https://orcid.org/0000-0002-0291-2252
Figures
Abstract
Toxic cyanobacteria are widely present in water bodies worldwide, particularly in reservoirs. The Calima Reservoir is primarily used for energy generation, but some areas of it are also designated for recreational activities, making it a significant contributor to regional and national tourism. In this study, four sampling events were conducted in 2023, covering different seasonal periods. During these samplings, various species of potentially toxic cyanobacteria were detected; the presence of these cyanobacteria was analyzed in relation to several physicochemical variables, and microcystin‑LR (MC-LR) was detected in four of the six sampling stations, although in concentrations below the maximum permissible limit. The reservoir’s temperature showed a positive correlation with cyanobacterial abundance. One of the objectives of the research was to assess the growth of M. aeruginosa by exposing it to water samples from four tributaries that flow directly into the reservoir, each with distinct physicochemical properties. The highest growth induction, exceeding 400%, was observed in samples from Tributary 2 (T2), located at the center of the reservoir. The analysis revealed that the variables most significantly associated with enhanced M. aeruginosa proliferation were ammoniacal nitrogen (32.8 mg NH3-N/L), alkalinity (280 mg CaCO3), and Kjeldahl nitrogen (33.6 mg N/L). In contrast, negative associations were observed with indicators of biodegradable organic matter, including of biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC) and coliform-type microorganisms, indicating an environment conducive to the proliferation of M. aeruginosa. These findings suggest that it is important to conduct detailed analyses of tributary waters to identify areas with high potential for cyanobacterial blooms. These findings highlight the influence of external nutrient inputs on bloom formation and the need for focused mitigation to protect water quality.
Citation: Herrera N, Llano S, Peñuela G (2026) Influence of environmental factors and tributaries on toxic cyanobacterial growth. PLoS One 21(3): e0340012. https://doi.org/10.1371/journal.pone.0340012
Editor: Barathan Balaji Prasath, Gujarat Institute of Desert Ecology, INDIA
Received: July 11, 2025; Accepted: December 15, 2025; Published: March 13, 2026
Copyright: © 2026 Herrera et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
The increase in nutrients in lakes and reservoirs, known as eutrophication, has become one of the most important problems for the sustainability of water resources at a global level. Eutrophication is aggravated by the incorporation of chemical pollutants generated by human activities in industrial plants and agricultural fields. The high availability of nutrients benefits the proliferation of some groups of organisms, among which cyanobacteria stand out [1].
Cyanobacteria are Gram-negative, photosynthetic prokaryotes that are very diverse in morphology, physiology and metabolism. They have great adaptability, and therefore occupy diverse aquatic environments, often forming excessive proliferations called cyanobacterial blooms that affect the sustainability of ecosystems, due to their potential toxicity, extremely high biomass levels and the hypoxic conditions that develop during their death and decay [2].
Toxins produced by cyanobacteria (cyanotoxins) pose a risk to human and animal health and can cause illness and mortality at environmental concentrations [3]. Cyanobacteria can also proliferate in water treatment plants and generate undesirable organoleptic characteristics in water, which are difficult to remove. Removing cyanobacteria and cyanotoxins from aquatic systems involves increased costs of water treatment and infrastructure maintenance [4]
Efficient removal of cyanobacteria and cyanotoxins is very complex and poses serious challenges that can only be addressed through a deep understanding of the growth dynamics and ecophysiology of toxic cyanobacteria cells and populations [5].
The discharges that fall into water bodies frequently increase the concentration of nutrients and cause cyanobacteria to proliferate until blooms form. Pollution could have profound impacts on the composition of microorganisms and the function of the community. Often, bacteria, particularly cyanobacteria, are dominant in contaminated sediments, indicating that they have a strong resistance to contamination [6].
The Calima Reservoir, located in the department of Valle del Cauca, Colombia, is one of the region’s most vital water resources. In recent years, the presence of cyanobacterial blooms has become increasingly evident in this reservoir. This study aimed to analyze the occurrence of these blooms over four periods in 2023, assessing the influence of various physicochemical and environmental variables. Additionally, the growth of Microcystis aeruginosa cultures was evaluated by exposing them to water samples from four tributaries. The findings of this research could contribute to the development of effective management strategies to mitigate this issue.
