Shallow reservoirs in urban perimeter: evaluation of trophic status and relations with the zooplanktonic community

Reservatórios rasos em perímetro urbano: avaliação do estado trófico e relações com a comunidade zooplanctônica

Abstract:

Aim  The zooplankton community is used as a bioindicator of environmental changes and can be an indicator of trophic status in aquatic environments through changes in the composition of the community. The objective of this work was to study the variation of shallow reservoir systems in an urban park, evaluating the relationships between physical, chemical, and biological variables.

Methods  The collections were conducted monthly for a year in the surface of reservoirs. The physical and chemical variables of the water were measured using a multiparametric probe on the surface. Nutrient analysis was performed using spectrophotometry in the laboratory. Zooplankton was collected using a plankton net (60 µm mesh size). Principal Coordinate Analysis (PCoA) was used to verify whether the three reservoirs present differences in zooplankton community composition.

Results  Altogether, 43 taxa belonging to 16 families were collected. Rotifera was the most representative group, with 27 taxa, Cladocera had 13 taxa, and Copepoda had only three taxa. The environmental variables indicated different trophic status between the reservoirs, demonstrating greater eutrophication in reservoirs 1 and 3. An association between the composition of the zooplankton community and the trophic state of the reservoirs was verified.

Conclusions  The composition of the zooplankton community shows differences among three of the shallow urban reservoirs studied. Reservoir 1 exhibited Filinia terminalis and Asplanchna herrick as indicator species. As for reservoir 2, with a lower trophic status, the indicative species were Bosmina freyi and Diaphanosoma polyspina, correlated with lower concentrations of nitrate, nitrite, total phosphorus, pH, and lower values of electrical conductivity. Reservoir 3 exhibited Brachionus angularis and Brachionus calyciflorus as indicator species, demonstrating a similar nutrient profile to R1, but with higher nitrate concentrations.

Keywords:  bioindicator; Cladocera; Copepoda; Rotifera

Resumo:

Objetivo  A comunidade zooplâncton é utilizada como bioindicador de mudanças ambientais e pode ser um indicador do estado trófico em ambientes aquáticos através de mudanças na composição da comunidade. O objetivo deste trabalho foi estudar a variação de sistemas de reservatórios rasos em um parque urbano, avaliando as relações entre variáveis físicas, químicas e biológicas.

Métodos  As coletas foram realizadas mensalmente durante um ano na superfície dos reservatórios. As variáveis físicas e químicas da água foram medidas por meio de sonda multiparamétrica na superfície. A análise dos nutrientes foi realizada por espectrofotometria em laboratório. O zooplâncton foi coletado com rede de plâncton (malha de 60 µm). A Análise de Coordenadas Principais (PCoA) foi utilizada para verificar se os três reservatórios apresentam diferenças na composição da comunidade zooplanctônica.

Resultados  Ao todo foram coletados 43 táxons pertencentes a 16 famílias. Rotifera foi o grupo mais representativo, com 27 táxons, Cladocera teve 13 táxons e Copepoda teve apenas três táxons. As variáveis ambientais indicaram diferentes estados tróficos entre os reservatórios, demonstrando maior eutrofização nos reservatórios 1 e 3. Foi verificada associação entre a composição da comunidade zooplanctônica e o estado trófico dos reservatórios.

Conclusões  A composição da comunidade zooplanctônica apresenta diferenças entre três dos reservatórios urbanos rasos estudados. O reservatório 1 exibiu Filinia terminalis e Asplanchna herrick como espécies indicadoras. Já no reservatório 2, com menor status trófico, as espécies indicativas foram Bosmina freyi e Diaphanosoma polyspina, correlacionadas com menores concentrações de nitrato, nitrito, fósforo total, pH e menores valores de condutividade elétrica. O reservatório 3 exibiu Brachionus angularis e Brachionus calyciflorus como espécies indicadoras, demonstrando perfil nutricional semelhante ao R1, mas com maiores concentrações de nitrato.

