The functional diversity of phytoplankton depends on the type of ecosystem and the seasonal period in areas affected by residues from the biggest Brazilian mining disaster

doi:10.21203/rs.3.rs-6298271/v1

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The functional diversity of phytoplankton depends on the type of ecosystem and the seasonal period in areas affected by residues from the biggest Brazilian mining disaster

https://doi.org/10.21203/rs.3.rs-6298271/v1

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1. Nine years ago, the southeastern region of Brazil experienced the largest mining disaster (in the Lower Doce River Basin), which impacted several natural ecosystems. The relationships between the functional diversity of phytoplankton and environmental conditions provide evidence of this community's responses to anthropogenic impacts. Thus, we aim to identify the main factors influencing the functional diversity of phytoplankton considering three types of ecosystems and two distinct seasonal periods. It is expected that functional diversity and the distribution of functional traits will be influenced by light availability, nutrient and metal concentrations, and grazing pressure.

2. Sampling was conducted between October 2018 and September 2021 in the Doce River and in shallow lakes and deep lakes of the floodplain. Functional diversity indices were measured based on seven functional traits, including volume, silica demand, heterocyst presence, mixotrophy capacity, aerotopes presence, flagela presence, and life form.

3. The RLQ models highlighted functional traits that can be used as indicators of environmental variation in the region. Deep lakes exhibited higher functional diversity due to lower eutrophication conditions, metal contamination, and greater light availability. In the Doce River, the increase in functional diversity during the dry season suggested an attenuation of anthropogenic pressures, considering the improvement in environmental conditions.

4. Our results showed a dependence of the functional diversity of phytoplankton on the type of environment and that these environments experience distinct effects according to seasonality. Additionally, we demonstrated that resource availability, lower metal contamination, and reduced grazing pressure sustained greater functional diversity of phytoplankton.

5. We emphasize the importance of considering functional aspects in phytoplankton as a tool in biomonitoring areas strongly impacted by anthropogenic activities.

Functional traits

functional dispersion

functional dynamic

environmental impact

mining dam

Phytoplankton plays a fundamental role in determining the quality and functioning of aquatic ecosystems (Cardinale, 2011). As primary producers, these communities significantly contribute to nutrient cycling and biomass production, influencing energy flow and contaminant accumulation in aquatic environments (Yilmaz et al., 2021). Understanding the regulatory processes that govern the structure and dynamics of phytoplankton communities is essential, particularly in ecosystems exposed to multiple anthropogenic impacts. While traditional approaches based on taxonomic identity have been widely employed in phytoplankton studies, functional diversity metrics have gained prominence in recent decades due to their ability to link community structure to ecosystem functioning (Cadotte et al., 2011) and their sensitivity to ecosystem alterations and disturbances (Mouillot et al., 2013).

Functional diversity focuses on the diversity of functional traits in organisms, defined as morphological, physiological, or behavioral characteristics that influence species survival and performance in a given environment (Tilman, 1999; Petchey and Gaston, 2002; Violle et al., 2007) as well as their role in ecosystem functioning (Dehling et al., 2014). Understanding these two dimensions is critical for elucidating community assembly mechanisms through adaptations to abiotic conditions and interspecies interactions while enabling predictions of ecosystem responses to numerous emerging environmental disturbances. For phytoplankton, well-established associations exist between specific functional traits and ecological axes, such as resource acquisition, predator defense, and resilience to environmental fluctuations (Reynolds, 2006; Litchman and Klausmeier, 2008). Functional traits are also closely linked to processes such as nutrient cycling, primary productivity, and sedimentation dynamics, making them invaluable for assessing ecosystem functioning (Brasil and Huszar, 2011). Thus, functional diversity metrics play a key role in investigating regulatory mechanisms in phytoplankton and their resilience capacity in the face of anthropogenic disturbances.

Anthropogenic impacts such as eutrophication, heavy metal contamination, and hydrological alterations significantly influence the functional diversity of phytoplankton communities (Salmaso and Tolotti, 2021). For example, eutrophication often selects traits associated with rapid resource acquisition and competitive dominance, reducing functional evenness and redundancy (Barrow et al., 2019). Despite the growing interest in functional approaches, specific responses of phytoplankton functional diversity to the complex interplay of stressors, such as metal contamination combined with grazing pressure, remain poorly understood. This knowledge gap is particularly evident in ecosystems affected by large-scale disturbances, such as mining tailings dam collapses.