2. Materials and methods
2.1. Sampling site
The Calima Reservoir is located in a tropical climate zone, at an elevation of 1.4 km above sea level, near the towns of Calima El Darién, Restrepo, and Yotoco. It is one of Colombia’s most significant ecosystems due to its size, importance for tourism, and role in energy production for the Valle del Cauca region. This artificial reservoir was created in 1967 by flooding an area of 19.34 km2, resulting in a total stored volume of 0.581 km3.
Currently, the reservoir has a surface area of 19.34 km2, a length of 13 km, and a width of 1.5 km. It has a maximum volume of 0.529 km3 and a minimum volume of 0.118 km3, the initial volume refers to the design or post-construction capacity, which has subsequently decreased due to factors such as sedimentation. Rainfall occurs throughout the year, with October being the wettest month and March the warmest. Fig 1 shows the reservoir and the six selected sampling points used to observe water quality variations and overall reservoir behavior. The six sampling sites were selected along the reservoir to monitor the behavior of the water body and changes in its quality. The dam site (E6), the central site (E5), and the tail site (E1) were essential as they provided an overall view of the reservoir’s inflow, retention and outflow, and changes in the ecosystem’s physical, chemical, microbiological, and hydrobiological characteristics. The remaining three sites were chosen based on pollution load criteria, such as nearby wastewater discharges (PTAR exit, E2), previous cyanobacterial bloom events, and high total coliform levels (E3, E4, and E5). Table 1 provides the specific locations of these sampling points. Additionally, the experiment included four types of discharge water, each tested at three concentrations, plus one control, with three replicates.
The base map was created by the author using Landsat 8/9 satellite imagery (Collection 2, Level-2) accessed through the USGS Earth Resources Observation and Science (EROS) Center (https://eros.usgs.gov/). These USGS products are in the public domain and are therefore fully compatible with the CC BY 4.0 license.
2.2. Sample collection and identification of cyanobacteria
To gain a deeper understanding of cyanobacterial blooms in the Calima Reservoir, four sampling events (campaigns) were conducted in 2023, specifically, two samplings were carried out in the dry season (February and April) and two in the rainy season (July and September). During each campaign, three water samples were collected from distinct depths: the surface (P1), mid-depth (50% of the photic zone limit) (P2), and the photic zone limit (P3). The mean depth was defined as 50% of the photic zone limit, where the latter corresponds to the depth at which the downwelling irradiance is reduced to 1% of the surface incident light. This limit was estimated from the Secchi disk depth by applying an conversion factor of 2.7, which provides an approximation of the 1% light penetration depth in the water column. Samples were stored in 50 mL plastic containers. In total, 72 samples were collected across the four time points. Additionally, at each site, a drag sample was obtained using a phytoplankton net (Biológika, Colombia) with a pore diameter of 25 μm, towed at approximately 3 m/s. This process consisted of filtering water for 5 minutes to concentrate the surface phytoplankton and performing a qualitative analysis of cyanobacteria and microalgae present in the reservoir. The collected material was partly analyzed directly under a microscope, while the remaining portion was fixed in situ with Lugol’s solution. It was refrigerated at a temperature between 0–6°C until the respective microscopic analyses were carried out.
The analyzed discharges included four sources: (T1) the wastewater treatment plant (PTAR) discharge output, (T2) Majagua, (T3) the wastewater inlet to PTAR, and (T4) Comfandi. These discharge samples represent the tributary inputs to the reservoir. Each sample was collected in 1-liter dark plastic bottles and transported to the laboratory under refrigeration for cyanobacteria growth induction tests. The tributary samples were taken in triplicate. Samples from tributaries 1 and 2 were collected during season 1 in February, and those from tributaries 3 and 4 during season 4 in September.
