Primary productivity drives temporal beta diversity of zooplankton communities in urban lakes

A produtividade primária determina a diversidade beta temporal em comunidades zooplanctônicas de lagos urbanos

Abstract

Aim  The objective of this study was to evaluate the temporal beta diversity of zooplankton communities in urban lakes in two distinct seasonal periods and to associate this diversity with environmental variability and primary productivity.

Methods  We sampled 14 urban lakes in Goiânia (Goiás state, Brazil) in February (rainy season) and September (dry season) 2023. Water samples were collected in each lake, to analyze phosphate and nitrogen nutrients. Furthermore, some limnological variables were obtained in the field. The zooplankton community was collected by filtering 200 liters of water through a 68 µm mesh and subsequent identification in the laboratory using a microscope.

Results  We identified 110 taxa, with the greatest richness of rotifers and testate amoebae. The temporal beta diversity (TBI) varied between 0.46 and 0.78 and was not correlated with the temporal variability of the abiotic factors. On the other hand, TBI was negatively associated with mean chlorophyll-a concentration (proxy of primary productivity).

Conclusions  In these urban lakes, high-productivity environments (e.g., eutrophic systems) promote biological homogeneity. We recommend that future studies incorporate additional variables to better understand environmental variability in urban lakes, as well as extend research over longer time scales.

Keywords:
TBI; seasonality; community structure

Resumo

Objetivo  O objetivo deste estudo foi avaliar a diversidade beta temporal das comunidades zooplanctônicas em lagoas urbanas em dois períodos sazonais distintos e associar essa diversidade com a variação ambiental e com a produtividade primária.

Métodos  Nossa coleta foi realizada em 14 lagoas urbanas em Goiânia (Goiás, Brasil) em fevereiro (período de chuva) e setembro (período de seca) de 2023. Em cada lagoa alguns parâmetros limnológicos foram obtidos em campo e amostras de água foram levadas para o laboratório para posterior análise dos nutrientes fosfatados e nitrogenados. A comunidade zooplanctônica foi obtida em cada lagoa através da filtragem de 200 litros de água em uma rede de 68 µm de abertura de malha e posterior identificação no laboratório utilizando microscópio.

Resultados  No total, 110 táxons foram identificados, com predominância da maior riqueza de rotíferos e tecamebas. A análise da diversidade beta temporal (TBI) variou entre 0,46 e 0,78 e tais valores não foram correlacionados com a variação temporal dos fatores abióticos estudados. Por outro lado, a diversidade beta temporal foi negativamente explicada pela concentração média de clorofila-a (medida indireta da produtividade primária).

Conclusões  Nestas lagoas urbanas, ambientes com alta produtividade primária (por exemplo, sistemas eutróficos) promoveram homogeneidade biológica. Recomendamos que estudos futuros incorporem variáveis adicionais para uma compreensão mais abrangente da variabilidade ambiental em lagoas urbanas, além de considerar escalas temporais mais extensas.

Palavras-chave:
TBI; sazonalidade; estrutura das comunidades

1. Introduction

Environmental factors, such as climate, soil, and water's physical and chemical properties, play a key role in shaping species distribution. Changes in these factors can lead to shifts in community structure across both spatial and temporal scales, thereby influencing patterns of beta diversity (Lindholm et al., 2020). The analysis of changes in community structure over time is known as temporal beta diversity (Legendre & Gauthier, 2014; Shimadzu et al., 2015), and it is widely used by ecologists to assess species' responses to temporal variations in environmental conditions (Pereira et al., 2024). Several indices have been developed to measure temporal beta diversity, using both presence-absence data and species abundance data (Baselga, 2010, 2012; Anderson et al., 2011). Among these, the Temporal Beta Index (TBI) proposed by Legendre (2019) stands out, as it quantifies changes in community structure between two time points, measuring both species losses and gains over that period (Vale et al., 2021; Simões et al., 2022).

Studies evaluating the influence of different environmental parameters on species losses or gains over time have become crucial for understanding the main factors driving changes in species composition and/or abundance over time (Choi et al., 2020; Simões et al., 2022). Environmental heterogeneity, for example, can explain changes in community structure across space or time, given that heterogeneous environments provide distinct ecological niches for a wider range of species (Heino et al., 2013; Zorzal-Almeida et al., 2017; Pereira et al., 2024). Some studies have also demonstrated a relationship between primary productivity and beta diversity. Although increasing productivity may enhance resource availability (Chase, 2010; Bini et al., 2014; Simões et al., 2022), highly productive environments, such as eutrophic systems, may exhibit biological homogenization (Zorzal-Almeida et al., 2017).

In this context, the use of organisms that respond rapidly to environmental changes becomes essential, such as zooplankton communities in aquatic environments (Araújo & Nogueira, 2016; Santos et al., 2025). In general, these communities consist of organisms from the groups Testate Amoebae, Rotifera, Cladocera, and Copepoda, which exhibit distinct morphologies and therefore respond differently to environmental changes (Bonecker et al., 2013). Furthermore, zooplankton organisms have low mobility, making them effective bioindicators in aquatic systems (Bonecker et al., 2013).

Therefore, the use of bioindicators that respond quickly to environmental disturbances is a valuable tool for managing aquatic ecosystems, especially in urban areas that experience high levels of pollution (Geminiano et al., 2021). In recent years, some studies have highlighted the importance of urban green areas (green spaces) and urban lakes (blue spaces) in providing a wide range of ecosystem services, particularly cultural services (Li et al., 2017; Oertli & Parris, 2019; Hasan et al., 2020). Thus, understanding the aquatic communities within these ecosystems, and the factors that shape them is crucial for guiding future management strategies for urban lakes (Teurlincx et al., 2019; Martins et al., 2024).