Palavras-chave:  bioindicador; Cladocera; Copepoda; Rotifera

1. Introduction

Urban reservoirs are essential for the sustainable development of cities and for the stability of ecosystems. They are created for thermal comfort, aesthetics, recreation (Ding et al., 2015; Luthy et al., 2020), and in some cases, for use as a water resource and rainwater retention basin (Gu et al., 2017). However, the lack of urban planning, disorderly constructions, untreated discharge of pollutants into water resources, and lack of maintenance make aquatic environments susceptible to degradation (Spahr et al., 2020, Chen et al., 2021). When built for rainwater storage, the reservoirs receive the insertion of allochthonous material, responsible for the incorporation of nutrients and pollutants (for example, nitrogen and phosphorus, coming from punctual or diffuse contamination media, such as sewage), causing a eutrophication process, which can bring as consequences the change in physical and chemical parameters, with modification of the composition of the aquatic community (Shen et al., 2021, Shen et al., 2022). In addition to other anthropic factors involved in the construction of these systems (such as land use, change in the watercourse, and use of water resources, which will impact the quality and quantity of water), natural factors are also associated, such as soil type, presence of riparian forest, geology, and terrain slope. The depth of aquatic environments also influences hydrodynamics, with shallow systems having the potential for particle resuspension through animal and climatic action (such as wind action), increasing the rate of nutrients and the trophic status (Scheffer, 2004; Ji, 2008; Antenucci et al., 2013). To detect the influence of these factors on water quality, ecological knowledge of these systems is necessary, both in their natural condition and in their responses to anthropic interference (Bucci et al., 2015).

The environmental quality of lentic systems, such as urban reservoirs, can be analyzed based on biological communities, such as the zooplankton community, which plays a vital role in nutrient and energy cycling (Ngochera & Bootsma, 2018; Zhang et al., 2018), in addition to locomotion capacity and longitudinal and latitudinal variation in the water column (Santos et al., 2018). Zooplanktonic organisms can be used as an indicator of environmental quality, reflecting changes in physical, chemical, and biological processes (Midya et al., 2018; Raut & Shembekar, 2015), because they have characteristics such as a short life cycle and rapid response to the various impacts that occur in the environment through changes in quantity, composition, and diversity (Das & Kar, 2016). However, studies are still needed to adjust their use as biological indicators in low-order rivers that have been transformed into reservoirs.

The objective of this work was to study the variation of shallow reservoir systems in an urban park, evaluating the relationships between physical, chemical, and biological variables, with the following hypotheses: i) the structure of the zooplankton community changes between the three connected urban reservoirs and between the sampling points within each reservoir due to the habitat condition; ii) the physical and chemical parameters influence the composition of the zooplankton community in the analyzed reservoirs system.

2. Materials and Methods

2.1. Study area

Three urban reservoirs were studied, built by damming a first-order river and connected in series: reservoir 1 (R1) is fed by water from a spring, reservoir 2 (R2) receives water from the first reservoir and surface runoff and its flow rushes directly into reservoir 3 (R3), which still receives water from the rainwater system, functioning as a retention basin. Reservoir 1 has birds (ducks and geese), R2 has the rooted macrophyte Typha domingensis (approximately 37% of it is occupied). The reservoirs are located in Parque dos Lagos, at a headwater of the Córrego Vertente Grande watershed, a tributary of the Frutal river (20º01’27” and 20º01’23”S; 48º55’20” and 48º55’33”W), with altitude varying between 527 and 540 m and about 53.000 m² in area, in the city of Frutal, Minas Gerais, Brazil (Figure 1).

Figure 1
Location of the study area, with identification of each reservoir (Frutal, Minas Gerais, Brazil). Where: R1 = reservoir 1; R2 = reservoir 2; R3 = reservoir 3.

The climate of the region is seasonal tropical Aw, according to the Köppen-Geiger classification (Alvares et al., 2013), with dry winters and rainy summers, and an average annual temperature and precipitation of 23.8ºC and 1626.9 mm, respectively (Accuweather, 2020). The total monthly precipitation for the sampled period was obtained from Accuweather (2020).

2.2. Physical and chemical variables and trophic state of reservoirs

Water samples were monthly collected from May/2019 to April/2020. Physical and chemical parameters of the water were measured at three sampling points in each reservoir (one at the water inlet, one at the intermediate (central) region, and another at the water outlet), totaling nine collection points (108 samples), with samples taken from the subsurface layer of water (Figure 1).

At each sampling point we measured the water temperature (Temp; °C), electrical conductivity (Cond; µS cm^-1), pH, turbidity (Turb; NTU), dissolved oxygen (DO; mg L^-1), oxidation -reduction potential (ORP; mV), and total dissolved solids (TDS; mg L^-1), using a multiparameter probe (HORIBA, U-50). The water samples for chemical determination were placed in previously cleaned polyethylene bottles with a storage capacity of 500 mL. Total phosphorus (TP; µg L^-1), orthophosphate (OP; µg L^-1), nitrate (NO₃; µg L^-1), nitrite (NO₂; µg L^-1), and total ammonia nitrogen (TAN; µg L^-1) were determined spectrophotometrically, according to Golterman et al. (1978) and Koroleff (1976).