2.3. Microscopic analysis
The observations were made by taking 30 µL of each sample under an optical microscope, using wet preparations and preparations with India ink. A trinocular phase contrast optical microscope model Eclipse E200 (Nikon, USA) was used to count the number of cyanobacteria cells and Microcystis spp. colonies. In addition, the IC Measure program (The Imaging Source, USA) was used to facilitate visualization through the USB connection of a 14MP Z14-HD camera (Nikon, USA) attached to the microscope. During the qualitative analysis, the morphological and morphometric characteristics of the species were observed. India ink was used when necessary to visualize the mucilage in colonial forms. Taxonomic identification at the intrageneric level was conducted using specialized bibliographic references for each cyanobacterial group, Microcystis spp. were identified based on morphological traits observed by optical microscopy, the presence of mucilage, and the application of standard taxonomic keys, following the classification system proposed by Hoffmann et al, 2005 [7]. While this approach provides reliable genus-level identification, that phenotypic variability within Microcystis can complicate species discrimination, making molecular markers or fluorescence-based methods necessary for unequivocal confirmation. Other phytoplankton genera were identified using taxonomic keys [8].
2.4. Cyanobacteria counts
A trinocular phase contrast microscope (Eclipse E200, Nikon, USA) was used to count cyanobacterial cells and colonies. Visualization was performed using the IC Measure program (The Imaging Source, USA) with a 14MP Z14-HD camera (Nikon, USA) attached to the microscope. Cell counting was conducted using a Neubauer chamber (Boeco, Germany), which has two fields, allowing for simultaneous duplicate counts. Three quadrants from each field, each containing 16 squares, were randomly selected. Each sample was thoroughly homogenized, and 30 µL were used for counting. To calculate cell density per mL, the mean value obtained was multiplied by the chamber’s dilution factor (5,000). For some highly concentrated samples, it was necessary to perform dilution by transferring 20 µL of sample into a 2 mL Eppendorf tube and bringing to volume with distilled water. Observations were made at total magnifications of 400X for cell counts and 100X for colony counts of the genus Microcystis. Each sample, including those from different depths and discharge sources, was thoroughly homogenized, and 30 µL was used for analysis. To determine cell density per mL, the average count was multiplied by the chamber’s dilution factor. The cell counts of Microcystis spp. were performed in triplicate under optical microscopy to minimize counting errors and ensure reproducibility of quantitative data.
2.5. Physicochemical and environmental analysis
In-situ measurements of water temperature, pH, oxidation-reduction potential (ORP), dissolved oxygen, and turbidity were conducted using a portable HQ2200 multiparameter device (Hach, USA). Physicochemical variables were analyzed in surface water samples from six locations, as well as at three different depths per location and in discharge samples. Water transparency was determined using a 20 cm diameter Secchi disk.
Additionally, laboratory analyses were performed to assess various physicochemical parameters, including electrical conductivity, orthophosphates, total phosphorus, total alkalinity, total hardness, ammonia nitrogen, total Kjeldahl nitrogen, nitrates, nitrites, and total organic carbon (TOC). Microbiological variables, such as heterotrophic/mesophilic bacteria, Escherichia coli, and total coliforms, were also evaluated to characterize the water matrix. The specific methods used for these analyses are detailed in Table 2.
2.6. Microcystis aeruginosa cultures and reagents
The M. aeruginosa cultures used for the growth induction tests were provided by the Organic Chemistry of Natural Products research group at the University of Antioquia, Medellín, Colombia. This strain was originally isolated from the Riogrande II Reservoir (Antioquia, Colombia) and maintained under controlled laboratory conditions. Cultures were grown in BG11 medium at 27°C, with constant aeration and a 12-hour light/12-hour dark photoperiod. Light intensity was set to 930 lx, and all cultures were maintained in closed systems. Aeration was provided using a Resun LP-60 air pump (4,200 L/h) connected to an air distribution system. This setup ensured uniform mixing and oxygenation of the cultures. The aeration rate corresponded to 1 L air per minute per culture. Dissolved oxygen concentration in the culture medium was monitored and maintained above 102 mg/L using a multiparameter SI Analytics (HandyLab 680 FK, Germany). Ventilation intensity was adjusted to maintain constant bubbling.