The Lower Doce River Basin (LDRB) in southeastern Brazil represents a relevant case study. In November 2015, the collapse of the Fundão dam released approximately 50 million m³ of mining tailings, causing extensive environmental damage along the Doce River and its adjacent ecosystems (ANA, 2015; Brasil, 2015). This wave of tailings traveled over 600 km to the Atlantic Ocean, profoundly altering aquatic habitats. Similar dam collapses have occurred in other countries, such as Hungary (2010), the Philippines (2012), Canada (2014), Australia (2018), and Brazil (2019), highlighting the widespread nature and severity of these events (Owen et al., 2020). However, the responses of phytoplankton communities following such events remain largely unexplored.

In these scenarios, functional traits related to body size, such as cell volume, are particularly relevant due to their critical role in determining resource uptake, sedimentation rates, and susceptibility to grazing (Reynolds, 2006). These traits, alongside others such as mixotrophy, aerotopes, and flagella presence, offer insights into adaptive strategies under varying environmental pressures (Litchman and Klausmeier, 2008). Understanding the selection of functional traits in response to resource availability, metal contamination, and grazing pressure is crucial for assessing ecosystem recovery and resilience.

In this study, our objective is to identify the key factors influencing the functional diversity of phytoplankton in the LDRB, focusing on four key metrics: functional dispersion, functional evenness, functional divergence, and functional redundancy. We examine these patterns across three ecosystem types (lakes, shallow lakes, and rivers) and two distinct seasonal periods. Additionally, we aim to identify sensitive functional traits as indicators of increased stress. It is expected that functional diversity and the distribution of functional traits will be influenced by light availability, nutrient and metal concentrations, and interactions with zooplankton through grazing pressure, without a direct expectation of an increase or decrease in functional diversity in response to these factors. These environmental factors are anticipated to affect functional diversity in different ways depending on the ecosystem type and seasonal period, reflecting the complexity of interactions between environmental conditions and phytoplankton adaptations. This study contributes to the understanding of post-impact structure and functional dynamics of phytoplankton communities and their implications for ecosystem functioning.

Study area

The study was conducted in the Lower Doce River Basin (LDRB) region, located in the state of Espírito Santo, Brazil (19º30' to 20º30' S and 41º30' to 39º30' W) (Fig. 1). This area encompasses the portion of the basin entirely located within the state of Espírito Santo, representing approximately 14% of the total drainage area of the Doce River (CBH Doce, 2010). The basin is characterized by a tropical climate with a wet season from October to March, followed by a dry period from April to September (RRDM/Fest, 2019). During the dry and wet season, the mean flow at the most downstream hydrological station, 40 km upstream from the river mouth, is 368 and 965 m³/s, respectively (Oliveira and Quaresma, 2017). Additionally, the flow regime is regulated by the upstream dams of the Doce River. It is important to note that the inputs of mining tailings affected the Doce River channel, Nova Lake, and the shallow lakes Areão, Areal, and Monsarás (Miller et al., 2023).

Sampling (for environmental variables, phytoplankton, and zooplankton) was conducted monthly between October 2018 and September 2021. There were exceptions to the monthly sampling sequence due to logistical setbacks (interruptions in October 2019 and March 2021) and the COVID-19 pandemic (between April and November 2020). Samples were collected from the surface of three distinct ecosystems: the Doce River channel (E0, E21, E22, and E26), deep lakes (E18 – Limão, E19 – Nova, and E20 - Juparanã), and shallow lakes (E24 – Areão, E25, and E25a - Monsarás) (Fig. 1). The deep lakes have a mean depth greater than 11 m, while the shallow lakes have an average depth between 1.5 and 3.1 m (Fig. 1). The deepest environment was Nova Lake (E19) (Gonçalves, et al. 2016), and the shallowest was Areão Lake (E24).

Environmental variables

Turbidity (Turb. - NTU) was determined in situ using a YSI Horiba U-53 multiparameter probe. This variable was used as a proxy to estimate light availability in the water column. Total phosphorus (TP - µg/L) and total nitrogen (TN - µg/L) concentrations were determined according to Valderrama (1981). The dissolved fractions of aluminum (dAl - µg/L) and iron (dFe - µg/L) and the total fractions of chromium (tCr - µg/L) and manganese (tMn - µg/L) were selected for being abundant in the mining tailings that impacted the region since 2015 (Segura et al., 2016) and for their toxicity to aquatic biodiversity (Nowicka, 2022). The concentration of these metals was determined by inductively coupled plasma atomic spectrometry with mass spectrometry detection (ICP-MS), as described in Rede Rio Doce-Mar (RRDM/Fest, 2019). For environmental variables, the recorded values were compared to the maximum limits established by current legislation (CONAMA 357/2005), using the classification of Class 2 water bodies. Water of Class 2 is to be suitable for human consumption after conventional treatment, recreational use, tourism, agriculture, and protection of aquatic communities (Brasil, 2005).