Thus, the main objective of our study was to assess the temporal beta diversity of zooplankton communities in urban lakes during two distinct seasonal periods in 2023 (rainy and dry seasons), and to relate this diversity to environmental variability and primary productivity. We expect elevated beta diversity between the rainy and dry seasons, driven by abiotic changes in lake conditions. Furthermore, we expect a negative relationship between temporal beta diversity and the concentrations of chlorophyll-a, a proxy of primary productivity, considering the possibility of eutrophication in some of these urban lakes. On the other hand, we expected a positive relationship between temporal beta diversity and environmental variability.

2. Material and Methods

2.1. Study area

This study was conducted in Goiânia, a large city located in the state of Goiás (Central-West region), Brazil, with a population of approximately 1,437,366 inhabitants. This city is home to several urban parks, some of which contain small lakes formed by the damming of streams that run through the city. A total of 14 lakes were sampled across various regions of the city (Figure 1 and Table 1).

Figure 1
Spatial distribution of the 14 urban lakes studied. L1 – Leolídio di Ramos Caiado Park; L2 – Liberdade Park; L3 – Beija Flor Park; L4 – Balneário Park; L5 – Cascavel Park; L6 – Vaca Brava Park; L7 – Areião Park; L8 – Botanical Garden; L9 – Flamboyant Park; L10 – Fonte Nova Park; L11 – Rosas Park; L12 – Buritis Park; L13 – Nova Esperança Park; and L14 – João Carlos Fernandes de Oliveira Park.

Table 1
Location and area of the 14 lakes sampled in different regions of the city of Goiânia, Goiás, Brazil.

Lakes	Region	Park	Latitude	Longitude	Lake area (m2)
L1	North	Leolídio di Ramos Caiado Park	16o37’42.8”	49o15’28.4”	11,103
L2	North	Liberdade Park	16o38’59.0”	49o14’02.7”	2,085
L3	North	Beija flor Park	16o39’09.4”	49o13’48.4”	4,201
L4	North	Balneário Park	16o36’54.6”	49o17’03.8”	851
L5	South	Cascavel Park	16o43’54.0”	49o17’35.4”	8,162
L6	South	Vaca Brava Park	16o42’30.7”	49o16’15.3”	14,278
L7	South	Areião Park	16o42’17.2”	49o15’13.9”	10,578
L8	South	Botanical Garden Amália Teixeira Franco	16o43’30.9”	49o15’17.3”	10,813
L9	South	Flamboyant Park	16o42’13.0”	49o14’15.1”	5,253
L10	Northwest	Fonte Nova Park	16o37’27.9”	49o18’48.5”	2,179
L11	Central	Rosas Park	16o40’43.3”	49o16’28.1”	11,080
L12	Central	Buritis Park	16o40’49.2”	49o15’40.2”	8,133
L13	Northwest	Nova Esperança Park	16o38’19.7”	49o19’54.1”	5,443
L14	West	João Carlos Fernandes de Oliveira Park	16o41’30.5”	49o21’09.8”	3,274

Sampling was conducted in February and September 2023, representing the rainy and dry seasons, respectively. The rainy season in this region usually falls in spring and summer, from October to April, with an average annual precipitation of between 1,200 and 1,800 mm. In contrast, the dry season lasts from May to September, with average precipitation values ranging from 100 mm to 400 mm during this period (Costa et al., 2012).

2.2. Sampling and laboratorial analysis

At each lake, we measured in the field the water temperature (^oC), pH, electrical conductivity (µS.cm^-1), and dissolved oxygen (mg.L^-1) using a digital probe (YSI). We also collected water samples for laboratory analysis (concentrations of total phosphorus (µg L⁻¹), dissolved phosphorus (µg L⁻¹), nitrate (mg L⁻¹) and ammonium nitrogen (mg L⁻¹)). Turbidity (NTU) was measured using a Digimed digital turbidimeter. On the same day as sampling, a subsample of water was filtered through Whatman GF/C glass-fiber filters (nominal pore size ~1.2 μm), and the filters were stored frozen for subsequent determination of chlorophyll-a concentrations (µg L⁻¹).

For the zooplankton community, in each lake, we collected 200L of water using a graduated bucket and filtered the water through a plankton net with a 68-μm mesh size. The sampling was carried out along the lake margins, and the bucket was thrown a few meters away from the shore with the aid of a rope. The collected material was stored in polyethylene bottles and preserved in a 4% formaldehyde solution buffered with calcium carbonate.

In the laboratory, unfiltered water samples were used to analyze total phosphorus, while orthophosphate, nitrate, and ammoniacal nitrogen concentrations were measured using filtered samples. Concentrations of total and dissolved phosphorus were determined using the ascorbic acid method, while nitrate and ammonium nitrogen were measured using the cadmium reduction and salicylate methods, respectively. All readings were performed with a spectrophotometer, and analyses followed the protocols outlined in Standard Methods (APHA, 2005). Chlorophyll-a concentrations were quantified by extraction with 90% acetone and subsequent spectrophotometric analysis, following Golterman et al. (1978).

For quantitative and qualitative analyses, the zooplankton samples were concentrated to final volumes ranging from 45 to 300 mL, depending on the amount of sediment and organisms. We identified organisms using a microscope. Density was estimated by counting, in Sedgwick–Rafter chambers, at least 10% of the total volume of each sample (Bottrell et al. 1976). The final density was expressed as individuals per m³, using the Equation 1:

$$N=n\times \left(V_{bottle}/V_{analyzed}\right)\times \left(1000/V_{filtered}\right)$$ (1)

where: n is the number of individuals of the species counted in the quantitative analyses; V_bottle is the volume of the sample preserved in 4% formaldehyde; V_analyzed is the volume of the subsample analyzed under the microscope (Sedgwick–Rafter chamber); 1000 corresponds to the conversion factor to 1 cubic meter; and V_filtered is the volume of water filtered through a 68 µm mesh net.