2.7. Effect of tributaries on cyanobacteria growth
Approximately 10 mL of M. aeruginosa stock cultures were mixed with 200 mL of discharge samples prepared at three different concentrations, along with a control: (1) 25%: 50 mL of discharge water + 150 mL of BG11 culture medium; (2) 50%: 100 mL of discharge water + 100 mL of BG11 culture medium; (3) 100%: 200 mL of discharge water only; Control: 200 mL of BG11 culture medium.
The discharge samples were not filtered. After 12 days, samples were collected for cell counts and microscopic observations, and cell densities were compared. In total, there were three treatments, four discharge types, and one control, each with replicates, resulting in 48 cultures.
The M. aeruginosa growth tests were conducted in a laboratory room with shelves, under the following controlled conditions: a 12-hour light/12-hour dark photoperiod, artificial lighting at 930 lx, temperature of 25°C, and constant aeration.
2.8. Cyanotoxin analysis
An ACQUITY UPLC H-Class liquid chromatograph coupled to a Xevo TQD triple quadrupole mass spectrometer (UPLC/MS/MS) (Waters, USA) was used for analysis. Each of the 72 water samples, previously filtered and contained in 50 mL volumetric flasks, was processed under vacuum through an Oasis HLB (Hydrophilic Lipophilic Balance) extraction cartridge (60 mg/3 mL, Waters, USA), conditioned at a flow rate of 5 mL/min with methanol and water in varying proportions. The analytes were then eluted with 10 mL of HPLC-grade methanol into 25 mL vials. Subsequently, the sample was dried under a gentle flow of air for approximately two hours under constant observation until a final volume of approximately 1 mL was obtained. For identification and quantification, 30 μL of the concentrated sample was injected into the instrument.
Separation of analytes was performed using a Kinetex C18 separation column with a 1.7 μm particle size and dimensions of 2.1 mm × 50 mm. The aqueous mobile phase consisted of a water-methanol mixture (95:5) with 5 mM ammonium formate, while the organic mobile phase consisted of methanol-water (95:5) with 5 mM ammonium formate. The mass spectrometer was operated with a source temperature of 150°C, a desolvation temperature of 350°C, a desolvation gas flow (N₂) of 650 L/h, and a cone gas flow (N₂) of 50 L/h. Ionization was performed in positive electrospray mode (ESI +), with a capillary voltage of 3.5 kV.
The certified cyanotoxin standards (MC-LR, MC-RR, MC-YR, and NOD) used in this study had a purity greater than 95% and were obtained from Eurofins Abraxis (Warminster, USA).
2.9. Statistical analysis
To establish statistically significant differences between the cell density measurements obtained in the tests with the samples of the tributaries, an ANOVA was applied with a significance level of 0.05. In cases where statistically significant differences were found, the Tukey test was performed to establish differences between the discharges evaluated. The R Core Team statistical package was used. p < 0.05 was considered significant. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Descriptive and inferential statistical processing was performed. All statistical tests were run in the RStudio R 4.2.3 application. In addition, a correlation analysis was performed to explore the data and identify possible relationships between variables, and a principal component analysis (PCA) was performed to reduce the dimensionality of the set of variables and thus simplify the interpretations of the relationships between them. Finally, a descriptive analysis was performed and a multiple regression model was carried out to search for causality between the physicochemical variables and the abundance of cyanobacteria.