Phytoplankton sampling and functional diversity

For the qualitative analysis of phytoplankton, samples were collected by horizontal towing with a plankton net with a mesh size of 20 µm. Subsurface water samples were collected in polypropylene bottles for phytoplankton quantitative analyses. Qualitative and quantitative samples were fixed with 3–5% formalin solution and 1% acetic Lugol solution, respectively. Phytoplankton identification was performed using a Motic Panthera optical microscope at the lowest possible taxonomic level. Phytoplankton density (ind./mL) was determined according to Weber (1973), using a Motic AE2000 inverted microscope. For this, sedimentation in counting chambers was conducted following the method proposed by Utermöhl (1958). Samples were allowed to settle for at least three hours per centimeter of chamber height (Lund et al., 1958), and counts were performed in random fields according to the methodology proposed by Uehlinger (1964), until at least 150 individuals of the most abundant species were counted. Specific biovolume (µm³), used for the functional size characterization of species, was calculated as the average volume of 20 individuals of each species (whenever possible), using the approximate geometric shapes of the organisms (Hillebrand et al., 1999; Sun and Liu, 2003). Abundance, used in statistical analyses, was determined as the product of the numerical density of each species and its specific biovolume and was expressed in µm³/mL.

Seven functional traits based on species morphology, physiology, and behavior were selected, related to reproduction processes, resource acquisition, and resistance to losses due to sedimentation and herbivory (Litchman & Klausmeier, 2008; Litchman et al., 2010). These traits include (I) Volume (continuous - µm³), (II) Silica demand (binary), (III) Heterocyst presence (binary), (IV) Mixotrophy capability (binary), (V) Aerotopes presence (binary), (VI) Flagella presence (binary), and (VII) Life form (Col – Colony life form, Fil – Filament life form, Uni – Unicellular life form).

Zooplankton and grazing pressure

For the assessment of grazing pressure (GP), zooplankton samples were obtained at the same sampling sites as phytoplankton. Sampling was done by filtering 50 L of water through a 68 µm mesh plankton net. The samples were fixed with 4% formaldehyde solution. Zooplankton quantification was performed in a Sedgwick-Rafter chamber using three subsamples of 1 mL. Zooplankton density (ind./L) was calculated, and biomass was estimated using dry weight and length regression (Bottrell et al., 1976, Ruttner-Kolisko, 1977, and Pinto-Coelho, 2004), where at least 30 individuals of each species were measured. The GP was estimated by the ratio between zooplankton biomass (dry weight – µg DW/L) and phytoplankton biomass (chlorophyll-a multiplied by 66 to convert µg/L to µg DW/L) (Jeppesen et al., 1994, Jeppesen et al., 1997, Jeppesen et al., 2005, Castilho et al., 2023). This relationship indicates of the approximate percentage of phytoplankton biomass removed by zooplankton action.

Data analysis

To determine spatial (between environments) and temporal (between seasonal periods) differences in environmental variables and phytoplankton functional diversity, we performed non-parametric Kruskal-Wallis chi-square tests using the kruskal.test() function. Subsequently, multiple comparisons were conducted using Dunn's test, corrected with Bonferroni adjustments, employing the dunn.test() function from the dunn.test package (Dinno, 2024).

To test the effects of resource availability, metal concentration, and grazing pressure on the functional diversity of phytoplankton, we focused on four metrics. First, we estimated the functional dispersion (FDis), functional evenness (FEve), functional divergence (FDiv), and functional redundancy (FR). FDis is represented as the mean distance of species, weighted by abundance, to the centroid of the multidimensional functional trait space (Laliberté and Legendre, 2010). FEve represents the regularity of species abundance distribution in the multidimensional functional space. This index shows how much of the environmental gradient may be occupied by the community species. FDiv represents the proportion of abundance displayed by species with the most extreme functional traits (species farthest from the center of the multidimensional functional space) composing the communities. This index represents complementarity in species’ resource use along the environmental gradient. The greater the FDiv, the lower the competition among species for resources. These functional diversity indices range from 0 to 1 (Mouillot et al., 2013). FR was calculated according to Bello et al. (2007), as the difference between species diversity (Gini-Simpson index) and functional diversity (Q Rao’s). FR varies from 0, when all species are functionally different, to 1, when all species are functionally identical. The Gini-Simpson index was calculated using the diversity() function from the vegan package (Oksanen et al., 2024). Functional diversity indices were calculated using the dbFD() function from the FD package (Laliberté et al., 2014), considering the abundance matrix (237 sites x 318 species) and trait matrix (318 species x 6 traits).