2.3. Data analysis

Initially, we conducted a Principal Component Analysis (PCA) to reduce the dimensionality of the set of environmental variables (Legendre & Legendre, 2012) and to explore the limnological characteristics of the lakes studied during the rainy and dry seasons. Prior to the analysis, the limnological parameters were log-transformed, except for the pH values. The Broken-Stick method (Jackson, 1993) was used as a stopping-rule to explain the axes in the PCA. We also applied a Permutational multivariate analyses of variance (PERMANOVA; Anderson, 2001) to compare the limnological variables between rainy and dry seasons, using the adonis2 function from vegan package (Oksanen et al., 2022) with 999 permutations.

Temporal beta diversity analysis (TBI) was used to measure changes in the structure of the zooplankton community between time 1 (February 2023) and time 2 (September 2023) (Legendre, 2019). This index ranges from 0 to 1 and can be decomposed into two components: the abundances-per-species loss and gains between the two periods (Legendre, 2019). For this analysis, we used zooplankton species density data, applying the percentage difference dissimilarity index (% difference), which corresponds to the Bray-Curtis index. Additionally, we tested whether the observed difference between the two periods was statistically significant using a paired t test. Considering that species abundances are not normally distributed, we also applied a permutation test (999 permutations). We also performed a temporal beta diversity analysis (TBI) analysis considering the different zooplankton groups: Testate Amoebae, Rotifera, and Microcustaceans (Cladocera and Copepoda). The analysis was performed using the TBI.R function from the adespatial package (Dray et al., 2019).

To identify potential factors associated with temporal beta diversity (TBI for the whole community, testate amoebae, rotifers, and microcrustaceans), we used beta regression (Cribari-Neto & Zeileis, 2010), using betareg package (Zeileis et al., 2013). This approach is appropriate because our response variable (TBI) varied between 0 and 1. As explanatory variables, we included the environmental variability between the sampling periods (rainy and dry seasons) and primary productivity (mean chlorophyll-a concentration measured in both periods). In this way, chlorophyll-a concentration was used as a proxy for primary productivity. Environmental variability was obtained using a standardized Euclidean distance of all abiotic parameters (water temperature, dissolved oxygen, pH, electrical conductivity, turbidity, total phosphorus, dissolved phosphorus, nitrate, and ammonium nitrogen) measured in the rainy and dry seasons. To compute the Euclidean distance, the data were log-transformed prior to analysis (except pH). We decided to synthesize these variables, given the correlations among some of them and the fact that our main objective was to assess the relative importance of two key factors: environmental variability between the two periods and the primary productivity of the lakes. All analyses were performed in R program (R Core Team, 2024).

3. Results

The sampled lakes showed high concentrations of dissolved oxygen (mean concentration of 6.66 mg.L^-1 during the rainy season and 6.86 mg.L^-1 during the dry season), except for the lakes located in L4 (Balneário Park; 2.8 mg.L^-1 and 1.46 mg.L^-1 in the rainy and dry season, respectively) (Table 2). The first two axes of the PCA explained 49.8% of the data variability. According to the Broken-Stick model, only the first axis was significant (33%) and was negatively correlated with nitrate (NO₃) and positively correlated with turbidity, total phosphorus and chlorophyll-a (Figure 2). We did not observe a clear distinction between the rainy and dry seasons. Indeed, PERMANOVA revealed no significant effects of seasonal variability on environmental conditions (R² = 0.062; F = 1.73; p = 0.164). However, some lakes stood out during the dry season, exhibiting high concentrations of total phosphorus and chlorophyll-a. Indeed, when analyzing the mean values, we found that electrical conductivity, turbidity, and both total and dissolved phosphorus reached their highest levels during the dry season (Table 2).

Table 2
Descriptive statistics (mean, minimum, maximum, and standard deviation-S.D.) of the limnological variables measured in each urban lake sampled during the rainy (February 2023) and dry (September 2023) seasons in Goiânia (Goiás, Brazil).

	Rainy				Dry
Limnological parameters	Mean	Min.	Max.	S.D.	Mean	Min.	Max.	S.D.
Water temperature (oC)	27.16	24.7	29.4	1.22	30.94	29.21	34.04	1.37
Dissolved oxygen (mg.L-1)	6.66	2.80	10.80	2.08	6.86	1.46	10.42	2.62
pH	7.37	5.92	10.41	1.00	7.54	5.30	10.53	1.50
Electrical conductivity (µS.cm-1)	101.76	22.6	165.7	47.83	123.79	15.0	207.0	55.01
Turbidity (NTU)	18.57	3.21	63.70	15.70	31.11	7.40	66.80	18.11
Total phosphorus (µg.L-1)	39.42	5.63	87.80	26.37	82.98	10.61	266.66	69.45
Dissolved phosphorus (µg.L-1)	2.63	0.12	6.37	1.97	4.73	<0.10	31.17	8.39
Nitrate (mg.L-1)	3.65	<0.01	11.49	4.14	3.48	0.187	13.10	4.31
N-ammonium (mg.L-1)	0.127	<0.01	0.881	0.24	0.105	0.005	0.722	0.19
Chlorophyll-a (µg.L-1)	8.12	0.819	30.034	7.96	8.17	0.273	23.208	7.33

Figure 2
Score distribution of lakes along the first two principal components (PC1 and PC2). Variables highly correlated with the first principal component are indicated in the figure. Rainy season = February 2023; Dry season = September 2023. DO= dissolved oxygen; DP = dissolved phosphorus; TP= total phosphorus; Chlo = chlorophyll-a; EC = electrical conductivity; NO₃ = nitrate and NH₄ = N-ammonium.