3. Results and discussion
3.1. Dynamics of potentially toxic species in the Calima reservoir
The samples obtained during the four campaigns corresponded to the dry season during the months of February and April and the wet season during the months of July and September. The results regarding the number of cyanobacteria per mL only revealed significant variations in the density of cyanobacteria in Campaign 2, carried out in April 2023. The behavior and appearance of potentially toxic cyanobacteria in the reservoir during the four sampling periods can be seen in Fig 2, where the dynamics of cyanobacteria are schematized, representing each of the four sampling campaigns in a different color. The highest cyanobacterial densities (7,29x10⁶ cells/mL) occurred during Campaign 2 (April), with a proportion of cyanobacteria of 89%. For the other campaigns the proportions were very different, with proportions of 7%, 3% and 1% in Campaigns 3, 1 and 4, respectively (Table A and Fig A in S1 File).
E1: Tail, E2: PTAR exit, E3: Entrance 4-5, E4: Perto Buga, E5: Center, E6: Dam. Feb: Campaign 1, April: Campaign 2, July: Campaign 3 and Sep: Campaign 4.
With respect to the six sampling stations, the lowest concentration of cyanobacteria was detected during the four sampling periods in the dam point (E6) (Fig 2). In this station, presence of cyanobacteria was only found in Campaign 2, in which 366 cells/mL were visualized.
The highest concentrations of cyanobacteria were observed during Campaign 2, in April 2023, which corresponds to the dry season. When discriminated by sampling station, it was observed that the PTAR exit station (E2) presented the highest cell density (8.70X107 cells/mL), followed by the tail station (E1) (1.30X107 cells/mL) and Puerto Buga (E4) (1.28X107 cells/mL).
In July, with the beginning of the rainy season, the reservoir level increased, and significant changes were recorded in the composition and biomass of cyanobacteria, with a decrease in these being observed. The same occurred for September, during which the rainy season continues.
According to the results obtained from the observation of cyanobacteria colonies in the samples by season and sampling point, dominance of the species M. aeruginosa is observed in the dry season, at all sampling points (Fig 3). Moreover, colonies of the species M. flos-aquae, M. wesenberguii and the genus Aphanocapsa were also detected.
E1: Tail, E2: PTAR exit, E3: Entrance 4-5, E4: Perto Buga, E5: Center, E6: Dam.
In April 2023, a dry season was recorded during which M. aeruginosa predominated, with a number of colonies of approximately 180/mL at the PTAR station, followed by the species M. wesenbergii (Fig 3).
3.2. Proportion of toxic cyanobacteria to phytoplankton
The quantification of the proportion of potentially toxic cyanobacteria relative to total phytoplankton in each sampling campaign showed that the second campaign had the highest incidence of cyanobacteria, as shown in Fig 4. The proportion is further broken down by the three sampled depths (P1, P2, and P3).
Three sampled depths (P1 = surface, P2 = mid-depth (50% of the photic zone limit), and P3 = photic zone limit). E1: Tail, E2: PTAR exit, E3: Entrance 4-5, E4: Perto Buga, E5: Center, E6: Dam.
At the tail station (E1), cyanobacteria were present at the three depths, with proportions of 30% with respect to the total phytoplankton for Depth 1 (surface), and 98 and 97% for Depths 2 and 3, respectively. According to Fig 4, M. aeruginosa was the species detected in the highest concentration. For the PTAR exit station (E2), a proportion of 88% of cyanobacteria and 12% of the rest of the phytoplankton was determined on the water surface. Similarly, for the Puerto Buga station (E4), a 55% concentration of cyanobacteria was observed at the surface level.
However, in this study the prevalence of cyanobacteria in the water body was of rather short duration, since in the following campaign the concentration of cyanobacteria decreased significantly, as can be seen in Fig 3.
3.3. Influence of physicochemical and environmental variables on the presence of cyanobacteria in the four sampling campaigns
According to the multiple regression models, the most appropriate inferential statistical model that meets the assumptions includes the variables temperature, pH, dissolved oxygen, chemical oxygen demand (COD), total suspended solids, total dissolved solids, nitrogen and orthophosphates. Based on the model results, the most statistically significant variables were temperature and pH, followed by dissolved oxygen, total suspended solids, and orthophosphates. Table 3 presents the explanatory variables in order of significance, as indicated by the Pr(>|t|) values.