RLQ models combined with Fouth-corner analysis were performed to test the relationships between environmental variables and functional traits (Dray et al., 2014). For RLQ analyses, multivariate analyses were initially applied separately to the matrices of environmental variables (R), species abundance (L), and functional traits (Q), as follows: 1) Correspondence analysis (CA) on the L matrix, log-transformed, using the dudi.coa() function; 2) Principal component analysis (PCA) on the R matrix, standardized to mean zero and variance one, using the dudi.pca() function. The weighting of observations in the PCA applied to the R matrix was equalized by the values of observations resulting from the CA performed on the L matrix; and 3) Principal component analysis (PCA) on the Q matrix, using the dudi.hillsmith() function. This function allows for PCA with mixed sets of variables (Hill and Smith, 1976). The weighting of species in the PCA applied to Q matrix was equalized by the values of species resulting from the CA performed on the L matrix. RLQ analysis was applied to the results of these multivariate analyses using the rlq() function. Type 6 RLQ model was selected as it represents a combination of RLQ models 2 and 4. The significance and direction of the observed relationships between R and Q matrices were evaluated using Fourth-corner analysis, applied to L, R, and Q matrices, based on the type 6 RLQ model results. Fourth corner analyses were performed using the fourthcorner() function and p-values were adjusted by the fdr method. The functions used for RLQ and Fourth-corner analyses are included in the ade4 package (Dray and Dufour, 2007).

Principal Coordinate Analyses (PCoA) were performed on the Euclidean distance matrix of phytoplankton functional diversity indices using the cmdscale() function. Subsequently, environmental variables were fitted to the ordination to assess the relationships between functional diversity and the studied environmental gradients (directions of increasing environmental variables) using the envfit() function from the vegan package. Each variable was independently tested and fitted to the ordination using 99999 permutations.

All analyses were performed using R Studio software (R Core Team, 2023), with the significance level set at p < 0.05. The p-values obtained from the RLQ and Fourth-Corner analyses were adjusted using the False Discovery Rate (FDR) method. Additionally, the number of permutations for envfit analyses and the number of repetitions for testing the RLQ and Fourth-Corner models were fixed at 50,000.

Environmental variables

In the Doce River, the mean values of turbidity (108.3 NTU), TP (170 µg/L), dAl (116.9 µg/L), tCr (8.7 µg/L), dFe (282.5 µg/L), and tMn (70.5 µg/L) were significantly higher in the wet season (Table S1, Fig. 2). In contrast, no significant seasonal variations were observed in the shallow and deep lakes (Fig. 2). It is important to note that, despite higher nutrient averages in the Doce River, the maximum values were found in shallow lakes. During the wet season, there was an increase in turbidity, nutrient, and metal concentrations in the Doce River and shallow lakes (Fig. 2). However, during the dry season, the abiotic variables in this environment showed reduced means, resembling the variation in the lakes (Fig. 2). Deep lakes exhibited the lowest values for all environmental variables, with significant differences compared to other environments. However, during the dry season, the concentrations of abiotic variables in the Doce River were similar to those recorded in the deep lakes (Fig. 2).

During the wet season, shallow lakes and Doce River showed concentrations above the limits established by CONAMA Resolution 357/2005 for Class 2 water bodies for turbidity. Regarding nutrients, Only the Doce River, during the dry period, showed concentrations below the CONAMA 357/2005 limits for TP, while the TN average in deep lakes was below the limits for both seasonal periods (Fig. 2). Most the Doce River and shallow lakes samples exceeded the limits established by legislation, whereas fewer samples in deep lakes exceeded these limits. As for metals, we recorded mean concentrations above CONAMA 357 limits only for dFe and tMn in shallow lakes (Fig. 2). However, there were records of samples exceeding the regulatory limits of current legislation in both seasonal periods for all studied environments for metals dAl, dFe, and tMn (Fig. 2). No sample exceeded the CONAMA 357 limits for tCr.

Phytoplankton functional diversity and grazing pressure

We recorded 318 phytoplankton taxa in the LDRB, classified into 126 genera and 15 classes. The five main classes, in terms of species contribution, were Chlorophyceae (22%), Bacillariophyceae (19.5%), Cyanophyceae (19.2%), Zygnemaphyceae (12.9%), and Euglenophyceae (10.4%). Other less frequent classes included Trebouxiophyceae (5.3%), Coscinodiscophyceae (2.5%), Dinophyceae (2.2%), Xanthophyceae (1.3%), Chlamydophyceae, Chrysophyceae, Cryptophyceae, and Mediophyceae (0.9%), and Oedogoniophyceae (0.3%).