The trophic state index of the reservoirs was calculated according to the phosphorus concentration (Lamparelli, 2004).

2.3. Definition of the sample volume, collection, and identification of the zooplankton community

A pilot sampling was performed to determine the sample volume. Therefore, 20, 50, 100, and 200 L were collected in the three reservoirs using a graduated bucket. Each volume was filtered through a plankton net (60 µm mesh size), placed in 500 mL dark flasks, and preserved in 4% formalin.

A qualitative analysis of the organisms was conducted using a stereomicroscope, binocular microscope, and taxonomic bibliography (Koste, 1978; Reid, 1985; Segers, 1995; Perbiche-Neves, 2011; Elmoor-Loureiro & Sousa, 2021). Quantitative analysis of cladocerans and copepods was performed on cross-linked acrylic plates. Stereomicroscope, nauplius, and rotifers were quantified in 1 mL subsamples in Sedgewick-Rafter chambers, with a binocular microscope. Quantification was conducted until reaching 100 individuals of the most abundant species, or the entire sample when the density did not reach this value (Bicudo & Bicudo, 2004).

Friedman’s nonparametric test was used to verify differences in species richness and abundance between different volumes, with Dunn’s post hoc test. The volume of 20 liters presented a higher abundance of species and richness, so this quantity was used for the quantitative sampling of the zooplankton community (Figure 2). The 20 L sampling was conducted first, and it might have interfered with the subsequent samplings, given the small size of the reservoirs. As the other volumes were withdrawn sequentially, the initial extraction could have influenced the reduction in species richness due to disturbances in the same area.

Figure 2
Friedman’s non-parametric test, used to verify differences in species richness and abundance between different volumes, with Dunn’s post hoc test.

With the volume of 20 defined, the zooplankton community was sampled at three points in each reservoir (Figure 1), totaling nine collection points, with sampling between May/2019 and April/2020 (n=108). The sampling, sorting, and identification procedures were the same as those presented for choosing the sample volume.

2.4. Data analysis

The ecological attributes richness, density (ind m^-3), abundance, relative abundance, Shannon index, and Simpson's dominance were calculated using the Past software (Hammer et al., 2001).

To verify the difference in the physical and chemical variables between the reservoirs, the average of the sampling points of each reservoir was used, where the months were considered replicas (Gotelli & Ellison, 2016). The normality of the data and homoscedasticity of the variances were verified by the Lillierfors and Bartlett’s test at the significance level α = 0.05, verifying that the data do not fit the premises for the analysis of variance with the R Development Core Team (2021). Abiotic data were evaluated by Principal Components Analysis (PCA) and undertaken according to software Statistica 10 (StatSoft, Inc., 2011).

Principal Coordinate Analysis (PCoA) was performed based on the similarity matrix using the Bray-Curtis index to verify whether the three reservoirs have differences in community composition. The data were standardized and transformed into square roots. Differences in community composition between reservoirs and collection points, as well as interaction, were analyzed using Permutational Multivariate Analysis of Variance (PERMANOVA) with a two-way crossed design (Anderson, 2001), with reservoir and collection site being fixed factors.

The relationship between zooplanktonic composition and abiotic variables was examined using Distance-Based Linear Model Analysis (DistLM) (Legendre & Anderson, 1999). After performing the correlation analysis between the variables, it was decided to remove the TDS variable from the model due to the autocorrelation (70%) with Cond. The “step-wise” selection procedure and the adjusted R² selection criterion were used. Distance‐based redundancy analysis (dbRDA) was used to visualize the DistLM results (Anderson et al., 2008; Clarke & Gorley, 2006; Clarke & Warwick, 2001).

Analysis of indicator species (IndVal) was performed to identify potential zooplanktonic species indicating the trophic status of the reservoirs, considering the number of individuals and environmental parameters (Dufrene & Legendre, 1997). Species with significant IndVal results (p<0.05) above 70% were considered indicator species for environmental conditions, being more sensitive to environmental changes. Species with intermediate IndVal, between 45% and 70%, were considered species that detect environmental conditions, being more resistant to environmental changes, with higher adaptability to environmental changes. In this study, the value ≤45% was used as a limit to demarcate the IndVal index, following the criterion adopted by Verdú et al. (2011).

