Bioaccumulation and Tolerance of Metals in Floristic Species of the High Andean Wetlands of the Ichubamba Yasepan Protected Area: Identification of Groups and Discriminant Markers

Abstract

The Ichubamba Yasepan wetlands, in the Andean páramos of Ecuador, suffer heavy metal contamination due to anthropogenic activities and volcanic ash from Sangay, impacting biodiversity and ecosystem services. This quasi-experimental study evaluated the bioaccumulation and tolerance of metals in high Andean species through stratified random sampling and linear transects in two altitudinal ranges. Concentrations of Cr, Pb, Hg, As, and Fe in water and the tissues of eight dominant plant species were analyzed using atomic absorption spectrophotometry, calculating bioaccumulation indices (BAIs) and applying principal component analysis (PCA), clustering, and linear discriminant analysis (LDA). Twenty-five species from 14 families were identified, predominantly Poaceae and Cyperaceae, with Calamagrostis intermedia as the most relevant (IVI = 12.74). The water exceeded regulatory limits for As, Cr, Fe, and Pb, indicating severe contamination. Carex bonplandii showed a high BAI for Cr (47.8), Taraxacum officinale and Plantago australis for Pb, and Lachemilla orbiculata for Hg, while Fe was widely accumulated. The LDA highlighted differences based on As and Pb, suggesting physiological adaptations. Pollution threatens biodiversity and human health, but C. bonplandii and L. orbiculata have phytoremediation potential.

1. Introduction

The Andean páramos, alpine ecosystems located at elevations above 3000 m.a.s.l., are recognized for their high biodiversity and their critical role as water sources for the lower Andean watersheds [1,2]. However, these ecosystems face increasing threats from heavy metal contamination from anthropogenic activities such as mining, intensive agriculture, and industrial processes, as well as from natural sources such as volcanic activity [3,4,5]. Metals such as lead (Pb), cadmium (Cd), mercury (Hg), copper (Cu), arsenic (As), and nickel (Ni) accumulate in soils and surface waters, particularly in wetlands, seriously affecting flora, fauna, and ecosystem services [6,7,8].

Mining activity, which is predominant in the Andes, is a major source of Cd, Cu, and Hg pollution, especially in areas affected by artisanal gold mining, where improper practices release these metals into water sources, compromising water quality and human health [4,7]. In addition, Sangay is an active volcano that emits large amounts of volcanic ash rich in heavy metals and metalloids such as cadmium, lead, mercury, arsenic, and iron. These elements, present in the magmatic composition, are transported by the wind and deposited in surrounding ecosystems, including the páramos and bofedales of Ichubamba Yasepan. This deposition makes ash a major source of pollution in the region. Once the ash settles, heavy rainfall in the moorlands causes heavy metals to leach into the environment, as water dissolves the metal compounds attached to the ash particles. The volcanic soils in the area, which act as reservoirs for these metals, release the contaminants into water bodies due to the humid conditions [9]. It should be noted that these non-essential metals, unlike essential micronutrients such as Fe, Mn, Zn, Cu, Mo, and Ni, are toxic and alter the physiological processes of plants, reducing biodiversity and affecting soil health [10,11]. The bioaccumulation of these contaminants in species such as rainbow trout (Oncorhynchus mykiss) and agricultural crops poses a significant risk to human health through the food chain [8].

The bioavailability of heavy metals in volcanic soils is influenced by factors such as pH, soil texture, and organic matter, which facilitate their uptake by plants and their entry into food webs [12]. Recent studies have reported serious toxicological effects, such as an increased incidence of thyroid cancer in regions with high exposure to heavy metals from volcanic ash, highlighting the implications for public health [13]. In addition, leaching of ash deposits and rainfall runoff aggravate contamination in surrounding areas, intensified by anthropogenic activities and natural geological processes [9,14].

To mitigate these impacts, strategies such as phytoremediation have shown potential to absorb and stabilize heavy metals, using plant species adapted to contaminated environments [15,16]. Likewise, bioremediation with metal-reducing bacteria and the implementation of sustainable agricultural practices offer promising solutions, although their effectiveness depends on the local ecological context [17,18,19].

In this context, the relationships between tolerance and bioaccumulation of metals in plants of the wetlands of the protected area are analyzed in order to identify groups and discriminating markers that allow for the identification of differential patterns of bioaccumulation of heavy metals in plant species and the existence of specific physiological mechanisms of absorption and retention, with the objective of creating strategies for the management and conservation of wetlands in the medium and long term.