The FDis was higher during the wet season in the deep lakes, followed by the Doce River in the dry season (Table S2). However, we observed significant seasonal differences for this variable only for the Doce River (Fig. 3). Regarding the variation between environments, we observed that the FDis variation in the Doce River was similar to that in the shallow lakes during the wet season and in the deep and shallow lakes during the dry period (Fig. 3). We did not observe significant variation for FEve (Fig. 3). For FDiv, significant seasonal variation was also not recorded, but there was differentiation between environments. During the dry period, the Doce River exhibited higher FDiv values compared to deep lakes; however, these values were similar to those observed in shallow lakes (Fig. 3). For FR, significant seasonal variation was also not recorded (Fig. 3). Spatial differences in FR were identified between the Doce River and deep lakes during the wet season (Fig. 3).

Based on the list of species recorded for the evaluated environments (RRDM/Fest, 2021), the zooplankton community was predominantly composed of herbivorous/omnivorous organisms with filtering, scraping, and suctioning characteristics. Approximately 12% of the recorded species were identified as raptorial/predatory organisms. However, the GP remained predominantly low, especially in the Doce River (Table 3). No significant seasonal and spatial variation was recorded for GP.

Relationship of functional traits with environmental variables

During the wet season, traits such as Si (Si demand) and Vol (volume) were inversely related to GP and dFe, respectively (Fig. 4A). Het (heterocite presence) organisms showed a negative relationship with TN, dFe, and tMn, while Mix (mixotrophy capability) organisms were favored by increased tCr. With the exception of GP and dFe, Fla (flagella presence) was favored by all the environmental variables studied. The Uni (unicelullar life form) was positively correlated with almost all environmental variables except for GP. In contrast, Col (colony life form) and Fil (filamento life form) forms were negatively correlated with most environmental variables.

During the dry period, minimal changes were observed in the overall response patterns of functional traits compared to the wet period. The main modifications included a reduction in the influence of dFe on Vol, a negative relationship between Het and Turb, as well as a decreased effect of Turb and TP on Col forms, and tCr on Fil forms (Fig. 4B).

The RLQ analyses revealed that environmental variables influenced the distribution of phytoplankton species (mod2: p < 0.001) and that community composition depends on environmental variables and is influenced by species functional traits (mod4: p < 0.001) for both seasonal periods studied. For the wet season, 95.75% of the data variation was ordered within the first two axes (Fig. 5). Deep lakes showed a stronger association with filamentous (Fil) and colonial (Col) life forms. Although with a more tenuous relationship, organisms forming heterocysts (Het), aerotopes (Aer), and larger volume are also associated with deep lake ecosystems. These positive associations in deep lakes were linked to conditions of lower metal and nutrient concentrations and lower turbidity.

Conversely, the high concentrations of tCr, TP, dAl, and turbidity observed in the Doce River were positively associated with the presence of flagellates (Fla) and organisms with high sílica (Si) demand and mixotrophic capacity (Mix). In contrast, shallow lakes were characterized by higher concentrations of TN, dFe, and tMn, which favored the prevalence of small volume unicelular (Uni) forms, often Fla, and exhibited a reduced occurrence of Het and Aer (Fig. 5).

In the dry season, 89.52% of the data variation was summarized in the first two axes (Fig. 5). During this period, the effects of TP and TN were more pronounced in the Doce River compared to other environments, while the effects of dFe, tMn, turbidity, and GP were less intense (Fig. 4). Organisms with larger volume and high Si demand, as well as Uni forms, exhibited increased representation in the Doce River. Conversely, in shallow lakes, Col forms and Fla predominated. In deep lakes, during the dry season, Fil forms associated with Aer and Het organisms were more abundant (Fig. 5).

Relationship of diversity indices with environmental variables

In the wet season, PCoA summarized 72,3% of the data variation in its first two axes. The first PCoA axis was responsible for a theoretical variation of increase in functional diversity on the positive side and an increase in community specialization on the negative side, represented by the gradients of FDis and FDiv, respectively (Fig. 6). The adjustment of environmental variables also indicated that the first axis was strongly associated with eutrophication processes, metal contamination, and higher turbidity on its negative side, with Turb and TN significantly influencing the functional structure of phytoplankton. Conditions of lower eutrophication and higher light availability (lower turbidity) favored an increase in FDis and FR, especially in deep lakes. In contrast, these conditions contributed to the increase in FDiv in shallow lakes and the Doce River.

In the dry season, PCoa described 69.0% of the data in its first two axes. The first axis described the gradients of increasing FDis and FR on its negative side and FDiv on its positive side (Fig. 6). The first axis represented the gradients of increasing FDis on the negative side and FDiv and FEve on the positive side. However, during the dry period, the highest FDis pattern was recorded in the Doce River, while deep lakes exhibited greater variation in functional diversity indices compared to the wet period. Additionally, we found that Turb and concentrations of Fe and Cr were significantly associated with the functional structure of phytoplankton during the dry period, where the reduction in these environmental variables resulted in increased FDis, especially in the Doce River.