Abundance distribution was performed to verify whether the dominance of species varied between reservoirs. These curves ordered the species from the most abundant to the least abundant. Ranks were constructed for each reservoir. The abundance data were transformed into log + 1.

3. Results

3.1. Physical and chemical variables of reservoir water

The principal component analysis (PCA) with 12 abiotic variables retained 48.36% of the variability from the original data on the first two axes (axis 1 = 27.84%; axis 2 = 20.52%). The variables TDS (-1.79), OP (-1.70), Cond (-1.68), Turb (1.52), and NO₂ (-1.22), positioned on the negative side of PC1, associated with most of the points representing R1 and R3. The variables Temp (0.80) and ORP (0.36) positioned on the positive side of PC1, associated with R2. The variable NO₃ (-2.33) is positioned on the negative side of PC2 (Figure 3). Observing the distribution of points, it is possible to identify that in PC1 we have the representation of the trophic degree, where there is an increase in it from the positive side to the negative side.

Figure 3
Principal component analysis (PCA) of the physical and chemical parameters from the water of the three reservoirs studied. Where: circles = R1; triangle = R2; square = R3; DO = Dissolved oxygen; Temp = Temperature; Cond = Electrical conductivity; TDS = Total Dissolved Solids; ORP = Oxidation-reduction potential; Turb = Turbidity; NO₃ = Nitrate; NO₂ = Nitrite; TAN = Total Ammonia Nitrogen; PT = Total Phosphorus; OP = Orthophosphate.

All reservoirs had mesotrophic conditions (Table 1).

Table 1
Values found for the trophic state index of water in the studied reservoirs, according to Lamparelli (2004).

Sites	TSI (PT)			Classification
	Minimum	Maximum	Mean
R1	35	59	49.9	mesotrophic
R2	37	53	41.8	mesotrophic
R3	40	53	46.9	mesotrophic

• R1 = Reservoir 1; R2 = Reservoir 2; R3 = Reservoir 3; TSI = Trophic State Index.

3.2. Zooplankton community structure

A total of 43 taxa were identified and distributed in 16 zooplankton families. Rotifera was the most representative group with 27 taxa (61.90%), followed by Cladocera with 13 taxa (30.95%) and Copepoda with only three taxa (7.17%). Rotifera was represented by seven families (Brachionidae, Asplanchnidae, Gastropodidae, Trochosphaeridae, Lepadellidae, Lecanidae, and Synchaetidae). The two families that contributed significantly to the species richness in the reservoirs were Lecanidae, with ten species, and Brachionidae, with eight species.

Cladocera was represented by five families (Chydoridae, Bosminidae, Macrothricidae, Sididae, and Ilyocriptidae), with the following species most found in the samples: Bosmina freyi, Diaphanosoma polyspina, Diaphanosoma spinulosum, Chydorus cf. sphaericus, and Chydorus pubescens. Copepoda was represented by three families: Diaptomidae and Cyclopidae, with species Notodiaptomus deitersi, Thermocyclops decipiens, and Thermocyclops minutus, and an unidentified family belonging to the order Harpacticoida.

In the Rotifera group, 14 species were recorded in R1 and 13 species in the other two reservoirs. Brachionus and Lecane were the most represented genus in this study. For Cladocera, 12 species were recorded in reservoir 1 (R1), 11 in reservoir 2 (R2), and only one in reservoir 3 (R3). The most represented genus were Bosmina and Chydorus (three species each). For Copepoda, three species were recorded, belonging to the genera Notodiaptomus and Thermocyclops, and all of them appeared in the three reservoirs.

The largest number of individuals among the three reservoirs was found in R3, with a value seven times higher than in the other reservoirs (Figure 4). Rotifera was the group with the highest relative abundance in all reservoirs (92.7% in R1; 62.3% in R2 and 99.7% in R3). The highest relative abundance of Cladocera was observed in R2 (21.6%), followed by R1 (1.4%). Among the orders of Copepoda, Calanoida showed higher relative abundance than Cyclopoida. Cyclopoida had higher relative abundance in R1 (1.1%), followed by R2 (0.9%), and R3 (0.3%) (Figure 4).

Figure 4
Relative abundance graph of the groups studied in the three reservoirs. R1: Reservoir 1, R2: Reservoir 2, R3: Reservoir 3. Values above the bars represent the number of individuals m^-3 in each reservoir.