2. Materials and Methods

2.1. Study Area

The Ichubamba Yasepan protected area covers an area of 4790.13 hectares and is located in the Guamote Canton, Chimborazo Province, Ecuador, in the Ecuadorian Andes, at a latitude of 2°5′9.47 south and a longitude of 78°29′38.64 west. This area is part of Ecuador’s National System of Protected Areas (SNAP) and is ecologically characterized by its páramo ecosystems, with an altitudinal range between 3448 and 4120 m above sea level. The climate is typical of high mountains, with an average annual temperature of 7.2 °C and annual precipitation of 1246.6 mm per square meter. The geology of the area is characterized by volcanic soils and the presence of 154.75 hectares of wetlands. Rainwater runs off the mountains to the east and accumulates in the middle zone, where it forms the Yasipan River, which flows from east to west through the protected area (Figure 1).

The methodological scheme used was the following (Figure 2):

2.2. Floristic Inventory

The wetlands were stratified in two altitudinal ranges: lower (3440 to 3640 m.a.s.l.) and upper (3640.1 to 3840 m.a.s.l.), with areas of 1744.32 and 1542.26 hectares, respectively. Grids of 100 × 100 m were applied using Arc GIS software version 10.8, resulting in 196 cells: 154 in the lower stratum and 42 in the upper stratum (Figure 3).

Stratified random sampling was used to determine the sample, which is a robust methodology for conducting flora inventories, as it allows different subgroups to be adequately represented. Its main objective is to divide the population into strata that are internally homogeneous but heterogeneous among themselves, which improves the accuracy of estimates. The correct definition of the strata was key, taking into account the altitude factor, as it allows for the capture of the ecological and floristic variability in the Ichubamba Yasepan wetlands, which significantly affects species composition, environmental conditions, and, potentially, heavy metal bioaccumulation patterns. This method has proven effective in ecological studies by improving data representativeness and minimizing geographical and ecological gaps [20]. In addition, it balances the sampling effort between strata and reduces the overall variance in the mean estimator, which improves the reliability of flora characterization [21]. Previous research has confirmed that this approach improves statistical efficiency compared to simple random sampling, especially in populations with high heterogeneity [22].

The sample size for each stratum was calculated using the sample size equation for finite populations [23]. The formula was developed by Krejcie and Morgan in their pioneering work in 1970:

$$\mathrm{n}={\displaystyle \frac{\mathrm{N}·\mathrm{Z}·\mathsf{\rho }(1·\mathsf{\rho })}{\left(\mathrm{N}-1\right)·e^2+{\mathrm{Z}}^2\mathsf{\rho }·(1-\mathsf{\rho })}}$$

where (N) is the population size, (Z) is the confidence level, (p) is the expected proportion, (e) is the margin of error, and n is the sample. A sample of 130 was determined, and then the proportionality constant was calculated with the formula Κ = n/N, obtaining a constant of 0.66.

Table 1. Sample calculation for the upper and lower strata.

Altitudinal Stratum	Number of Cells	Sample Per Stratum
Upper	42 × 0.66	28
Lower	154 × 0.66	102
Total	196	130

shows the number of cells for each stratum and its respective sample calculation. Linear transects were applied for the collection of flora data, and the design was based on walking in straight and segmented lines, depending on the characteristics of the landscape and the need to minimize sampling biases [24,25]. Each transect was 100 m long and 2 m wide [26].

During data collection, the species present were identified, recording their diversity and abundance [27]. Samples not recognized in the field were collected and transferred to the Herbarium of the Escuela Superior Politécnica de Chimborazo (Espoch), where they were cleaned by removing external residues, and then pressed for preservation and subsequent identification.

The data were subjected to statistical analysis to calculate biodiversity metrics such as the importance value index (IVI) [28,29,30].

2.3. Bioaccumulation of Metals in Wetlands

The first step was to analyze the concentrations of metals in the water of the 10 wetlands for the following parameters: Pb, Hg, As, Cr, and Fe. Two samples were taken for each wetland, collected in multiple locations to take into account the spatial variability in contamination and the dynamics of metals [31,32]. Samples were collected early in the morning in containers suitable for preserving their characteristics [33].

The analysis of the samples was performed by atomic absorption spectrophotometry (AAS), which is widely used for the detection of specific metals. Flame AAS and graphite furnace AAS were used, which are effective for the analysis of trace metals [34,35]. Special attention was paid to the detection limits of the method and possible interferences. The analyses were performed in the Laboratory of Basic Sciences and Bromatology of the Faculty of Livestock Sciences of Espoch. To ensure the accuracy and reliability of the results, quality control measures were applied, such as the use of certified reference materials and the performance of duplicate analyses that improved the integrity of the results [36]. The results were validated by one-way ANOVA to assess the significance of samples from different locations [32].

The second step was to analyze the concentrations of metals in roots, stems, and leaves in 8 plant species that presented the highest values of the importance value index (IVI), due to their ecological relevance. Collection was performed uniformly to ensure that the samples represented the variability in the wetland ecosystem.