The highest temperature was 27.9°C at the PTAR exit station (E2), which is the same point where the highest concentration of cyanobacteria was found (Figs 2-3). However, the temperature trends observed in this study show a decline in the later sampling campaigns, which corresponded with a reduction in cell concentration. 7.24 was the pH that favored the growth of cyanobacteria at this same site (Table B in S2 File).
Explanatory variables listed in order of significance; analysis applied to the physicochemical variables measured during the four sampling periods and the concentration of potentially toxic cyanobacteria.
3.4. Influence of four tributaries on the growth of M. aeruginosa
The results obtained after exposing M. aeruginosa cultures for 12 days to samples from four tributaries are shown in Fig 5. Tributary 2 was the one that had the greatest effect on the growth of cyanobacteria, with induction proportions for the three concentrations evaluated of 490% with respect to the control, which was a conventional culture medium for the growth of cyanobacteria (BG11).
Each tributary was evaluated using three concentrations (C1: 25%, C2: 50% and C3: 100% and a control) for 12 days. (T1) the wastewater treatment plant (PTAR) discharge output, (T2) Majagua, (T3) the wastewater inlet to PTAR, and (T4) Comfandi. Different treatments are statistically indicated *** p < 0.001.
Regarding the other tributaries, both Tributaries 1 and 4 had no effect on growth. Even when the samples were used at 100% concentration (C3), cyanobacterial growth was inhibited by 23% and 18%, respectively.
For Tributary 3, the highest growth induction (326%) was observed at Concentration 1, which consisted of 25% tributary sample mixed with BG11 culture medium. However, as the proportion of the tributary sample increased, growth declined compared to the control, with reductions of 79% and 7% at Concentrations 2 and 3, respectively.
To observe the differences in the growth of M. aeruginosa when samples from Tributaries 1 and 2 and the control were exposed, microscopic observations were made (Fig 6). In the sample of Tributary 2, it is possible clearly to observe the growth of very large colonies in the culture of M. aeruginosa, which are compact and composed of a greater number of cells. In contrast, in the control the number of colonies observed decreases. However, the growth with Tributary 1 is completely different, as no growing cells are observed, indicating the inhibitory effect on the growth of M. aeruginosa.
The group corresponding to Tributary 2 is statistically different from the other groups, indicating that it contains all the nutrients and conditions necessary for the growth of M. aeruginosa.
3.4.1. Influence of the environmental and physicochemical variables of the four tributaries on the growth of M. aeruginosa.
Given that Tributary 2 presented ideal conditions for the growth of cyanobacteria at all the concentrations evaluated, its microbiological and physicochemical variables were analyzed and some determining variables were found. Of these, the temperature of 24.9°C was the highest of all the tributaries and the one that coincided with the greatest growth of cyanobacteria.
The Partial Least Squares Regression (PLSR) model enabled the identification of key variables explaining cyanobacterial growth. A positive association was observed with ammoniacal nitrogen, alkalinity, and Kjeldahl nitrogen, while a negative association was found with indicators of biodegradable organic matter, including BOD, filtered BOD, COD, TOC, and coliform-type microorganisms (Fig 7). Some variables, such as orthophosphates, calcium, and magnesium were excluded from the analysis because they exhibited low variability across tributary types and acted as flat variables, introducing noise into the model (Table C in S2 File). Nevertheless, they provided information consistent with the conditions favoring cyanobacterial growth.
Variables represented with a blue bar are those that, according to the model, favor cyanobacterial growth.
Sample T2 exhibited high alkalinity (280 mg CaCO₃/L) and the highest concentration of ammoniacal nitrogen (32.8 mg NH₃-N/L). In contrast, this sample also showed low levels of nitrates (0.445 mg NO₃ ⁻ /L), total organic carbon (8.756 mg C/L) and recorded the lowest turbidity, with a value of 8.8 NTU.
Similarly, the values detected in the sample from Tributary 2 for microorganisms such as total coliforms, Escherichia coli, and thermotolerant coliforms indicate that this sample (T2) has the lowest concentrations of all the tributaries. Specifically, values of 46,000 MPN/100 mL were found for T2 whereas the other samples had values up to 8,360,000 MPN/100 mL, this highest concentration being found in Tributary 3.