The reservoir R1 showed the highest richness (9), followed by R2 (8) and R3 (7). Species dominance ranged from 0.36 (R1) to 0.38 (R2). When analyzing the result of the Shannon index, which assigns the same value to rare and abundant species, it is possible to state that the three reservoirs have species uniformity (Table 2).

Table 2
Mean and standard deviation of the results of the ecological indices of the three reservoirs studied.

	Richness	Dominance	Shannon
R1	9±2.4	0.36±0.1	1.34±0.3
R2	8±2.5	0.38±0.2	1.34±0.2
R3	7±2	0.37±0.2	1.30±0.4

• R1 = Reservoir 1; R2 = Reservoir 2, R3 = Reservoir 3.

3.3. Classification of species (Ranks)

Based on the species classification (ranking), Brachionus calyciflorus showed abundance values higher than 0.05 in both R1 and R3, demonstrating high abundance (Figure 5). Another species that stood out as abundant in R3 is Brachionus angularis. In R2, the most abundant species was Notodiaptomus deitersi, followed by Brachionus dolabratus. Note that the genus Brachionus was the one that stood out the most in terms of abundance.

Figure 5
Rank of zooplankton species found in the studied reservoirs. Where: circles = R1; triangle = R2, and square = R3. (A) Brachionus calyciflorus; (B) Brachionus dolabratus; (C) Gastropus cf hyptopus; (D) Asplanchna cf herrick; (E) Filinia terminales; (F) Lecane cf inermes; (G) Notodiaptomus deitersi; (H) Polyartrha vulgaris; (I) Bosmina freyi; (J) Brachionus angularis; (K) Diaphanosoma polyspina; (L) Brachionus falcatus; (M) Lecane bulla; (N) Trichocerca pusila; (O) Brachionus dimidiatus.

3.4. Influence of environmental variables on the zooplankton community

The composition of the zooplankton community showed a difference between the three reservoirs (Pseudo-F = 28.983; p = 0.001), where R1 was different from R2 (t = 4.14; p = 0.001), R1 different from R3 (t = 6.65; p = 0.001), and R2 different from R3 (t = 5.38; p = 0.001). Neither the sampling location within the reservoirs (Pseudo-F = 0.56004; p = 0.897) nor the interaction of factors (Pseudo-F = 0.66145; p = 0.922) proved to be significant (Figure 6).

Figure 6
Principal Coordinate Analysis (PCoA) constructed from the Bray-Curtis dissimilarity matrix of the three reservoirs and three sampling points within each reservoir.

The dbRDA plot for the composition of the zooplankton community and environmental variables demonstrates that the variables pH, electrical conductivity (Cond), dissolved oxygen (DO), oxidation-reduction potential (ORP), nitrite (NO₂), and total phosphorus (TP) influence the community composition, explaining 32.5% of the total variation in the data (Figure 7). In the marginal test, pH, DO, Cond, ORP, NO₂, and TP showed a significant relationship with the multivariate cloud of the data derived from the species when considered alone, without considering the other variables (Table 3). Variables NO₂, pH, Cond, TP, ORP, and DO explain around 11%, 10%, 7%, 7%, 6%, and 5% of the variation, respectively.

Figure 7
Distance-based redundancy analysis (dbRDA). Relationship between the ordering of sampling points based on the composition of the zooplankton community and environmental variables, as follows: DO (dissolved oxygen), Cond. (electrical conductivity), NO₃ (nitrate), NO₂ (nitrite), Prep. (precipitation), TP (total phosphorus), ORP (oxidation-reduction potential), and TAN (total ammonia nitrogen).

Table 3
Results of Distance-Based Linear Modeling (DistLM) between the relationship of environmental variables and the composition of the zooplankton community in marginal tests (variation explained by a single variable) and in sequential tests (variation explained by adding new variables at a time to obtain the criterion of optimal fit) using the adjusted R² selection procedure.