Considering the zooplankton communities, we can highlight the high density of rotifers and copepods in the studied lakes (Figure 3a). During the rainy season, we observed a higher density of rotifers (mean = 29,906 ind.m^-3), followed by copepods (mean = 29,262 ind.m^-3), testate amoebae (mean = 2,113 ind.m^-3), and cladocerans (mean = 1,746 ind.m^-3), whereas in the dry season, copepods showed the highest density (mean = 26,052 ind.m^-3), followed by rotifers (mean = 17,392 ind.m^-3), cladocerans (5,491 ind.m^-3), and testate amoebae (mean = 2,414 ind.m^-3) (Figure 3a). The highest density values were recorded in L11 (Rosas Park) during the rainy season and in L3 (Beija-Flor Park) during the dry season (Figure S1, available at the Data Availability).

Figure 3
Mean (a) density (log) and (b) richness values (±SE) recorded in the 14 urban sampled lakes (Goiânia, Goiás, Brazil) for different zooplankton groups during the rainy (February) and dry (September) seasons of 2023.

A total of 110 zooplankton taxa were identified (Table S1, available at the Data Availability), with 75 recorded during the rainy season and 82 taxa during the dry season. During the rainy season, rotifers were the dominant group (39 taxa; mean value among the lakes = 8.0 taxa), followed by testate amoebae (28 taxa; mean = 5.1 taxa), cladocerans (5 taxa), and copepods (3 taxa). Similarly, in the dry season, rotifers also dominated (43 taxa; mean value among the lakes = 8.5 taxa), followed by testate amoebae (23 taxa; mean = 4.8 taxa), cladocerans (13 taxa), and copepods (3 taxa) (Figure 3b).

In both periods (rainy and dry seasons), the lake with the highest taxa richness values was L1 (Leolídio di Ramos Caiado Park), with 23 and 29 taxa, respectively. In addition, we can also highlight the lake situated in Botanical Garden (L8) during the rainy season, with 19 taxa, and the L9 (Flamboyant Park) during the dry season, with 27 taxa (Figure S1, available at the Data Availability).

Temporal beta diversity (TBI) values between February and September 2023 (rainy and dry seasons, respectively) ranged from 0.459 (L4 - Balneário Park) to 0.782 (L8 – Botanical Garden) (Figure 4). The t-test showed that there was no significant difference between the sampled periods (p = 0.64). A significant loss of abundance-per-species between February and September 2023 was observed only in the lake situated in the Botanical Garden (p = 0.033) (Figure 5; Table S2, available at the Data Availability).

Figure 4
Temporal beta diversity (TBI) values between the rainy (February/23) and dry seasons (September/23) in each of the 14 urban lakes (Goiânia, Goiás, Brazil).

Figure 5
Components of abundances-per-species loss (B) and gains (C) between February and September 2023, for each of the urban lakes. The lakes are represented by black circles, and the same codes are presented in Figure 1 (L1-L14). The line indicates that the loss is equal to the gain. The position of the lakes above the line indicates that, on average, abundances-per-species gain dominated losses from rainy to dry seasons, while the position below the line indicates that losses dominated from rainy to dry seasons.

Considering the different zooplankton groups, no significant differences were observed in the community structure of testate amoebae, rotifers, and microcrustaceans between rainy and dry seasons (p > 0.05). On the other hand, we can highlight the loss of abundance-per-species of rotifers during the dry season in one lake (L7 – Areião Park; p = 0.048) (Table S2, available at the Data Availability).

Additionally, beta regression showed that primary productivity was negatively related to temporal beta diversity considering all zooplanktonic community (Table 3).

Table 3
Results of beta regression models explaining relationships between the temporal beta diversity (TBI) and the environmental variability and primary productivity (mean chlorophyll-a concentration). TBI was calculated for all zooplanktonic community and considering the separate groups (Testate amoebae, Rotifers and Microcrustaceans) in the studied lakes. P = significance level.

	Estimate	z value	P value
Total zooplankton community
Intercept	0.930	5.509	<0.0001
Mean chlorophyll-a concentration	-0.270	-2.983	0.002
Environmental variability	-0.006	-0.231	0.817
Testate amoebae
Intercept	0.335	0.537	0.591
Mean chlorophyll-a concentration	0.337	1.390	0.165
Environmental variability	0.207	0.290	0.772
Rotifera
Intercept	0.601	2.316	0.021
Mean chlorophyll-a concentration	-0.124	-0.877	0.381
Environmental variability	-0.029	-0.744	0.457
Microcrustaceans
Intercept	1.737	2.913	0.036
Mean chlorophyll-a concentration	-0.458	-1.485	0.137
Environmental variability	-0.054	-0.642	0.521

4. Discussion

Biodiversity in urban areas can be negatively impacted by factors such as habitat fragmentation, the introduction of non-native species (e.g., ducks and fish), and various anthropogenic uses (McKinney, 2008; Yang et al., 2022). However, our results revealed a high level of species richness in the sampled lakes, even compared to those observed in natural lakes (Vieira et al., 2007; Lopes et al., 2014). Martins et al. (2024) also find high values of phytoplankton species richness in these same urban lakes. These findings highlight the critical importance of conserving these urban ecosystems.

Among the biological groups assessed, the phylum Rotifera stood out, demonstrating the greatest taxon richness in both the rainy and dry seasons, which is a common pattern for tropical lakes (Neves et al., 2003). Several studies have shown that this group indeed stands out in terms of density and species richness compared to other zooplankton groups, due to its opportunistic nature (r-strategists), short life cycle, parthenogenetic reproduction, high tolerance to environmental variation, and resistance to disturbances (Allan, 1976; Green, 1972; Robertson & Hardy, 1984). The testate amoebae group also showed a high taxa richness in both periods. These taxa may be associated with the sediment of aquatic bodies (Alves et al., 2010), which would explain our results, given our sampling was conducted along the margins of the lakes.