3.5. Detection of cyanotoxins
Microcystin‑LR was detected at four sampling stations in the Calima reservoir across three of the four campaigns (Fig 8). Campaign 1 registered concentrations of 0.43 µg/L at Entrance 4‑5 (E3), Depth 2 (P2); and 0.25 µg/L at the dam station (E6), Depth 3 (P3). In Campaign 2, surface waters (P1) at the PTAR exit (E2) showed a concentration of 0.18 µg/L, while Entrance 4‑5 (E3), Depth 3 (P3) showed a concentration of 0.13 µg/L. No microcystin-LR was detected in Campaign 3, indicating a temporary absence of the toxin. Campaign 4 revealed a peak concentration of 0.60 µg/L at Puerto Buga (E4), depth 3 (P3).
E1: Tail, E2: PTAR exit, E3: Entrance 4−5, E4: Puerto Buga, E5: Center, E6: Dam. The base map was created by the author using Landsat 8/9 satellite imagery (Collection 2, Level-2) accessed through the USGS Earth Resources Observation and Science (EROS) Center (https://eros.usgs.gov/). These USGS products are in the public domain and are therefore fully compatible with the CC BY 4.0 license.
4. Discussion
Understanding which variables drove the growth of toxic cyanobacteria was fundamental, as it enabled the identification of key environmental parameters to monitor and manage in anticipation of future blooms. The lowest cell densities occurred at Dam (E6) across all four campaigns (Fig 2), likely due to lotic conditions and elevated flow that prevented large planktonic populations. Nonetheless, other factors such as temperature, precipitation, and nutrient levels were also implicated by other authors [9]. In contrast, during Campaign 2 (April), increased nutrient concentrations and higher temperatures coincided with elevated cyanobacterial densities, which were consistent with reports and confirmed that warmer conditions favored cyanobacteria [10,11]. Subsequently, during the rainy season campaigns (July and September), densities declined. Many authors have attributed this decline to rainfall-induced destratification and turbid mixing, which limited cyanobacterial proliferation [12]. The results suggested that mixing and water-column destabilization during rain events suppressed blooms, which reappeared only in the dry season. Following the wet season downturn, July’s campaign showed nearly zero cyanobacteria levels, with green algae and other phytoplankton dominating, a pattern that persisted into September.
The second sampling (April) revealed Microcystis as the dominant genus at higher concentrations (Fig 3). Bloom formation in Microcystis required preexisting buoyant populations tolerant of low nutrient levels, aided by gas vesicles, extracellular mucilage, and large colony size [13,14]. Mugani and colleagues similarly reported Microcystis dominance during bloom peaks, while other cyanobacteria and microalgae prevailed in early and senescent stages. They further noted the genus’s persistence from autumn through winter and even into the following year [15,16].
The study also found that cyanobacterial blooms inhibited other algal groups (Fig 4). Quantifying the proportion of toxic cyanobacteria relative to total phytoplankton was essential for assessing public health risks, water quality, and ecosystem management. Indeed, allelopathic effects of Microcystis were shown to suppress competitor species [17], with phytoplankton composition being shaped by light and nutrient availability [18].
Environmental drivers such as sustained high temperatures above 25°C promoted bloom formation through thermal stratification [19]. Together with elevated nitrogen and phosphorus, these factors supported sustained growth and dominance of toxigenic Microcystis in tropical regions [20]. While Microcystis generally thrived under high phosphorus, it survived vertical migration under low phosphorus, facilitating nutrient-driven succession with Planktothrix and Cylindrospermum [15]. Quantifying low level nutrients in surface waters remained a methodological challenge.