We identified significant relationships between environmental factors and the functional diversity of phytoplankton. The Doce River experienced greater impacts from seasonal variation in environmental conditions, with a wet season showing higher metals and phosphorus loads, and reduced light availability. These conditions are directly related to the input of material from the watershed through surface runoff, as there is a reduction in these factors during dry periods. The Doce River basin is characterized by significant sediment production and transport to the Doce River (Palu e Julien, 2020), with 63% of the basin area composed of pasture and the urban regions responsible for the discharge of 389 and 1020 tons of phosphorus and nitrogen, respectively, per year into the main channel of the river (RRDM/Fest, 2019). The process of land development in the river basin, particularly with impervious surfaces, increases surface runoff during the wet season. Therefore the replenishment capacity of water bodies by groundwater during the dry periods is adversely affected, as it hinders water percolation in the soil (Sajikumar and Remya, 2015; Abuhay et al., 2023), exerting a strong influence on the ecosystem processes. This may have been a significant factor responsible for reducing water levels in the Doce River during dry periods and increased stress during the wet season. Lakes are less subjected to pronounced seasonal differences. Furthermore, the increase in metals in shallow lakes and the Doce River during the wet season may indicate a combined impact with the input of external material and sediment resuspension.

We identified a strong correlation of functional traits with environmental conditions. During the wet season, the capacity for mixotrophy and silica demand were the most abundant traits, favored by increased nutrient availability and reduced light availability, especially in the Doce River. In our study, organisms with the Si trait were exclusively diatoms, and indeed, this group is known for its tolerance to turbulence in rivers (Lobo et al., 2016). Additionally, diatoms have a high phosphorus requirement (Hillebrand et al., 2013), which may explain its greater representativeness under conditions of increased PT concentration. Moreover, the increased abundance of Mix with turbidity highlights the advantage of this trait in reduced light conditions (Litchman et al., 2010), as energy acquisition occurs through heterotrophic pathways.

Moreover, tolerances of Cryptophyceae species to low light conditions have also been reported (Clegg et al., 2003; Litchman et al., 2010), supporting the responses for the Fla trait observed in our study. During the dry period, the Doce River exhibited a significant reduction in turbidity and metal contamination, resulting in an increased abundance of other less expressive functional traits compared to the wet season, which led to an increase in FDis in this environment. This condition of lower oxidative stress caused by metals (Nagajyoti et al., 2010) and higher light availability may have been important factors for the more significant contribution of other functional traits during the wet season in the fluvial environment.

Contrary to expectations, we found a negative relationship between the traits Het and Aer and metals such as iron and manganese. These traits are closely related to cyanobacteria, so a positive relationship between these traits and metal concentrations was expected, given the high metal requirement exhibited by this group of organisms (Huertas et al., 2014). Although this was not expected, when analyzing spatially, we observed that these traits were quite common in deep lakes, where metal concentrations were lower compared to shallow lakes and the Doce River. This suggests that other factors, such as turbulence, may strongly drive the occurrence of species exhibiting these traits. In deep lakes, there is a tendency for greater physical stability of the water column, leading to vertical resource gradients (Obertegger et al., 2007; Rangel et al., 2012, Gonçalves et al., 2016). This enables species to migrate vertically to resources acquisition or minimize losses (Salonen et al. 2024), providing competitive advantages within the community. Additionally, prolonged stratification periods lead to nutrient depletion in the epilimnion (Lewis and Wurtsbaugh, 2008). Our results showed a strong negative correlation between Het and the Aer with TN. This apparent relationship between lower nutrient concentrations, especially TN, and the formation of specialized cells for nitrogen fixation in deep lakes highlights the competitive advantage of this trait (Zeng and Zhang, 2022).

Furthermore, the ability for vertical movement within the water column to regions of higher or lower light represents a vital survival strategy in slow-flow systems (Litchman and Klausmeier, 2008), Where cell sedimentation is an important biomass loss process (Brasil and Huszar, 2011). This may explain the higher abundance of Fil and Col forms in these deep systems, especially when these life forms are concomitant with the formation of aerotopes.