Once collected, plant samples were subjected to appropriate pretreatment, including washing to remove surface contaminants and air-drying. Before grinding, the leaves, stems, and roots of each plant were separated to obtain a powder that facilitated digestion and analysis. Acid digestion with nitric acid (HNO₃) and hydrochloric acid (HCl) was applied, which is often used to effectively extract metal concentrations from plant tissues [37,38].

Atomic absorption spectrophotometry (AAS) was then used, particularly for Hg, considering that AAS can be optimized with techniques such as cold vapor generation to improve detection limits [39].

Finally, the bioaccumulation index (BAI) was calculated, which is defined as the ratio between the concentration of a metal in plant tissues (mg/kg) and its concentration in water (mg/L) [40,41]. This factor provides information on the efficiency of the plant in absorbing and accumulating metals in relation to their concentrations in the environment, which is essential for evaluating the phytoremediation potential and its implications for food safety [42,43].

The bioaccumulation index is quantified by the formula BAI = CP/CA, where CP represents the metal content in plant tissues and CA represents the metal content in water [41,42,43].

2.4. Multivariate Statistical Analysis

For the analysis of the data, a main table was constructed: in the columns were placed the variables corresponding to the species, and in the rows the concentrations of the different metals. MULTBIPLOT software version 16.430.0.0, which allows for the handling of multiple variables and facilitates the visualization of complex relationships, was used [44]. The following methods were applied:

To identify the main patterns of metal accumulation in the studied species and tissues, principal component analysis (PCA) was applied, which is a statistical technique to reduce the dimensionality of complex data by transforming correlated variables into uncorrelated principal components. The first component captures the largest variance, and the following ones capture decreasing variances, revealing significant patterns. Mathematically, PCA centers the data, calculates the covariance matrix, and obtains eigenvalues and eigenvectors, projecting the data onto the upper eigenvector subspace [45,46,47].

PCA is applied in various fields, such as image processing, genetics, and social and environmental sciences [48,49]. Its ability to handle diverse data makes it a versatile tool for data analysis.

For PCA, the data were standardized using the formula $\mathrm{Z}={\displaystyle \frac{\mathrm{X}-\mathsf{\mu }}{\mathsf{\sigma }}}$, where X is the original value, μ is the mean of the variable, and σ is the standard deviation. Then, the covariance matrix was calculated using the formula $\mathrm{S}={\displaystyle \frac{1}{\mathrm{n}-1}}{\sum }_{\mathrm{i}-1}^{\mathrm{n}}(\mathrm{X}\mathrm{i}-\overline{\mathrm{X}}\left)\right(\mathrm{X}\mathrm{i}-\overline{\mathrm{X}}{)}^{\mathrm{T}}$, and then the eigenvalues and eigenvectors of the covariance matrix were obtained using Sν = λν, where v is the eigenvector and λ is the corresponding eigenvalue, and the principal components were formed. Finally, the Hj biplot visualization was generated, which allows for the simultaneous representation of the observations and variables in a two-dimensional space. The coordinates of the observations are equal to F = XA, where A are the first two eigenvectors scaled by the square root of their respective eigenvalues, and the coordinates of the variables are equal to $\mathrm{G}=\mathrm{V}{\bigwedge }^{1/2}$, where V is the eigenvector matrix and Λ is the diagonal matrix of eigenvalues.

Cluster analysis allowed for the grouping of the data of the variables under study, ensuring that the objects within the same cluster were more similar to each other. Grouping was applied using K-means, widely used for its computational efficiency and simplicity. This method divides the data into K clusters by iteratively assigning data points to the centroid of the nearest cluster and recalculating the centroids until convergence is reached. K-means is especially effective for large datasets [50].

Linear discriminant analysis (LDA) was applied to construct a linear discriminant function that maximizes the separation between different classes. A fundamental principle of LDA is the optimization of the ratio of the between-class variance to the within-class variance, thus improving the classification performance [51]. The assumptions of normality of the predictor variables and homogeneity of the variance–covariance matrices between groups were verified [52].

Once the model was established, cross-validation was used, which helps to assess the predictive ability of the model with unanalyzed data, which is imperative to avoid overfitting [53].

3. Results

3.1. Composition and Structure of Wetlands

The plant community studied is composed of 25 species belonging to 14 botanical families, with Poaceae and Cyperaceae as the most important families, together representing about 30%. Asteraceae, with three species, stands out for its diversity and moderate distribution. Families such as Rosaceae and Geraniaceae also play a significant role, albeit with fewer species. In contrast, families such as Solanaceae and Iridaceae have a minimal contribution, indicating a restricted presence.

C. intermedia, stands out for its probable association with grassland habitats. Species such as Carex pichinchensis and Eleocharis sp. show adaptations to humid conditions, while Asteraceae, with three species recorded, reflects a moderate ecological participation. Together, these patterns indicate an ecosystem dominated by grasses and sedges, adapted to the wetlands, with a moderate floristic richness and a plant structure influenced by a few key families.

