google Earth. Water chemistry data were measured using a portable analyzer (YSI: PRO-PLUS), including water temperature (T), pH, dissolved oxygen (DO), and conductivity (Cond). Reservoir storage and dead water level was obtained from the local hydrographic bureau. A Secchi disk was used to determine water transparency (Tran). Total nitrogen (TN) and total phosphorus (TP) were measured by the potassium persulfate oxidation method. Chlorophyll a (Chla) was analyzed using a simplified method by Lin et al. (2003). Nitrate (NO3−), nitrite (NO2−), and phosphate (PO43−) were all included as environmental variables. A Beacon Cylindrospermopsin Plate kit method was used to detect Microcystis toxins (MT). 2.4. Statistical analyses We used the distance of each reservoir to nearest town as a proxy to measure a general urbanization density imposed on the reservoirs. We grouped the investigated reservoirs in two ways: (1) two location classes of downtown and rural reservoirs based on the distance from the city center to each reservoir, i.e. downtown > 25 km and adjacent rural < 25 km; and (2) three size classes according to their storage size, namely large (> 10 × 106 m3, eight), medium (5 × 106 ~ 10 × 106 m3, nine), and small reservoirs (< 5 × 106 m3, eight). Euclidean distance matrices for all environmental variables were calculated to compare characteristics of the different classes of reservoirs. Eutrophication is a process of nutrient enrichment of anthropogenic or natural origin, and trophic state categories have been proposed for lakes, rivers and reservoirs (Cigagna et al., 2016). The trophic state index (TSI) was used to classify the trophic status of the investigated reservoirs. Total phosphorus (TP), total nitrate (TN), chlorophyll a (Chla) and secchi disk depth (SD) were used to determine the average TSI according to the empirical equations for reservoirs in the province 3
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reservoir size classes (R2 = 0.036, p = 0.046), and a small but significant difference between downtown and rural reservoirs (R2 = 0.025, p = 0.017) (Table 1). Except in wet season (p = 0.0161), the averaged Bray-Curtis distance among downtown reservoirs was smaller that the distance and among rural reservoirs (dry season: p = 0.0404; transition season: p = 0.145) (Fig. S1). This means that the zooplankton have more similar species composition among downtown reservoirs than among rural reservoirs. The mean Bray-Curtis distance of a pair zooplankton communities between one downtown reservoir and one rural reservoir was significantly greater than 0.5 (p < 0.0001), e.g., 0.666 in the transition season, 0.589 in the wet season and 0.589 in the dry season.
Curtis dissimilarity matrices of abundance and biomass data. Distance-based Moran's eigenvector maps (dbMEMs, Dray et al., 2006; Borcard et al., 2011) were used to explore the spatial structure of zooplankton in the landscape of reservoirs. This approach allows a spectral decomposition of the spatial structure by producing orthogonal eigenvectors that can be used to represent spatial relationships among reservoirs. The first few eigenvectors (MEM1) describe the large scale spatial structure. The last few eigenvectors (MEMn), with the lower eigenvalues, describe fine spatial structure (Griffith and Peres-Neto, 2006; Peres-Neto and Legendre, 2010). Forward selection was applied to dbMEMs spatial predictors to pick out significant Moran’s indices. Variation partitioning was carried out to reveal the effect of pure environment variables (E), of pure spatial variables (S), as well as of their joint effect (E + S) on community composition (Wang et al., 2012). Combined season datasets and single season datasets were used in the spatial analysis. All statistical analyses were performed using R 3.3.3 (R Core Team, 2013), and the packages being “spacemakeR v0.0-5’, “packfor v0.0-8”, “AEM v0.6”, “PCNM v2.1-2”, “adespatial v0.1-0”, “betapart v1.5.0”, “vegan v2.4-5”, “ade4 v1.7-10”, “spdep v0.7-4”, “ape v5.0”, “ellipse v0.4.1”, “MASS v7.3-50”.
3.3. Beta-diversity and its seasonal dynamics A high beta-diversity was detected across all the investigated reservoirs (βsor = 0.96), mainly originating from species turnover (βsim = 0.93), and only negligibly from nestedness (βsne = 0.03). No significantly seasonal difference in regional beta-diversity among reservoirs was found (βsor in wet: 0.893, in transition: 0.894, in dry: 0.889). Beta-diversity within each reservoir class was higher than that between the reservoir classes (Fig. 4). Species contributing to beta-diversity (SCBD) varied over the three seasons, showing apparent seasonal dynamics. Numbers of dominant species contributing to beta-diversity declined from wet season to dry season. In the wet season, two species of rotifer (Brachionus calyciflorus, Asplanchna priodonta), one species of cladocera (Bosminopsis deitersi), and one species of copepod apart from nauplius (Mesocyclops thermocyclopoides) significantly contributed to regional beta-diversity. No species in the transition season significantly contributed to regional beta-diversity, whereas dominant species in the dry season contributed evenly to regional beta-diversity. Species with a SCBD value larger than 0.02 numbered 19, 22 and 24 species for each of the three seasons (Fig. 5).