This is one of the few studies that evaluates the direct effect of tributaries on the growth of potentially toxic cyanobacterial cultures. Most reports focus on environmental and physicochemical knowledge of tributaries, but not on their direct action on cells [21]. Significant positive associations were observed with ammoniacal nitrogen, alkalinity, and Kjeldahl nitrogen (Fig 7). High alkalinity (~280 mg CaCO₃/L) enhanced bicarbonate availability as an alternative carbon source under low CO₂, favoring M. aeruginosa [22]. Moreover, ammoniacal nitrogen, a preferred nitrogen form, was shown to fuel rapid growth under eutrophic conditions, underscoring the need to manage nitrogen levels. Despite low nitrate (0.445 mg NO₃ ⁻ /L), cyanobacteria mitigated this limitation through nitrogen fixation and ammonium uptake.
Although low total organic carbon (8.8 mg C/L) might have restricted growth, cyanobacteria exploited inorganic carbon and thrived with adequate nutrients [23]. Furthermore, the low turbidity in Sample 2 (8.8 NTU – the lowest of all the samples) enhanced light penetration, promoting photosynthesis and proliferation, a parameter often omitted in predictive models [24].
Although Microcystis blooms were typically associated with bacteria [25], cyanobacterial dominance was shown to influence thermotolerant coliform levels through competition, toxicity, or environmental conditions. The findings suggested that specific components in tributary samples stimulated growth at very low concentrations, indicating that mixtures with BG11 medium optimized cyanobacterial cultivation more effectively than standard culture media [26]. On the other hand, the sample from Tributary 2 had the highest levels of calcium and magnesium; although these are not the main limiting factors for the growth of cyanobacteria, they could indirectly influence their presence and proliferation by changing some environmental conditions of the water, such as its pH [27]. Orthophosphate peaked at 5.100 mg PO43 ⁻ /L, likely due to anthropogenic activity leading to persistent blooms and impacting water quality [28].
Regarding the presence of microcystin-LR (MC-LR), this was detected at 4 of the 6 sampling stations (Fig 8). The results obtained are concerning, given the potential for cyanotoxins to increase, especially in the dry season, which coincides with the holiday season for many tourists to the Calima reservoir. The concentrations of toxins can vary according to the conditions of each system [16] and higher concentrations can be detected that should be monitored, at least in the dry season of the year, in order to avoid the possibility of reservoir users suffering poisoning. However, the highest concentration was detected at Station 4 (Puerto Buga), with levels below the maximum permissible limit (1 µg/L). Dam station (E6) could be considered the one with the lowest toxicological risk due to the low presence of toxin-producing species. However microcystin LR was detected, possibly due to the toxin’s persistence. Microcystin-LR is a stable molecule that can remain in the water after cell lysis, with a half-life of up to 10 weeks under environmental conditions.
5. Conclusions
This study confirmed that the development and dynamics of cyanobacterial blooms in the reservoir were driven by key environmental variables: elevated temperature, thermal stability, nutrients (particularly ammoniacal nitrogen, alkalinity, and phosphorus), and low turbidity. The identification of Microcystis as the dominant genus during the dry season, accompanied by detectable levels of microcystin-LR, underscored the potential public health risk and highlighted the necessity for continuous monitoring. Although rainfall and mixing significantly reduced cyanobacterial biomass during the wet season, such effects were temporary, with favorable conditions re-emerging in dry periods. Furthermore, tributary inputs were demonstrated to influence cyanobacterial growth directly, emphasizing the importance of monitoring specific components even at low concentrations. Therefore, implementation of a continuous monitoring system, including physical, chemical, and biological parameters, and regular evaluation of tributary discharges, was recommended in order to to enable effective management strategies and prevent future toxic blooms in the reservoir [29].
Supporting information
S1 File. Supplementary tables of variables in the six sampling stations and tributaries.
https://doi.org/10.1371/journal.pone.0340012.s001
(DOCX)
S2 File. Supplemental Tables, Figures, and Captions.
https://doi.org/10.1371/journal.pone.0340012.s002
(DOCX)
Acknowledgments
This work was supported by CVC (Corporación Autónoma Regional del Valle del Cauca- Colombia). We thank Sara Gaviria for assistance with biological assays and data collection.
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