DistLM marginal test result
Variables		SS (trace)	Pseudo-F	P	Proportion
Dissolved oxygen		4576.8	20.959	0.047*	0.0581
pH		8507.1	41.135	0.003*	0.1079
Temperature		2223.4	0.9868	0.476	0.0282
Conductivity		5533.0	25.668	0.019*	0.0702
ORP		5212.9	24.078	0.030*	0.0661
Turbidity		3808.4	17.261	0.097	0.0483
Nitrate		3408.8	15.369	0.161	0.0436
Nitrite		8837.7	42.935	0.001*	0.1121
TAN		1910.5	0.8446	0.597	0.0242
Total Phosphorus		5817.8	27.095	0.010*	0.0738
Orthophosphate		2704.6	12.081	0.286	0.0343
Precipitation		1928.0	0.8525	0.552	0.0244
DistLM sequential test result
Variables	R2	SS (trace)	Pseudo-F	P	Proportion	Cumul. P
Nitrite	0.0860	8837.7	42.935	0.001*	0.1121	0.1121
Conductivity	0.1365	5815.5	29.907	0.022*	0.0737	0.1859
Nitrate	0.2097	7220.7	40.574	0.002*	0.0916	0.2775
DO	0.2561	5018.2	29.956	0.006*	0.0636	0.3411
pH	0.2865	3725.6	23.186	0.028*	0.0472	0.3884
Precipitation	0.2997	2470.1	15.663	0.134	0.0313	0.4197
TAN	0.3037	1828.5	11.661	0.314	0.0231	0.4429
Total Phosphorus	0.3046	1625.6	10.381	0.396	0.0206	0.4636
ORP	0.3067	1690.0	10.825	0.368	0.0214	0.4850

• ORP = Oxidation Reduction Potential; DO = Dissolved oxygen; TAN = Total Ammonia Nitrogen; SS = Sum of Squares; Cumul. P = Cumulative value of P.
• ^*
  P<0.05.

Nitrite was taken first in the sequential test. In addition, the variable that most increased the R² criterion was electrical conductivity (Cond). These two explain around 39% of the variation in the data cloud. The best solution suggested was the model with nine variables (pH, electrical conductivity, dissolved oxygen, nitrate, nitrite, ammonia nitrogen, total phosphorus, ORP, and precipitation), which combined explain around 48% of the total variation (Table 3).

3.5. Indicator species (IndVal)

The IndVal test identified four indicator species and 12 detectors of the environmental conditions of the reservoirs (Table 4). For reservoir 1 (R1), Asplanchna herrick and Filinia terminalis can be considered as indicator species, while Brachionus dolabratus, Notodiaptomus deitersi, and Thermocyclops minutus were classified as detector species. The reservoir 2 (R2) does not have indicator species but has two detector species belonging to the Cladoceran order (Bosmina freyi and Diaphanosoma polyspina). The reservoir 3 (R3) had two indicator species (Brachionus angularis and Brachionus calyciflorus) and five detectors (Brachionus falcatus, Brachionus dimidiatus, Polyarthra vulgaris, Trichocerca pusila, and Thermocyclops decipiens).

Table 4
Values obtained for indicator species of the studied environments.

Species	Reservoirs	Note IV	Mean	SD	p (*)	Classification
Filinia terminalis	R1	77.4	15.9	4.3	0.0002	Indicator
Asplanchna herrick	R1	74.5	15.3	3.6	0.0002	Indicator
Thermocyclops minutus	R1	53.6	19.7	4.9	0.0002	Detector
Brachionus dolabratus	R1	50.4	19.2	4.3	0.0002	Detector
Notodiaptomus deitersi	R1	50.3	28.9	3.9	0.0006	Detector
Bosmina freyi	R2	61.1	16.5	4.0	0.0002	Detector
Diaphanosoma polyspina	R2	58.7	25.3	5.2	0.0004	Detector
Brachionus angularis	R3	85.4	41.8	6.6	0.0002	Indicator
Brachionus calyciflorus	R3	81.6	73.4	4.7	0.0344	Indicator
Polyarthra vulgaris	R3	68.0	25.2	5.8	0.0002	Detector
Trichocerca pusila	R3	65.0	29.0	6.7	0.0002	Detector
Brachionus falcatus	R3	64.3	21.7	4.7	0.0002	Detector
Thermocyclops decipiens	R3	60.1	20.2	4.4	0.0002	Detector
Brachionus dimidiatus	R3	46.7	18.3	4.5	0.0004	Detector

• Where: R1 = reservoir 1; R2 = reservoir 2; R3 = reservoir 3; Note IV = index result for each species; SD = standard deviation. Significant values are in bold and with *. The highlighted species are those that have an index above 70%, being considered as an indicator.