The highest density values were recorded for rotifers and copepods. Copepods exhibit continuous reproduction (Edmondson et al., 1962), and other studies have also reported high densities of juvenile forms (nauplii and copepodites) (Neves et al., 2003; Sampaio & López, 2000). Furthermore, the juvenile and adult forms occupy different trophic niches, meaning there is no competition or niche overlap between them. Most nauplii and early copepodite stages are herbivorous, while the more advanced copepodite stages and adults are predominantly carnivorous (Neves et al., 2003).

Considering the influence of the hydrological regime, we did not observe changes in community structure between the two periods, as indicated by the TBI values, except in one lake (for all zooplankton community). In general, studies conducted in natural lakes associated with floodplains have shown that seasonality significantly influence changes in zooplankton communities, due to substantial variations in both physical and chemical characteristics, as well as in chlorophyll-a concentrations during the rainy and dry seasons (Picapedra et al., 2016; Serafim-Júnior et al., 2019). However, in this study, in artificial urban lakes, we did not observe significant differences in community structure between the seasonal periods, even when we considered the different zooplankton groups (testate amoebae, rotifera and microcrustaceans), with exception of the lake situated at Botanical Garden (L8; considering all zooplankton taxa) and the lake in Areião Park (L7; considering the rotifera group). Probably, the differences in physical and chemical characteristics of these urban lagoons were not large enough to cause changes in the structure of the zooplankton communities between seasonal periods.

Our hypothesis regarding the negative relationship between temporal beta diversity and chlorophyll-a concentration (a proxy for primary productivity) was supported. We found a negative relationship between TBI and chlorophyll-a when analyzing all zooplankton communities. Indeed, some studies report a negative relationship, as highly productive environments (e.g., eutrophic systems) may promote biological homogeneity (Zorzal-Almeida et al., 2017). In this context, eutrophication acts as an environmental filter, selecting species that are more tolerant to such conditions (Chase, 2007; Vilar et al., 2014; Silva et al., 2020). This probably explains the results observed in the urban lakes studied.

Our second hypothesis about the relationship between environmental variability and temporal beta diversity was not corroborated. According to some studies, greater changes in species composition are expected in more heterogeneous environments (Zorzal-Almeida et al., 2017; Pereira et al., 2024). However, the absence of a positive relationship between beta diversity and environmental variation has also been reported in other studies (Heino et al., 2013; Bini et al., 2014). A possible explanation for our results is the small difference between the rainy and dry periods, considering the abiotic variables analyzed. In this way, the limnological changes observed in the urban lakes were not substantial enough to modify the structure of the zooplankton communities. Indeed, based on the limnological variables analyzed, we did not detect a clear distinction between the rainy and dry seasons. In addition, the absence of a relationship between beta biodiversity and environmental variability can also be explained by the lack of some variable that are important for the zooplankton community (Zorzal-Almeida et al., 2017). Heino et al. (2013) suggested in their study that a more plausible explanation for the lack of a relationship between environmental heterogeneity and beta diversity lies in the combined influence of species-specific responses to environmental conditions and stochastic variation in species distributions that occur independently of those conditions. Only long-term monitoring will be able to determine whether this is indeed a pattern observed in these urban lakes.

In summary, our study demonstrated that the urban lakes surveyed exhibited high taxonomic richness but showed little temporal variation in community structure when comparing the rainy and dry seasons. This finding is significant given the current knowledge gap concerning the biodiversity of urban lakes. Our hypothesis regarding the negative relationship between beta diversity and chlorophyll-a concentrations (proxy of primary productivity) was supported, which may be valuable for future management initiatives in the region. Therefore, we suggest that future studies include additional variables to improve the understanding of environmental variability in these systems, such as: (i) depth measurements, which may help detect a potential disturbance during the dry period that could influence zooplankton communities; and (ii) the presence and density of aquatic macrophytes, commonly used as proxies for habitat heterogeneity, as they increase niche availability and contribute to the structuring of aquatic communities (Thomaz & Cunha, 2010). Finally, we also recommend that future studies expand the temporal scale.

Acknowledgements

This paper is supported by FAPEG (Proc. 202310267000229 / Chamada 03/2022 - Integridade ambiental em lagos urbanos e em áreas adjacentes: a biodiversidade como ferramenta de avaliação), and Instituto Nacional de Ciência e Tecnologia (INCT) in Ecology, Evolution and Biodiversity Conservation, supported by MCTIC/CNPq (proc. 465610/2014-5). Natália S. Pereira received a scholarship from CNPq (PIBIC). Danielle G. Pereira and Sarah S. Maia received a scholarship from INCT (Ecology, Evolution and Biodiversity Conservation). Nowadays, D.G. Pereira are receiving scholarships from TWRA (Tropical Water Research Alliance). Ligia P.B. Mesquita received a scholarship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). We are also grateful to Anny Kelly Nascimento Oliveira for the creation of the map presented in this study.

Data availability

Part of the data used in this study is available in the supplementary material deposited in the SciELO Data repository, accessible at https://doi.org/10.48331/SCIELODATA.ZEBNAK. Additional data not available in the repository may be provided by the authors upon request.