3. Results 3.1. Environmental condition and trophic status Measured environmental variables significantly varied for each reservoir classes in the three seasons. The smaller reservoirs had higher Chla and TP than the larger reservoirs did in all seasons (p < 0.05). Chla and Cond for downtown reservoirs were higher than rural reservoirs (p < 0.01). A higher TP was recorded in downtown reservoirs than in rural reservoirs in the transition season and dry season but not in the flooding season (p < 0.05) (Fig. 2). Large reservoirs were more homogenous with a relatively lower Euclidean distance compared to medium and small reservoirs, but no significant differences were detected between downtown reservoirs and rural reservoirs, although error bars of those indicators in the same type of reservoirs were so large, especially in dry season. The reservoirs were classified into either mesotrophic (30 < TSI < 50) and eutrophic (TSI > 50), while no oligotrophic reservoirs (TSI < 30) were detected. The trophic state was dynamic or fluctuating for many reservoirs; with three reservoirs in the wet season, five reservoirs in the transition season, and eight reservoirs in the dry season stayed in a mesotrophic state. Among all investigated twenty five reservoirs, only two reservoirs stayed at the mesotrophic state in all three seasons. The others were presumed to have undergone eutrophication.
3.4. Variation of zooplankton community and explaining variables Main environmental conditions for the downtown reservoirs (R1R12) were seasonally different from those of the rural reservoirs (R13R25) (see Appendix: Fig. S2). Zooplankton communities showed a clear succession from wet season to dry season (see Appendix: Fig. S2). Cladocerans and copepods were more likely to appear in the wet season, whereas most rotifers appeared in transition and dry seasons. nMDS analysis did not detect clear ordination differentiation between downtown and rural reservoirs. Mantel test showed significant correlations of both zooplankton abundance (p = 0.044) and zooplankton biomass (p = 0.022) with phytoplankton in the wet season, but only zooplankton abundance (p = 0.013) in the dry season (see Appendix: Table S1). Variation partitioning was conducted for both species presence-absence data and abundance data. Only the dry season zooplankton community in the presence-absence data was significant in the model test for spatial analysis. The first (MEM1) of the 8 positive MEM variables significantly explained the low spatial variation (3.7%), suggesting a broad-scale process affecting zooplankton occurrence in the dry season, corresponding to a low beta-diversity between downtown and rural reservoirs. However, environment variables (Chla) explained high part (10.8%) of the total variance in dry seasons, but not in the other two seasons.
3.2. Zooplankton richness and its seasonal dynamics In total, we identified 46 species of rotifers, 10 species of cladocerans, and 16 species of copepods from all the samples collected in the three seasons. The most common species in observed were: Polyarthra vulgaris (39), Brachionus calyciflorus (34), and Asplanchna priodonia (31) in rotifers; Bosmina sp. (21) and Bosminopsis deitersi (22) in cladocera; and Mesocyclops thermocyclopoides (27) in copepods. Species richness increased from the wet season to transition season and to dry season in each reservoir class. Simpson diversity increased for large and rural reservoirs in the transition season, and decreased in the dry season (Fig. 3). Most of the eight used estimators of species richness provided a higher predicted richness than the actual observation in estimating regional species richness. Species richness confidence interval in the investigated region ranged from 80 to 91 in these reservoirs. No significant difference was found between the three size classes or between the two location classes. PERMANOVA revealed significant differences in the structure of zooplankton community among the three seasons (R2 = 0.129, p = 0.001) and among three
4. Discussion Low beta-diversity between downtown and rural reservoirs regardless of seasonal dynamics suggests strong biotic homogenization under urbanization. The relatively higher beta-diversity within each reservoir class indicates that environmental filtering is the most 4
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Fig. 2. Characteristics of environmental variables for two groups of reservoir classes based on reservoir size and its distance to Dongguan City. LWS: large reservoirs in wet season, LTS: large reservoirs in transition season, LDS: large reservoirs in dry season. MWS: medium reservoirs in wet season, MTS: medium reservoirs in transition season, MDS: medium reservoirs in dry season. SWS small reservoirs in wet season, STS: small reservoirs in transition season, SDS: small reservoirs in dry season. UWS: downtown reservoirs in wet season, UTS: downtown reservoirs in transition season, UDS: downtown reservoirs in dry season. RWS: rural reservoirs in wet season, RTS: rural reservoirs in transition season, RDS: rural reservoirs in dry season.