In addition to environmental filters, phytoplankton also face pressure from interactions with consumer communities (Kruk et al., 2017). Studies have shown that smaller cells experience greater grazing pressure than larger cells, while colony formation provides an advantage by hindering predation (Pancic and Kiorboe, 2018). We observed a negative relationship between GP and Uni forms and a positive relationship with Col forms, highlighting the efficiency of colony formation as an anti-herbivory mechanism (Lurling, 2021). Our results showed that, although lakes exhibited higher GP than the Doce River, without seasonal variation, this pressure was even more pronounced in shallow lakes during the dry period. This increase in GP during the dry season, although not significant, may have been sufficient to influence competitive processes among phytoplankton life forms, reducing the abundance of Uni forms and increasing the contribution of Col forms, which are less susceptible to zooplankton predation. These results underscore the importance of incorporating variables indicative of community interactions and environmental variables to unravel the controlling mechanisms of phytoplankton functional diversity.

The effects of toxic components should also be considered in determining the structure and functional dynamics of biological communities. Our results showed adverse effects on Fil forms during the wet and dry seasons. Indeed, it has been recognized that damage to important critical metabolic processes, alterations in cellular structure, and oxidative stress are the main effects of metal toxicity (Nagajyoti et al., 2010). These effects of metals may have been lethal to the phytoplankton of the LDRB, resulting in a consequent reduction in the abundance of Fil forms. These results could explain the decrease in functional diversity, especially of FDis, with the increase in metal concentrations, particularly in the river and shallow lakes. However, Fla and Mix organisms appear tolerant to the increment in metal concentrations. Various antioxidant mechanisms are expressed in flagellated organisms (Dinophyta, Cryptophyta, and Euglenophyta) in response to oxidative effects caused by metals (Nowicka, 2022). The presence of these mechanisms may explain the good representation of these flagellated organisms under conditions of higher metal concentrations, especially in the fluvial environment during the wet season and in shallow lakes during the dry season.

Our results showed that FDis was driven by the greater variety of life forms in deep lakes during the wet season. The FDis metric indicates how diversely the functional traits of phytoplankton are distributed in functional space, considering not only the differences between species but their abundance (Laliberté and Legendre, 2010). This effect was mainly attributed to higher light availability and lower metal concentrations in these environments. Light availability’s crucial role in increasing phytoplankton’s functional diversity is well known (Reynolds, 2006; Weithoff et al., 2014) and this factor is strongly influenced by hydrology (Rangel et al., 2012). Our results suggest that the more stable conditions in deep lakes, in both seasonal periods, compared to the riverine system or shallow lakes, contributed to developing a greater variety of functional traits in phytoplankton, expanding their niche occupation range, as observed by FDis. Santos et al. (2022) also suggested water column stability as a factor responsible for the niche differentiation occupied by species and greater functional diversity. Similarly, conditions of greater stability, due to fluvial damming were responsible for increased FDis in the Paraíba do Sul River (Graco-Roza et al., 2021).

On the other hand, during the dry season, there was a reduction in flow regime, better light availability, and lower metal concentrations in the Doce River, which provided favorable conditions for the recruitment of less abundant functional traits during the wet season, justifying the increase in FDis in this environment. This increase in FDis reflects the complementarity of functional traits present in the community and the better distribution of abundance (Laliberté and Legendre, 2010), allowing species to explore available resources in different ways (Mason et al., 2005; Villegér et al., 2008). In contrast, in the lakes, the functional diversity of phytoplankton did not show significant modification during the dry season compared to the wet season, suggesting that seasonal environmental changes did not have sufficient force to alter the pattern of functional trait composition recorded in these environments, maintaining functional diversity relatively stable over time. This may indicate that the environmental pressures experienced by lake communities may not have driven the modification of functional composition in terms of substituting functional traits, but instead altered the pattern of abundance distribution of species within these traits. This may explain the seasonal variation in FDis in the Doce River and its maintenance in the lakes.

Although there is a distinction in species abundance distribution pattern in the functional space, this did not result in seasonal or spatial differences for FEve. The FEve metric indicates the community's resource use, being more efficient when FEve is high (Mason et al., 2005; Kuebbing et al., 2018) due to the more uniform distribution of species abundance in the functional space. These results show that the LDRB’s phytoplankton efficiently occuped the functional space over time and space. In contrast, FDiv values showed slight environmental variation, higher in the Doce River than in deep lakes. This indicates that the phytoplankton of the Doce River exhibited slightly higher abundance in outer regions of the functional space, i.e., species with functional traits further away from the community's mean functional traits (Villegér et al., 2008). Such findings suggest that the phytoplankton of the Doce River comprises organisms exhibiting greater differentiation, particularly those with a demand for silica, flagellates, and mixotrophic capacity. Due to lower niche overlap, a higher abundance of organisms with extreme characteristics represents less resource competition (Mason et al., 2005). In contrast, the lower FDiv values in deep lakes indicate that most of the functional space is occupied, leading to greater niche overlaps (Mouillot et al., 2013). These distinct functional patterns reflect the adaptations of phytoplankton to different environmental conditions, especially between lotic and lentic ecosystems (Chen et al., 2015).