• Cite as:
  Pereira, N.S., et al. Primary productivity drives temporal beta diversity of zooplankton communities in urban lakes. Acta Limnologica Brasiliensia, 2026, vol. 38, e19. https://doi.org/10.1590/S2179-975X6125

References

• Allan, J.D., 1976. Life history patterns in zooplankton. Am. Nat., 110(971), 165-180. https://doi.org/10.1086/283056
  » https://doi.org/10.1086/283056
• Alves, G.M., Velho, L.F.M., Simões, N.R., & Lansac-Tôha, F.A., 2010. Biodiversity of testate amoebae (Arcellinida and Euglyphida) in different habitats of a lake in the Upper Paraná River floodplain. Eur. J. Protistol., 46(4), 310-318. PMid:20869856. https://doi.org/10.1016/j.ejop.2010.07.001
  » https://doi.org/10.1016/j.ejop.2010.07.001
• Anderson, M.J., 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26(1), 32-46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x
  » https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x
• Anderson, M.J., Crist, T.O., Chase, J.M., Vellend, M., Inouye, B.D., Freestone, A.L., Sanders, N.J., Cornell, H.V., Comita, L.S., Davies, K.F., Harrison, S.P., Kraft, N.J., Stegen, J.C., & Swenson, N.G., 2011. Navigating the multiple meanings of β diversity: A roadmap for the practicing ecologist. Ecol. Lett., 14(1), 19-28. PMid:21070562. https://doi.org/10.1111/j.1461-0248.2010.01552.x
  » https://doi.org/10.1111/j.1461-0248.2010.01552.x
• APHA, 2005. Standard methods for the examination of water and wasterwater. Washington: Byrd Prepress Springfield, 21st ed.
• Araújo, A.P., & Nogueira, E.M.S., 2016. Zooplâncton como bioindicador das águas do reservatório natural do povoado Olhos d’água do Souza, Glória, Bahia, Brasil. Rev. Ouricuri., 6(2), 001-016. https://doi.org/10.59360/ouricuri.vol6.i2.a3168
  » https://doi.org/10.59360/ouricuri.vol6.i2.a3168
• Baselga, A., 2010. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr., 19(1), 134-143. https://doi.org/10.1111/j.1466-8238.2009.00490.x
  » https://doi.org/10.1111/j.1466-8238.2009.00490.x
• Baselga, A., 2012. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob. Ecol. Biogeogr., 21(12), 1223-1232. https://doi.org/10.1111/j.1466-8238.2011.00756.x
  » https://doi.org/10.1111/j.1466-8238.2011.00756.x
• Bini, L.M., Landeiro, V.L., Padial, A.A., Siqueira, T., & Heino, J., 2014. Nutrient enrichment is related to two facets of beta diversity for stream invertebrates across the United States. Ecology 95(6), 1569-1578. PMid:25039221. https://doi.org/10.1890/13-0656.1
  » https://doi.org/10.1890/13-0656.1
• Bonecker, C.C., Simões, N.R., Minte-Vera, C.V., Lansac-Tôha, F.A., Velho, L.F.M., & Agostinho, A.A., 2013. Temporal changes in zooplankton species diversity in response to environmental changes in an alluvial valley. Limnologica 43(2), 114-121. https://doi.org/10.1016/j.limno.2012.07.007
  » https://doi.org/10.1016/j.limno.2012.07.007
• Bottrell, H.H., Duncan, A., Gliwicz, Z.M., Grygierek, E., Herzig, A., Hillbricht-Ilkowska, A., Kurasawa, H., Larsson, P., & Weglenska, T., 1976. A review of some problems in zooplankton production studies. Nor. J. Zool., 24, 419-456.
• Chase, J.M., 2007. Drought mediates the importance of stochastic community assembly. Proc. Natl. Acad. Sci. USA 104(44), 17430-17434. PMid:17942690. https://doi.org/10.1073/pnas.0704350104
  » https://doi.org/10.1073/pnas.0704350104
• Chase, J.M., 2010. Stochastic community assembly causes higher biodiversity in more productive environments. Science 328(5984), 1388-1391. PMid:20508088. https://doi.org/10.1126/science.1187820
  » https://doi.org/10.1126/science.1187820
• Choi, J.Y., Kim, J.C., & Kim, S.K., 2020. Changing distributions of zooplankton communities in a coastal lagoon in response to rainfall seasonality. J. Coast. Res., 102(sp1), 69-74. https://doi.org/10.2112/SI102-009.1
  » https://doi.org/10.2112/SI102-009.1
• Costa, H.C., Marcuzzo, F.F.N., Ferreira, O.M., & Andrade, L.R., 2012. Espacialização e sazonalidade da precipitação pluviométrica do Estado de Goiás e Distrito Federal. Rev. Bras. Geogr. Fis., 5(1), 87-100. https://doi.org/10.26848/rbgf.v5i1.232785
  » https://doi.org/10.26848/rbgf.v5i1.232785
• Cribari-Neto, F., & Zeileis, A., 2010. Beta Regression in R. J. Stat. Softw., 34(2), 1-24. https://doi.org/10.18637/jss.v034.i02
  » https://doi.org/10.18637/jss.v034.i02
• Dray, S., Bauman, D., Blanchet, G., Borcard, D., Clappe, S., Guénard, G., Jombart, T., Larocque, G., Legendre, P., Madi, N., Wagner, H.H., Siberchicot, A., & Chambers, J., 2019. adespatial: Multivariate multiscale spatial analysis. R package version 0.3-3 [online]. Retrieved in 2025, August 12, from https://cran.r-project.org/package=adespatial
  » https://cran.r-project.org/package=adespatial
• Edmondson, W.T., Comita, G.W., & Anderson, G.C., 1962. Reproductive rate of copepods in nature and its relation to phytoplankton population. Ecology 43(4), 625-634. https://doi.org/10.2307/1933452
  » https://doi.org/10.2307/1933452