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Fig. 3. Species richness and true Simpson diversity for two groups of reservoir classes. Hollow circles represent richness or diversity index in each reservoir, and triangles represent mean value in each class. The abbreviations of reservoirs are shown in Fig. 2.
abundance in reservoirs. Within some taxonomic groups, small-sized species are expected to dominate urban communities, as dispersal limitation increases with increasing body size in zooplankton (De Bie et al., 2012; Oertli and Parris, 2019). Small species were more diverse in more urbanized waterbodies in contrast to large-bodied species, which were more diverse in less urbanized systems (Gianuca et al., 2018). While eutrophication of reservoirs is beneficial for cosmopolitan species, oligotrophic species such as Daphnia galeata were absent from the present investigation. Regional species richness depends on biological taxa and spatial scale in investigation. Pelagic habitats of tropical reservoirs usually harbor fewer species under habitat homogenization. In our case, the total regional species richness was low in all three seasons, and rotifers contributed to gamma diversity far more than cladocerans and copepods. Urban water bodies are often eutrophic with frequent disturbance and strong predation pressure from high cultured fish density (Zhao et al., 2013, 2015). Thus, the zooplankton community tends to be composed of small species; for instance, the common rotifer species in our reservoirs are small sized and capable of escaping predators visually.
Table 1 Two-Way PERMANOVA for zooplankton community across three sampling seasons and three size classes and two distance classes. The three size classes are large (> 10 × 106 m3), medium (5 × 106 ~ 10 × 106 m3), and small reservoirs (< 5 × 106 m3). The two distance classes are downtown (< 25 km) and adjacent rural area (> 25 km). Df
Sums of Sqs
Mean Sqs
F.Model
R2
Pr (> F)
Season Storage Season × Size Residuals Total
2 2 4 61 69
3.686 1.018 1.664 22.263 28.630
1.843 0.509 0.416 0.365
5.050 1.395 1.140
0.129 0.036 0.058
0.001*** 0.046* 0.18
season distance season × distance Residuals Total
2 1 2 64 69
2.707 0.550 0.485 18.024 21.766
1.354 0.550 0.243 0.282
4.806 1.952 0.861
0.124 0.025 0.022
0.001*** 0.017* 0.718
Significance codes: * 0.05; ** 0.01; *** 0.001.
important process in determining the variation in zooplankton communities. The Chla data indicating food availability is important in structuring zooplankton communities, particularly in the dry season. Nevertheless, no significant difference in regional beta-diversity among reservoirs was detected between the three seasons investigated.
4.2. Beta-diversity and seasonal dynamics As expected, low beta-diversity was observed between the three reservoir size classes and between the two location classes, indicating that urbanization greatly homogenizes the environmental habitats, and a few “urban-adaptable” species may become increasingly widespread and locally abundant (McKinney, 2006). In the present study, spatial distribution pattern of reservoirs largely contributes to the low betadiversity among the reservoir size classes. Small and large reservoirs are scattered randomly, facilitating dispersal and migration of zooplankton between size classes at the local scale. However, beta-diversity within each reservoir size class was relatively higher than expected. The higher
4.1. Species richness and community composition Zooplankton communities in our investigated reservoirs composed mostly of common or tropical cosmopolitan species. Rotifers were dominated by small-bodied common species such as Polyarthra vulgaris, Brachionus calyciflorus, and Asplanchna priodonia. Small sized species such as Bosmina and Bosminopsis were dominant in Cladocera. Urbanization resulted a lower number of zooplankton species and lower 6
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Fig. 4. Spatial turnover and nestedness components of beta-diversity based on incidence distance matrices within three seasons and across each group. ßsne: value of the nestedness component, ßsim: value of the turnover component. WS: wet season, TS: transition season, DS: dry season. BWS: between distance classes in wet season, BTS: between distance classes in transition season, BDS: between distance classes in dry season. AWS: among three size classes in wet season, ATS: among three size classes in transition season, ADS: among three size classes in dry season.