Table 2. Density, relative frequency, and importance value index of wetland species.

Family	Species	%Relative Density	%Relative Frequency	Importance Value Index (IVI)
Apiaceae	Eryngium humile Cav. (1797)	3.87	2.7	3.28
Apiaceae	Daucus montanus Humb. & Bonpl. ex Schult. (1809)	2.83	2.22	2.53
Plantaginaceae	P. australis Lam. (1791)	8.19	5.56	6.38
Asteraceae	Gnaphalium spicatum Lam. (1783)	1.80	2.7	2.25
Cyperaceae	Eleocharis sp.	3.64	4.44	4.04
Asteraceae	Diplostephium ericoides Kunth & Wedd. (1857)	2.99	2.2	3.16
Campanulaceae	Centropogon solisii E.Wimm. (1938)	2.43	1.11	1.77
Lamiaceae	C. nubigenum Kunth & Kuntze (1891)	7.31	3.33	4.82
Ericaceae	Vaccinium floribundum Kunth (1819)	2.99	2.1	2.61
Fabaceae	Trifolium amabile Kunth (1816)	1.29	2.41	3.35
Fabaceae	Medicago polymorpha L. (1753)	3.15	1.11	2.13
Geraniaceae	Geranium laxicaule G.Don (1831)	4.85	5.56	5.20
Polygonaceae	Rumex. acetosella L. (1753)	4.64	6.67	5.15
Iridaceae	Tigridia pavonia L.f. & DC. (1802)	1.03	2.70	1.87
Lamiaceae	Stachys elliptica Kunth (1818)	2.43	2.10	2.32
Cyperaceae	Carex pichinchensis Kunth (1816)	3.64	2.10	2.93
Orobanchaceae	Lamourouxia virgata Kunth (1818)	5.50	2.33	3.42
Poaceae	Agrostis perennans Walter & Tuck. (1843)	1.86	5.56	3.71
Poaceae	C. intermedia J.Pres & Steud. (1840)	7.76	17.78	12.74
Rosaceae	L. orbiculata Ruiz & Pav. (1798)	7.19	6.67	7.93
Geraniaceae	Geranium diffudum L. (1753)	3.15	2.22	2.69
Asteraceae	T. officinale F.H.Wigg. (1780)	5.61	6.67	4.90
Cyperaceae	Carex bonplandii Kunth (1816)	6.67	6.43	6.57
Rubiaceae	Galium hypocarpium Endl. ex Griseb. (1879)	3.64	2.22	2.93
Solanaceae	Solanum nigrescens M.Martens & Galeotti (1845)	1.54	1.11	1.32

shows the species and families found in the floristic inventory, in addition to calculations of relative density, relative frequency, and species importance value. The analysis of the composition and structure of the vegetation in the wetlands reveals a structure dominated by species of high ecological relevance. Among these, C. intermedia (Poaceae) is positioned as the most outstanding species, with an IVI of 12.74, reflecting a combination of high frequency and density, which suggests a wide distribution and a marked adaptation to local environmental conditions. It is followed by L. orbiculata (Rosaceae), which also has considerable ecological value, albeit with a lower frequency. The relative importance of these species points to a pattern of functional dominance that could influence the structure and dynamics of the ecosystem (Figure 4).

Other species such as P. australis and C. nubigenum have high density values but a more localized distribution, which translates into a moderate IVI, showing their relevance in specific sectors of the wetland. In contrast, species such as Solanum nigrescens and Tigridia pavonia show small values, indicating low ecological representativeness, possibly linked to a restricted distribution or specific environmental requirements.

3.2. Concentrations of Metals in Water

The quantitative chemical analysis of the water samples reveals a significant transgression of the permissible limits established by the Ecuadorian Institute of Standardization Standard for Drinking Water (INEN 1108) [54], the Official Mexican Standard for Environmental Health and Water for Human Use and Consumption (NOM-127-SSA1) [55], and the Unified Text of Secondary Legislation of the Ministry of Environment (TULSMA) [56] for various heavy metals. Specifically, the average concentration of arsenic (2.5825 mg/L) exceeds the thresholds for human consumption and preservation of aquatic biota by orders of magnitude. Similarly, chromium (0.1525 mg/L) and iron (0.623 mg/L) present values that double or triple the regulatory limits for both quality criteria. Lead (0.0106 mg/L) exhibits a slight but relevant exceedance of the standard.

Table 3. Metal concentrations in water, and compliance with regulations.

Parameters	Mean ± Standard Deviation (mgL)	Water for Domestic Consumption	Water for Flora and Fauna
		INEN 1108-NOM-127-SSA1 (Permissible Limits)	TULSMA (Permissible Limits)
As	2.5825 ± 0.1000	0.01	0.05
Cr	0.1525 ± 0.0200	0.05	0.05
Fe	0.623 ± 0.0500	0.3	0.3
Pb	0.0106 ± 0.0020	0.01	0.01
Hg	0.00381 ± 0.0005	0.006	0.0002

shows the parameters that comply and do not comply with the regulations applied to water for human consumption and for the use of flora and fauna in ecosystems.