4. Discussion

The study showed that the zooplankton community is sensitive to changes in the physical and chemical characteristics of the aquatic environment, altering the structure of the community when there are changes in trophic conditions, being efficacious and good indicators of environmental quality in shallow urban water systems, with indicator and detectors species of trophic conditions. The zooplankton community structure is influenced by abiotic factors, climatic and hydrological regime (Serafim-Júnior et al., 2011; Medeiros et al., 2020).

The pH of reservoirs R2 and R3 was lower, comparing to R1. This result may be associated with the frequent input of allochthonous material from surface runoff, and, consequently, higher decomposition rates, increasing the electrical conductivity values, especially in R3. Under artificial conditions, such as those found in this study, the high electrical conductivity values are due to wastewater discharges from urban activities — which occurs directly in the studied environments — and it is a crucial variable in the identification of pollutant load in aquatic environments, collaborating to evaluate the availability of ions in aquatic systems (Leira et al., 2017). The total phosphorus concentration (TP) is also incorporated into natural environments, mainly due to human activities, such as the discharge of urban wastewater containing superphosphate detergents (Yuan et al., 2018). Thus, the highest amounts observed in R1 and R3 may originate from the action of surface runoff, introducing allochthonous material. In addition, the presence of aquatic avifauna in R1, composed of members of the Anatidae family (e.g., ducks, geese), helps in the increase of nutrients promoted by their activities, acting in the process of increasing the trophic status of the system (Adhurya et al., 2021). The time factor also has an influence on this nutrient — the longer the life and use of the systems, the higher the presence of phosphorus due to its retention in the sediment. In R1, the existence of birds promotes upturn, resuspending nutrients to the water column (Reddy et al., 1996).

The zooplankton community has several species that are perceptible to changes in the environment. Most are sensitive and end up being replaced, while others can thrive and occupy the various available niches (De-Carli et al., 2017). Rotifera were the most abundant organisms in the zooplankton community in all reservoirs. It is due to their opportunistic habits and high environmental adaptability, which give them the ability to colonize environments, especially in environments with high levels of nutrients (Arruda et al., 2017; Brito et al., 2020). Brachionus was the most abundant genus among the Rotifera, also observed by other authors who studied reservoirs (García-Chicote et al., 2019; De-Carli et al., 2017), proving to be a good indicator of environments with greater trophy (García- Chicote et al., 2019) due to the high abundance in R1 and R3 (Figures 4 and 7; Table 4).

The results of the dbRDA analysis indicated the importance of the variables nitrate and electrical conductivity in the structure of zooplankton communities (Figures 6 and 7). The high values for these parameters in the third reservoir (R3) occur because the system receives surface rainwater runoff from upstream neighborhoods and clandestine sewage disposal, which increases the nutrient load and the trophic status. Similar results were found in the research carried out by Rosińska et al. (2019), that verified that the concentration of physical and chemical parameters in urban lakes models the structure of the zooplankton community, causing the selection of species according to their plasticity (Zaganini et al., 2011).

The Cladoceran fauna showed higher abundance in R2 (Figure 4). It can be inferred that the representative presence of this group only in R2 is related to the presence of aquatic macrophytes (Elmoor-Loureiro & Mendonça-Galvão, 2008), which have a sheltered role and help in the process of removing nutrients from the system, which consequently reduces the trophic status (Sahidin et al., 2018; Jurczak et al., 2019). The presence of Cladocera is related to less eutrophic conditions, such as low levels of nitrate, nitrite, total phosphorus, orthophosphate, and electrical conductivity (Mashkova et al., 2021), a fact that was observed in this study through the association of the detector species Bosmina freyi and Diaphanosoma polyspina and the low concentrations of the variables mentioned above, corroborating the studies by Jurczak et al. (2019) and Otake et al. (2020), in which the community responds directly to the nutrient concentrations in the system and its productivity.

The analysis of indicator species (IndVal) showed that each environment has a set of species. Among the 14 species presented, 10 are detector species and the other four are indicators. However, some studies (McGeoch et al., 2002; García-Chicote et al., 2019; Schmidt et al., 2020) claim that indicator species with high IV values may not be efficient in monitoring ecological changes, as they are highly specific and restricted to particular environmental conditioning. Therefore, detector species that present moderate levels of specificity are more useful in verifying environmental conditions, as they have higher adaptability to different variations of the environment, resisting and remaining in it (McGeoch et al., 2002; Hunt & Hosie, 2006). Detector or generalist species have a greater possibility of locomotion to nearby habitats with elevated speed when there are changes in habitat conditions, unlike indicator or specialist species, which undergo abrupt change and may be replaced or disappear from that environment (McGeoch et al., 2002).