communities and seasonal dynamics, and it was also found to be important in our investigated reservoirs. No significant environmental variables were detected to explain the variation of zooplankton communities in the wet season and the transition season, which was the same situation for the study of zooplankton in subtropical river systems (Zhao et al., 2017). The difference in environmental variables in the two seasons becomes much lower and insignificant because of strong disturbance by rainfall. Strong precipitation in the wet season may cause water overflow or flooding of reservoir, and may enhance the immigration and emigration of species between adjacent reservoirs (Kim et al., 2012). Stocking of bighead carp and silver carp is common in our investigated reservoirs, and an increase in fish density in the growth season could greatly affect the structure of zooplankton communities through predation, resulting in dramatic changes to their abundance and richness (Gardiner et al., 2014; Rocha and Fellowes, 2018). Fish predation could have a negative effect on zooplankton abundance and community structure (Zhang et al., 2013; Sato et al., 2018) and it accounts for the scarcity of largesized cladocerans during summer months in lakes in the subtropics (Havens et al., 2015). Our multivariate analysis explored bottom-up effect from phytoplankton represented by Chla, but no top-down effect from fish predation was explored because of a lack of fish data, despite its significance for regulating zooplankton composition.
beta-diversity within each reservoir size class implies the local communities are distinct and heterogeneous. Therefore, urban water bodies potentially provide a diverse range of habitats for a mixture of common and rare macro-invertebrate taxa and represent a valuable biodiversity resource within anthropogenically dominated landscapes (Hill et al., 2015, 2017). The compositional variation among local communities (beta-diversity) may reflect environmental heterogeneity, but may also reflect reduced exchange of organisms between habitat patches (Leibold and Mikkelson, 2002; Legendre, 2014). In the present study in Dongguan, dubbed the “world factory” for its booming manufacturing industry, the influence of human activities on reservoirs changes with distance from the reservoirs to the downtown center and with reservoir size. Distinct eutrophication of reservoirs and alteration of hydrology may have shaped divergent zooplankton community structure, leading to relatively high beta-diversity within each reservoir size class. We found that species replacement rather than species nestedness was responsible for beta-diversity. Nestedness usually occurs in natural systems where a group of species maintains a stable coexistence in local communities (Rohr et al., 2014). In urban landscapes, human disturbance inevitably modifies natural patterns and reduces nestedness. Composition of species that contributed to beta-diversity (SCBD) exhibited seasonal dynamics in our investigated reservoirs. These species were similar in species composition, but the exact contribution by each species varied between seasons. Species contribution to beta-diversity was strongly related to species characteristics such as occupancy, abundance, niche position and niche breadth (Heino and Grönroos, 2017). Generalist species with broad niches contribute less to beta-diversity than species with small- or intermediate-sized niches because species with a small niche breadth may occur in environmentally restricted conditions (Slatyer et al., 2013). However, pelagic species of rotifer Brachionus calyciflorus and cladocrean Bosminopsis deitersi in our reservoirs were usually considered to be cosmopolitan and euryoecious species (Kotov, 1997; Xiang et al., 2010), but they still highly contributed to beta-diversity.
5. Conclusion Our results provide evidence that urbanization can influence the community structure and assembly processes of zooplankton communities in tropical reservoir systems. The general similarities in species composition reflected by low beta-diversity and weak spatial variation across the studied reservoirs indicate homogenization of the structure and composition of zooplankton communities. In contrast to phytoplankton, zooplankton are more sensitive to their food and predation through bottom-up and top-down effects and less sensitive to the physicochemical variables. Increasing eutrophication reduces food limitation for zooplankton, but enhances predation with high fish production. We only detected Chla as a significant variable explaining the high variation of zooplankton community in the dry season. This suggests we need collect fish data to better understand zooplankton community under urbanization in the future.
4.3. Environmental variables shaping community structure Chlorophyll-a as a proxy of food in our study has a vital role in shaping zooplankton community structure. Zooplankton composition varied with spatial and temporal variation of Chla (Bartrons et al., 2015), thus pelagic zooplankton community showed strong spatial structure and seasonal dynamics. Food availability and quality can significantly affect growth and reproduction of zooplankton (Dumont, 1977; Irigoien et al., 2015). Productivity of the phytoplankton community is a well-known driver of among-lakes variation in zooplankton
Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 31670460) and the Science and Technology Project of Guangdong Province, China (No. 2015B020235007). We 7
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Fig. 5. Species contribution to beta-diversity (SCBD) across three seasons. Broken red vertical lines group species into independent categories based on SCBD values, that is, > 0.05, 0.05 ~ 0.04, 0.04 ~ 0.03, and 0.03 ~ 0.02. Each group is classified according to rotifer (black bar), cladocerans (dark grey bar), and copepoda (light grey bar). For better visualization, only the species with SCBD values larger than 0.02 were shown.
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