• Geminiano, M.M., Soares, A., & Pinto, A.L., 2021. Influência do uso da terra nos parâmetros da qualidade das águas superficiais do monumento das lagoas urbanas da cidade de Três Lagoas/MS no inverno de 2019. Geosul 36(78), 558-581. https://doi.org/10.5007/2177-5230.2021.e71717
  » https://doi.org/10.5007/2177-5230.2021.e71717
• Golterman, H.L., Clymo, R.S., & Ohnstad, M.A.M., 1978. Methods for physical and chemical analysis of freshwater. Oxford: Blackwell Scientific Publications, 214 p.
• Green, J., 1972. Freshwater ecology in the Mato Grosso, Central Brazil: III. Associations of Rotifera in meander lakes of the Rio Suiá Missú. J. Nat. Hist., 6(2), 229-241. https://doi.org/10.1080/00222937200770221
  » https://doi.org/10.1080/00222937200770221
• Hasan, S.S., Zhen, L., Miah, M.G., Ahamed, T., & Samie, A., 2020. Impact of land use change on ecosystem services: a review. Environ. Dev., 34, 100527. https://doi.org/10.1016/j.envdev.2020.100527
  » https://doi.org/10.1016/j.envdev.2020.100527
• Heino, J., Grönroos, M., Ilmonen, J., Karhu, T., Niva, M., & Paasivirta, L., 2013. Environmental heterogeneity and β diversity of stream macroinvertebrate communities at intermediate spatial scales. Freshw. Sci., 32(1), 142-154. https://doi.org/10.1899/12-083.1
  » https://doi.org/10.1899/12-083.1
• Jackson, D.A., 1993. Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches. Ecology 74(8), 2201-2214. https://doi.org/10.2307/1939574
  » https://doi.org/10.2307/1939574
• Legendre, P., 2019. A temporal beta‐diversity index to identify sites that have changed in exceptional ways in space–time surveys. Ecol. Evol., 9(6), 3500-3514. PMid:30962908. https://doi.org/10.1002/ece3.4984
  » https://doi.org/10.1002/ece3.4984
• Legendre, P., & Gauthier, O., 2014. 2014. Statistical methods for temporal and space–time analysis of community composition data. Proc. Biol. Sci., 281(1778), 20132728. PMid:24430848. https://doi.org/10.1098/rspb.2013.2728
  » https://doi.org/10.1098/rspb.2013.2728
• Legendre, P., & Legendre, L., 2012. Numerical ecology. Amsterdam: Elsevier, vol. 24.
• Li, F., Liu, X., Zhang, X., Zhao, D., Liu, H., Zhou, C., & Wang, R., 2017. Urban ecological infrastructure: an integrated network for ecosystem services and sustainable urban systems. J. Clean. Prod., 163, S12-S18. https://doi.org/10.1016/j.jclepro.2016.02.079
  » https://doi.org/10.1016/j.jclepro.2016.02.079
• Lindholm, M., Alahuhta, J., Heino, J., & Toivonen, H., 2020. Temporal beta diversity of lake plants is determined by concomitant changes in environmental factors across decades. J. Ecol., 109(2), 819-832. https://doi.org/10.1111/1365-2745.13508
  » https://doi.org/10.1111/1365-2745.13508
• Lopes, P.M., Bini, L.M., Declerck, S.A., Farjalla, V.F., Vieira, L.C., Bonecker, C.C., Lansac-Tôha, F.A., Esteves, F.A., & Bozelli, R.L., 2014. Correlates of Zooplankton Beta Diversity in Tropical Lake Systems. PLoS One 9(10), e109581. PMid:25330034. https://doi.org/10.1371/journal.pone.0109581
  » https://doi.org/10.1371/journal.pone.0109581
• Martins, M.P.M., Soares, K.W.D.S., Carvalho, P., & Bortolini, J.C., 2024. Phytoplankton community diversity, dominance, and rarity: a case study of tropical urban lakes. Acta Limnol. Bras., 36, e1. https://doi.org/10.1590/s2179-975x6123
  » https://doi.org/10.1590/s2179-975x6123
• McKinney, M.L., 2008. Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst., 11(2), 161-176. https://doi.org/10.1007/s11252-007-0045-4
  » https://doi.org/10.1007/s11252-007-0045-4
• Neves, I.F., Rocha, O., Roche, K.F., & Pinto, A.A., 2003. Zooplankton community structure of two marginal lakes of the river Cuiabá (Mato Grosso, Brazil) with analysis of Rotifera and Cladocera diversity. Braz. J. Biol., 63(2), 329-343. PMid:14509855. https://doi.org/10.1590/S1519-69842003000200018
  » https://doi.org/10.1590/S1519-69842003000200018
• Oertli, B., & Parris, K.M., 2019. Review: toward management of urban ponds for freshwater biodiversity. Ecosphere 10(7), e02810. https://doi.org/10.1002/ecs2.2810
  » https://doi.org/10.1002/ecs2.2810
• Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., & Wagner, H., 2022. Package “vegan.” Vienna: The Comprehensive R Archive Network.
• Pereira, A.L.A., Carvalho, P., Granzotti, R.V., Vieira, L.C.G., & Bini, L.M., 2024. Temporal beta diversity increases with environmental variability in zooplankton floodplain communities. Limnology 25(1), 1-10. PMid:39840382. https://doi.org/10.1007/s10201-023-00724-7
  » https://doi.org/10.1007/s10201-023-00724-7
• Picapedra, P.H.S., Fernandes, C., & Lansac-Tôha, F.A., 2016. Zooplankton community in the Upper Parnaíba River (Northeastern, Brazil). Braz. J. Biol., 77(2), 402-412. PMid:27533733. https://doi.org/10.1590/1519-6984.20215
  » https://doi.org/10.1590/1519-6984.20215
• R Core Team, 2024. R: A Language and Environment for Statistical Computing [online]. Retrieved in 2025, August 12, from https://www.r-project.org/