Concerning the environmental conditions of indicator and detector species, for R1, the community composition is associated with the nitrite variable. The species B. dolabratus, T. minutus, and N. deitersi are usually found in environments with eutrophic conditions (De-Carli et al., 2017; Landa & Colchete, 2020). In R3, among the seven species found as indicators and detectors, four are of the genus Brachionus, one of Trichocerca, one of Polyarthra, and one representative of Copepoda (Thermocyclops decipiens), which, according to the dbRDA analysis, are indicators of the conditions of this reservoir, that is, an environment with conditions of high conductivity and nitrogenous compounds concentrations, as observed by Oliveira et al. (2015).

The community structure in the studied systems did not show differences in ecological indices, richness, abundance, and diversity. However, the community composition was different among the three studied environments, and the abiotic variables are responsible for modeling the community composition. The sensitivity of the analyses can explain this since the indices show an overview of the community, which is widely used for quick sampling (Chawla et al., 2012) and have more or less strong correlations with other parameters. An example is the Shannon index, which is influenced by species richness and composition uniformity (Magurran, 2013).

As much as the three environments have connections, the PCoA analysis showed that the community composition is different among the three systems. This information agrees with the data collected by Cottenie & De-Meester (2003), that, when studying connectivity parameters between reservoirs, did not find evidence that this is a determining parameter for the composition of the dam community. However, this is not a single pattern. Local and specific characteristics of each area involve biotic and abiotic interactions that interfere in the dynamics of each community, shaping its structure (Thompson & Townsend, 2006).

The Shannon index showed that the reservoirs have low species diversity compared to other urban reservoirs studied by Rosińska et al. (2019) and Gayosso-Morales et al. (2017). This low species diversity may be linked to environmental conditions, such as depth and physical and chemical changes in the water. In the abundance rank in which it is used as an ecological pattern of dominance, the species B. calyciflorus and B. dolabratus showed the highest dominance for R1, the species Notodiapttomus deitersi and B. dolabratus were dominant in R2, and B. angularis and B. calyciflorus were in R3. Despite its dominance (as demonstrated by the ranking of species), IndVal indicated that B. calyciflorus is not an indicator or detector species due to the analytical criteria and parameters. IndVal is a more sensitive analysis; it evaluates the composition of parameters attributing habitat specificity (presence in samples and abundance). Therefore, when evaluating the dbRDA analysis with IndVal, it can be inferred that the species that detect the conditions found in R1 (T. minutus, B. dolabratus, and N. deitersi) are associated with the variable nitrite. The species B. freyi and D. polyspina were the detector species for R2, which according to the dbRDA analysis, is associated with total ammonia nitrogen (TAN). And for R3, the detector species were P. vulgaris, T. pusila, B. falcatus, T. decipiens, and B. dimidiatus, which according to the dbRDA, are associated with the variables electrical conductivity, nitrate, dissolved oxygen and ORP.

5. Conclusion

The composition of the zooplankton community shows differences among three of the shallow urban reservoirs studied. The community composition in R1 and R3 was associated with total dissolved solids, orthophosphate, electrical conductivity, turbidity, total phosphorus, and nitrite concentration. R1 exhibited Filinia terminalis and Asplanchna herrick as indicator species. As for R2, with a lower trophic status, the indicative species were Bosmina freyi and Diaphanosoma polyspina, correlated with lower concentrations of nitrate, nitrite, total phosphorus, pH, and lower values of electrical conductivity. R3 exhibited Brachionus angularis and Brachionus calyciflorus as indicator species, demonstrating a similar nutrient profile to R1, but with higher nitrate concentrations.

Acknowledgements

Thanks to the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for the scholarship granted to the first author to conduct the project. VK thanks the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) and the Universidade do Estado de Minas Gerais (UEMG) for funding.

• Cite as: Martins, H.L. et al. Shallow reservoirs in urban perimeter: evaluation of trophic status and relations with the zooplanktonic community. Acta Limnologica Brasiliensia, 2024, vol. 36, e15. https://doi.org/10.1590/S2179-975X8322

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Edited by

• Associate Editor: Priscilla de Carvalho.

Publication Dates

• Publication in this collection
  20 May 2024
• Date of issue
  2024

History

• Received
  17 Dec 2022
• Accepted
  28 Mar 2024