The non-compliance of the water in the wetlands for most of the parameters analyzed implies a potential risk to both human health and the ecological integrity of the wetlands. The marked elevation in the concentration of As suggests a source of contamination of considerable impact, linked to anthropogenic activities or geochemical processes inherent in the area. The presence of chromium, iron, and lead at elevated levels points to multifactorial contamination.

3.3. Concentration of Metals in Plant Segments

Analysis of metal distribution reveals differential uptake and translocation strategies. The exclusive accumulation of Cr in the roots of C. bonplandii suggests an efficient radical uptake with limited translocation to aerial tissues, as a defense mechanism against its toxicity. In contrast, Pb was detected in all species, albeit at low concentrations, with a homogeneous distribution in T. officinale indicative of efficient translocation, while its absence in P. australis roots raises the possibility of alternative uptake pathways.

Table 4. Concentration of metals in segments of floristic species.

Name	Segment	Mean ± Standard Deviation (mgL)
		Cr	Pb	Hg	As	Fe
L. orbiculata	Root	0.00 ± 0.01	0.03 ± 0.01	1.31 ± 0.20	1.91 ± 0.15	50.00 ± 2.50
	Stem	0.00 ± 0.01	0.19 ± 0.03	0.00 ± 0.01	1.84 ± 0.14	45.00 ± 2.25
	Leaves	0.00 ± 0.01	0.12 ± 0.02	0.00 ± 0.01	1.93 ± 0.15	55.00 ± 2.75
C. bonplandii	Root	21.88 ± 1.50	0.02 ± 0.01	0.00 ± 0.01	1.99 ± 0.16	40.00 ± 2.00
	Stem	0.00 ± 0.01	0.10 ± 0.02	0.00 ± 0.01	2.09 ± 0.17	48.00 ± 2.40
	Leaves	0.00 ± 0.01	0.06 ± 0.01	0.00 ± 0.01	2.11 ± 0.17	30.00 ± 1.50
T. officinale	Root	0.00 ± 0.01	0.44 ± 0.05	0.00 ± 0.01	2.46 ± 0.20	36.00 ± 1.80
	Stem	0.00 ± 0.01	0.40 ± 0.05	0.00 ± 0.01	2.48 ± 0.20	34.00 ± 1.70
	Leaves	0.00 ± 0.01	0.39 ± 0.05	0.00 ± 0.01	2.57 ± 0.21	50.00 ± 2.50
R. acetosella	Root	0.00 ± 0.01	0.01 ± 0.01	0.00 ± 0.01	2.93 ± 0.23	58.24 ± 2.90
	Stem	0.00 ± 0.01	0.07 ± 0.01	0.00 ± 0.01	2.90 ± 0.23	67.20 ± 3.36
	Leaves	0.00 ± 0.01	0.13 ± 0.02	0.00 ± 0.01	2.99 ± 0.24	31.36 ± 1.57
C. intermedia	Root	0.00 ± 0.01	0.10 ± 0.02	0.00 ± 0.01	3.20 ± 0.25	40.32 ± 2.02
	Stem	0.00 ± 0.01	0.03 ± 0.01	0.00 ± 0.01	3.04 ± 0.24	38.08 ± 1.90
	Leaves	0.00 ± 0.01	0.11 ± 0.02	0.00 ± 0.01	3.04 ± 0.24	56.00 ± 2.76
Eleocharis sp.	Root	0.00 ± 0.01	0.16 ± 0.03	0.00 ± 0.01	3.18 ± 0.25	62.72 ± 3.14
	Stem	0.00 ± 0.01	0.00 ± 0.01	0.00 ± 0.01	3.19 ± 0.25	56.45 ± 2.82
	Leaves	0.00 ± 0.01	0.06 ± 0.01	0.00 ± 0.01	3.13 ± 0.25	68.99 ± 3.45
P. australis	Root	0.00 ± 0.01	0.00 ± 0.01	0.00 ± 0.01	3.18 ± 0.25	50.18 ± 2.51
	Stem	0.00 ± 0.01	0.43 ± 0.05	0.00 ± 0.01	3.22 ± 0.26	60.21 ± 3.01
	Leaves	0.00 ± 0.01	0.13 ± 0.02	0.00 ± 0.01	3.19 ± 0.26	37.63 ± 1.88
C. nubigenum	Root	0.00 ± 0.01	0.26 ± 0.02	0.00 ± 0.01	3.34 ± 0.27	65.23 ± 3.26
	Stem	0.00 ± 0.01	0.19 ± 0.03	0.00 ± 0.01	3.26 ± 0.26	75.26 ± 3.76
	Leaves	0.00 ± 0.01	0.11 ± 0.02	0.00 ± 0.01	3.29 ± 0.26	35.12 ± 1.76

shows the concentration of metals in three segments (roots, stems, and leaves) of the eight floristic species of the bofedales. The marked accumulation of Hg in the roots of L. orbiculata contrasts with the absence of translocation to stems and leaves, suggesting a radical retention mechanism to protect photosynthetic tissues. Despite the high concentration of As in water, plants showed moderate concentrations with a uniform distribution in their different parts, implying efficient uptake and translocation, but with a possible limitation in total accumulation as a tolerance strategy.