  » https://www.r-project.org/
• Robertson, B.A., & Hardy, E.R., 1984. Zooplankton of Amazonian lakes and rivers. In Sioli, H., ed. The Amazon: Limnology and landscape ecology of a mighty tropical river and its basin. Dordrecht: Springer Netherlands, 337-352. https://doi.org/10.1007/978-94-009-6542-3_13
  » https://doi.org/10.1007/978-94-009-6542-3_13
• Sampaio, E.V., & López, C.M., 2000. Zooplankton community composition and some limnological aspects of an oxbow lake of the Paraopeba River, São Francisco River Basin, Minas Gerais, Brazil. Braz. Arch. Biol. Technol., 43(3), 285-293. https://doi.org/10.1590/S1516-89132000000300007
  » https://doi.org/10.1590/S1516-89132000000300007
• Santos, J.F.D., Nascimento, J.R., & Simões, N.R., 2025. Effects of urban pollution on zooplankton diversity along the Almada River (Bahia, Brazil). Acta Limnol. Bras., 37, e103. https://doi.org/10.1590/s2179-975x3224
  » https://doi.org/10.1590/s2179-975x3224
• Serafim-Júnior, M., Perbiche-Neves, G., & Lansac-Tôha, F.A., 2019. An assessment of the factors determining rotifer assemblage in river-lake systems: the effects of seasonality and habitat. Zoologia 36, e24191. https://doi.org/10.3897/zoologia.36.e24191
  » https://doi.org/10.3897/zoologia.36.e24191
• Shimadzu, H., Dornelas, M., & Magurran, A.E., 2015. Measuring temporal turnover in ecological communities. Methods Ecol. Evol., 6(12), 1384-1394. https://doi.org/10.1111/2041-210X.12438
  » https://doi.org/10.1111/2041-210X.12438
• Silva, C.O., Rangel Junior, A., Perbiche-Neves, G., Pinheiro, A.P., & Lacerda, S.R., 2020. Low zooplankton richness indicating adverse drought and eutrophication conditions in a reservoir in northeastern Brazil. Iheringia Ser. Zool., 110, e2020009. https://doi.org/10.1590/1678-4766e2020009
  » https://doi.org/10.1590/1678-4766e2020009
• Simões, N.R., Dias, J.D., Meerhoff, M., Lansac-Tôha, F.A., Bini, L.M., & Bonecker, C.C., 2022. Drivers of zooplankton beta diversity in natural shallow lakes and artificial reservoirs in the Neotropics. Hydrobiologia 849(17), 3705-3717. https://doi.org/10.1007/s10750-022-04825-9
  » https://doi.org/10.1007/s10750-022-04825-9
• Teurlincx, S., Kuiper, J.J., Hoevenaar, E.C., Lurling, M., Brederveld, R.J., Veraart, A.J., Janssen, A.B.G., Mooij, W.M., & de Senerpont Domis, L.N., 2019. Towards restoring urban waters: understanding the main pressures. Curr. Opin. Environ. Sustain., 36, 49-58. https://doi.org/10.1016/j.cosust.2018.10.011
  » https://doi.org/10.1016/j.cosust.2018.10.011
• Thomaz, S.M., & Cunha, E.R.D., 2010. The role of macrophytes in habitat structuring in aquatic ecosystems: methods of measurement, causes and consequences on animal assemblages’ composition and biodiversity. Acta Limnol. Bras., 22(2), 218-236. https://doi.org/10.4322/actalb.02202011
  » https://doi.org/10.4322/actalb.02202011
• Vale, C.G., Arenas, F., Barreiro, R., & Piñeiro‐Corbeira, C., 2021. Understanding the local drivers of beta‐diversity patterns under climate change: The case of seaweed communities in Galicia, North West of the Iberian Peninsula. Divers. Distrib., 27(9), 1696-1705. https://doi.org/10.1111/ddi.13361
  » https://doi.org/10.1111/ddi.13361
• Vieira, L.C.G., Bini, L.M., Velho, L.F.M., & Mazao, G.R., 2007. Influence of spatial complexity on the density and diversity of periphytic rotifers, microcrustaceans and testate amoebae. Fundam. Appl. Limnol., 170(1), 77-85. https://doi.org/10.1127/1863-9135/2007/0170-0077
  » https://doi.org/10.1127/1863-9135/2007/0170-0077
• Vilar, A.G., van Dam, H., van Loon, E.E., Vonk, J.A., van Der Geest, H.G., & Admiraal, W., 2014. Eutrophication decreases distance decay of similarity in diatom communities. Freshw. Biol., 59(7), 1522-1531. https://doi.org/10.1111/fwb.12363
  » https://doi.org/10.1111/fwb.12363
• Yang, Y., Chen, H., Mamun Abdullah Al, M.A., Ndayishimiye, J.C., Yang, J.R., Isabwe, A., Luo, A., & Yang, J., 2022. Urbanization reduces resource use efficiency of phytoplankton community by altering the environment and decreasing biodiversity. J. Environ. Sci., 112, 140-151. PMid:34955197. https://doi.org/10.1016/j.jes.2021.05.001
  » https://doi.org/10.1016/j.jes.2021.05.001
• Zeileis, A., Cribari-Neto, F., Gruen, B., Kosmidis, I., Simas, A.B., & Rocha, A.V., 2013. Betareg: Beta Regression [online]. Retrieved in 2025, August 12, from https://cran.r-project.org/web/packages/betareg/index.html
  » https://cran.r-project.org/web/packages/betareg/index.html
• Zorzal-Almeida, S., Bini, L.M., & Bicudo, D.C., 2017. Beta diversity of diatoms is driven by environmental heterogeneity, spatial extent and productivity. Hydrobiologia 800(1), 7-16. https://doi.org/10.1007/s10750-017-3117-3
  » https://doi.org/10.1007/s10750-017-3117-3

Edited by

• Associate Editor:
  Danielle Katharine Petsch

Publication Dates

• Publication in this collection
  27 July 2026
• Date of issue
  2026

History

• Received
  12 Aug 2025
• Accepted
  13 May 2026