Our results revealed that deep and with less disturbance, tipically lentic environments tend to support greater functional diversity in phytoplankton, as represented by FDis and FEve. Similar findings were made by Santos et al. (2024) for the zooplankton community in areas affected by mining waste. This may indicate that the greater stability of the water column in deep lakes can support higher and more functionally diverse biodiversity compared to the more dynamic environments. This pattern is even more evident in the wet season, where more dynamic systems such as rivers and shallow lakes experience more significant disturbances. Additionally, these more disturbed environments presented lower competition due to higher FDiv.

Furthermore, we observed that the phytoplankton community of the LDRB showed high susceptibility to environmental disturbances due to the dynamics observed for FDis and FR. While high FR values confer greater resistance to environmental disturbances (Joner et al., 2011), reduced FDis values indicate lower functional diversity (Laliberté and Legendre, 2010). Therefore, the increase in FDis in the Doce River during dry periods was attributed to the development of unique species, that is, species with functional traits that were less prevalent in this environment, such as the decline in the abundance of Fla and Mix species and the subsequent increase in the abundance of larger Vol species, mainly associated with reduced turbidity (Weithoff et al., 2014). These results highlight the importance of functional diversity in phytoplankton and how it can influence ecosystem stability and resilience to environmental changes.

The availability of resources and lower metal stress favored the development of a more functionally diverse phytoplankton, confirming the first point addressed in our hypothesis. The wet season was responsible for more adverse conditions in the Doce River and shallow lakes. In contrast, during the dry season, improvements in environmental conditions in the river led to the increased functional diversity of phytoplankton in this system. On the other hand, no drastic seasonal changes were observed in the functional diversity of phytoplankton in the lakes. However, the pattern of trait recruitment, i.e., the distribution of species abundance in functional space, was slightly different between the seasonal periods in these environments. These findings lead us to refute the second point addressed in our hypothesis regarding the independence of phytoplankton functional responses to the type of environment and seasonal period, as we perceive differences in the behavior of functional diversity among the studied environments, highlighting a spatial dependence.

Additionally, we concluded that the effects of seasonality exhibited a certain dependence on the type of environments studied, with its influence being more evident in the functional diversity of the Doce River. Our work provides an initial and integrated understanding of functional diversity in the LDRB, highlighting the influence of environmental factors on phytoplankton functional patterns. We emphasize the importance of considering functional aspects in phytoplankton biodiversity analyses as a tool in biomonitoring areas strongly impacted by anthropogenic activities, such as the region of the LDRB affected by mining tailings. This information is relevant for managing and conserving these aquatic ecosystems, as well as understanding the ecological processes that occur in this region.

Author Contribution

Conceptualisation: F.B.O., S.Z.A. Developing methods: F.B.O. Conducting the research: F.B.O., S.Z.A. Data analysis: F.B.O. Data interpretation: F.B.O., L.O.C., E.M.E., L.P.D., G.F.B., A.D.B., V.O.L., S.Z.A. Preparation figures & tables: F.B.O. Writing: F.B.O., L.O.C., E.M.E., L.P.D., G.F.B., A.D.B., V.O.L., S.Z.A.

Acknowledgement

We thank the teams of Professor Dr. Gilberto Fonseca Barroso (UFES), Dr. Vânya Márcia Duarte Pasa (UFMG), and Dr. Eneida Maria Eskinazi-Sant’Anna (UFOP) for their contribution to the nutrient, metal, and zooplankton analyses, respectively. We also acknowledge the members of the freshwater phytoplankton theme for their assistance in phytoplankton community analyses. This study was conducted within the scope of the Aquatic Biodiversity Monitoring Program, Environmental Area I, developed in partnership between Fundação Renova and Fundação Espírito-santense de Técnologia (FEST), based on the technical-scientific cooperation agreement, DOU No. 38/2018, which provided financial support. We declare that there is no conflict of interest.

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The functional diversity of phytoplankton depends on the type of ecosystem and the seasonal period in areas affected by residues from the biggest Brazilian mining disaster

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Abstract

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INTRODUCTION

MATERIALS AND METHODS

Study area

Environmental variables

Phytoplankton sampling and functional diversity

Zooplankton and grazing pressure

Data analysis

RESULTS

Environmental variables

Phytoplankton functional diversity and grazing pressure

Relationship of functional traits with environmental variables

Relationship of diversity indices with environmental variables

DISCUSSION

CONCLUSIONS

Declarations

Author Contribution

Acknowledgement

References

Additional Declarations

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