Finally, Fe showed high concentrations in all species, reflecting its bioavailability and generalized absorption capacity, although the variability in distribution among roots, stems, and leaves suggests interspecific differences in translocation and storage, linked to the particular physiological needs of each species in the bog.

3.4. Bioaccumulation Index (BAI) and Tolerance of Each Species to Metals

Table 5. Calculation of the bioaccumulation index in the eight floristic species.

Species	Cr	Pb	Hg	As	Fe
L. orbiculata	0.0	10.6	114.6	0.7	80.3
C. bonplandii	47.8	5.6	0.0	0.8	63.1
T. officinale	0.0	38.5	0.0	1.0	64.2
R. acetosella	0.0	6.7	0.0	1.1	83.9
C. intermedia	0.0	7.6	0.0	1.2	71.9
Eleocharis sp.	0.0	6.7	0.0	1.2	100.7
P. australis	0.0	17.8	0.0	1.2	79.2
C. nubigenum	0.0	17.3	0.0	1.3	94.0

shows how the BAI in various plant species exhibits a marked selectivity in heavy metal uptake. Specifically, C. bonplandii showed a high capacity for Cr bioaccumulation, with a BAI of 47.8, while the other species did not exhibit any uptake. It is noteworthy that Pb bioaccumulated in all of the species analyzed, with particularly high values in T. officinale and P. australis, despite its low concentrations in the water.

Hg presented an extreme and specific bioaccumulation pattern with L. orbiculata, indicating a unique capacity to concentrate this metal present at low aquatic concentrations. On the other hand, As, despite its high concentration in the waters of the wetland, showed low BAI values in all species, suggesting the presence of tolerance or exclusion mechanisms. Fe stood out for being highly bioaccumulated by all of the species studied, with BAIs ranging from 63.1 to 100.7, and with Eleocharis sp. being the species with the highest accumulation.

The observed variability in bioaccumulation between species and metals underlines the complexity of plant–pollutant interactions, and the high accumulation of Pb and Hg in certain species could imply risks for the food chain through consumption by herbivores. The limited bioaccumulation of As, despite its high availability, suggests physiological adaptations of these plants to As toxicity.

Regarding the tolerance of each species to metals, L. orbiculata and C. bonplandii exhibited high tolerance to mercury and chromium, respectively, through root retention as the main strategy to limit transfer to photosynthetic tissues. On the other hand, T. officinale and P. australis showed remarkable tolerance to lead, with homogeneous distribution in their tissues or preferential accumulation in stems and leaves, suggesting active translocation and detoxification mechanisms.

Iron tolerance was a common feature in most species, with high bioaccumulation rates of this metal in Eleocharis sp., R. acetosella, and C. nubigenum, reflecting their adaptation to soils rich in this essential element. The zero or low accumulation of certain metals, such as Cr and Hg in C. intermedia, Eleocharis sp., and C. nubigenum, indicates the presence of efficient exclusion mechanisms, possibly through biochemical barriers or selective regulation of ion transport in the roots. These strategies, which include the synthesis of phytochelatins and antioxidants, allow plant species to mitigate toxicity, highlighting the diversity of adaptations that support survival in heavy metal environments.

3.5. Principal Component Analysis

The analysis reveals significant patterns of heavy metal accumulation across the four main axes, which explain 91.8% of the total variance (Figure 5).

Axis 1 (32.96%) is characterized by As, showing a strong accumulation in the roots of Eleocharis sp. and C. nubigenum species, as well as in the leaves of C. intermedia. This suggests that these species are exposed to soils with high As concentrations, with the roots acting as the main uptake site. In addition, Cr also contributes to this axis, with notable accumulation in the roots of C. bonplandii.

Axis 2 (23.232%) is characterized by Pb, with significant accumulation in the parts of T. officinale and R. acetosella, highlighting them as bioaccumulator species. Some Fe accumulation is also observed in the leaves of Eleocharis sp. and in the stems of R. acetosella, suggesting a combined exposure to multiple metals in these species.

Axis 3 (21.05%) is dominated by Cr and Hg. The roots of C. bonplandii accumulate Cr, while the roots of L. orbiculata show accumulation of Hg. This reflects multifactorial contamination in their habitat (Figure 6).

Axis 4 (14.597%), characterized by Fe, highlights the accumulation of this metal in the stems and roots of C. intermedia, and also in the leaves of R. acetosella, C. nubigenum, and P. Australis, suggesting that Fe is a common element in many plant parts.

3.6. Cluster Analysis

Cluster 1 shows similar accumulation patterns for Cr and Pb, indicating that these metals tend to accumulate together in certain parts of plants, mainly in the roots of C. bonplandii, which have extremely high values for Cr and Pb. It should also be noted that the stems of P. australis and C. nubigenum have moderate Pb accumulation (Figure 7).

Cluster 2 has lower accumulations of Cr and Pb but shows some accumulation of Fe and As. The roots of C. intermedia have a high value for Fe, and the roots of Eleocharis sp. have a low value for As, indicating lower relative accumulation. This cluster also suggests that the leaves of C. nubigenum, R. acetosella, and P. australis, along with the roots and stems of C. intermedia, accumulate Fe and As more moderately.

Cluster 3 has low accumulations of all metals, especially L. orbiculata, which shows low negative values for some metals. This indicates that L. orbiculata and aerial parts of C. bonplandii have medium accumulation of specific heavy metals.

3.7. Discriminant Analysis

Table 6. Ranking function coefficients (Function 1).

Elements	Species
	L. orbiculata	C. bonplandii	T. officinale	R. acetosella	C. intermedia	Eleocharis sp.	P. australis	C. nubigenum
Cr	4.5	5.3	5.8	6.7	7.0	7.2	7.3	7.5
Pb	−98.8	−117.6	−97.1	−169.6	−174.8	−184.8	−167.9	−176.0
Hg	−11.4	−21.1	−22.2	−32.3	−33.9	−35.1	−33.7	−35.0
As	861.0	957.6	1119.9	1339.7	1405.2	1445.4	1447.4	1495.8
Fe	1.1	1.1	1.19	1.544	1.5	1.7	1.588	1.6

shows how the values of the coefficients of the linear discriminant analysis (LDA) classification function classified eight plant species using concentrations of five metals; the model generated six discriminant functions, of which the first two explain 99.5% of the variance.

Function 1, which explains 98.2% of the variance, with a canonical correlation of 0.997, is dominated by arsenic (As), with a standardized coefficient of 1.147 and a correlation of 0.866. This function clearly separates L. orbiculata and Carex bonplandii, with low concentrations of As, from C. nubigenum and P. australis, with high concentrations. Function 2, which contributes an additional 1.3% of the variance (canonical correlation of 0.841), is influenced by lead (Pb) (coefficient = 1.034, correlation = 0.886), with T. officinale standing out for its high Pb concentrations.

4. Discussion

4.1. Implications of Metal Contamination for Wetland Plant Species

Wetlands, dominated by Poaceae and Cyperaceae, are key ecosystems for ecological balance, but they face pressures due to heavy metal contamination. This contamination induces oxidative stress and physiological alterations that affect the growth, reproduction, and structure of vegetation, decreasing biodiversity and favoring tolerant species [57,58,59]. Some species develop hyperaccumulation mechanisms, although excessive contamination reduces the vitality of the flora and modifies the composition of plant communities, evidencing the vulnerability of these ecosystems [60,61].

The presence of heavy metals above regulatory limits represents environmental and health risks, with bioaccumulation in the trophic chain that threatens human health through the consumption of contaminated agricultural products and livestock [62]. These contaminants, which are non-biodegradable, are associated with carcinogenic effects, neurotoxicity, and immunosuppression [63,64]. Low floristic richness and biodiversity loss reflect the ecological impact, requiring rigorous monitoring and updating of regulatory frameworks to mitigate adverse effects through interdisciplinary strategies [65,66,67].

4.2. Bioaccumulation

The bioaccumulation of heavy metals in plants shows heterogeneous patterns, with marked differences between metals and species. As shows low bioaccumulation rates, mainly in roots, suggesting specific regulatory mechanisms for its uptake and root retention [68]. In contrast, Pb and Hg exhibit more pronounced accumulation dynamics; for example, T. officinale shows efficient bioaccumulation and translocation of lead, increasing the risk of trophic transfer [69,70]. The high accumulation of mercury in certain species highlights ecological concerns due to its potential for ecosystem impact. Accumulation profiles are determined by specific retention and translocation mechanisms, as in Carex bonplandii, which selectively retains Cr in its roots, possibly as a form of protection against toxicity in photosynthetic tissues [70]. Fe, on the other hand, accumulates widely due to its bioavailability and metabolic role, reflecting the influence of environmental and phylogenetic factors on uptake strategies [69].

4.3. Group Identification, Discriminant Markers, and Complex Relationships

Wetlands face ecological alterations due to the accumulation of heavy metals that modulate plant uptake and generate trophic bioaccumulation risks for herbivores and humans [71,72]. Root uptake is the main route of incorporation, with some species showing tolerance to As and Pb through biochemical adaptations, such as the synthesis of compounds that mitigate toxicity [73,74]. However, exposure to these metals induces physiological stress, with increased reactive oxygen species (ROS), implying metabolic costs that affect plants’ growth and reproduction [75,76]. Discriminant analysis highlights that As and Pb concentrations significantly differentiate wetland and páramos species, reflecting distinct ecological niches [77].

The bioavailability of Pb, limited by its binding to organic matter, favors its accumulation in roots, with specific tolerance in wetland species adapted to high levels [78,79]. As, influenced by interactions with iron oxides, shows greater accumulation in specific species, possibly due to anthropogenic or geological sources [80]. Competitive interactions, such as between Fe and As or Pb and Cr, are key to understanding effects on plant health and diversity [81]. Concentrations of Pb, Hg, and As underscore the need for continuous monitoring and management strategies to mitigate risks to livestock and human health, integrating environmental and agricultural health measures in Andean regions.

The accumulation of heavy metals such as lead (Pb), mercury (Hg), and chromium (Cr) in wetlands represents a trophic risk due to their transfer to aerial parts of plants, promoting bioaccumulation in the food web and affecting the health of terrestrial and aquatic organisms [82,83]. Species such as C. bonplandii and L. orbiculata stand out for their ability to absorb Hg and Cr, showing potential for phytoremediation by acting as biological filters without compromising their vitality [84,85].

In addition, macrophytes such as Azolla filiculoides employ rhizofiltration and phytoaccumulation mechanisms to stabilize pollutants, improving the water quality and minimizing leaching [86,87]. These findings underscore the importance of investigating hyperaccumulating species to optimize remediation strategies in wetlands affected by industrial runoff, strengthening the conservation of these ecosystems [88,89].

4.4. Limitations and Future Prospects

A fundamental limitation of this study lies in the absence of a direct analysis of heavy metal concentrations in the soil of the bofedales. This omission restricts the ability to differentiate the relative contributions of metals dissolved in water versus those present in the soil matrix in the bioaccumulation processes of plant species. Although bofedales are predominantly aquatic ecosystems, the volcanic nature of their soils, characterized by an abundance of organic matter and iron oxides, suggests that they may function as significant reservoirs for elements such as arsenic (As), lead (Pb), and chromium (Cr), directly influencing their bioavailability to biota. Consequently, the interpretation of the bioaccumulation index (BAI) is biased, as it currently primarily reflects absorption from the aquatic compartment, without considering possible uptake from the soil, which could lead to an underestimation or overestimation of the actual capacity of plants to accumulate metals.

To address this limitation, the subsequent phase of this project will incorporate the analysis of heavy metals in soil samples and their correlation with the levels detected in plant tissues. This methodological expansion will allow for a readjustment of the BAI, providing a more accurate representation of the combined bioaccumulation of metals from both compartments: water and soil. In addition, metal speciation studies will be implemented to assess their bioavailable fraction, and the complex interactions between plants, soil, and water will be investigated. These analyses will enable a comprehensive understanding of tolerance and bioaccumulation patterns in these wetland ecosystems, which is essential for their conservation and sustainable management.

5. Conclusions

The high Andean wetlands of Ichubamba Yasepan are home to a plant community with moderate floral richness, with 25 species from 14 botanical families, where C. intermedia (IVI 12.74), C. bonplandii (IVI 6.57), and Eleocharis sp. (IVI 4.04) stand out for their ecological dominance, reflecting their structural role in this ecosystem. The lower representation of other families suggests specialized ecological niches, influenced by the extreme conditions of the páramos.

Water analysis revealed severe heavy metal contamination, with concentrations of arsenic (2.5825 mg/L), chromium (0.1525 mg/L), iron (0.623 mg/L), and lead (0.0106 mg/L) that far exceed regulatory limits for human consumption and biota preservation, while mercury (0.00381 mg/L) exceeds the standards for ecosystem protection. This contamination, probably derived from anthropogenic sources (intensive agriculture) and natural sources (volcanic activity of Sangay), represents a significant threat to biodiversity and ecosystem services.

The eight species studied exhibited differential patterns of bioaccumulation and tolerance to heavy metals, evidencing specific physiological adaptations. C. bonplandii showed high tolerance to chromium (BAI 47.8), accumulating it exclusively in the roots through root retention, which minimizes toxicity in photosynthetic tissues. L. orbiculata demonstrated exceptional tolerance to mercury (BAI 114.6), restricting it to the roots with minimal translocation, possibly through sulfide complexes. T. officinale and P. australis stood out for their tolerance to lead (BAI 38.5 and 17.8), with efficient translocation to stems and leaves, suggesting antioxidant mechanisms but posing risks of trophic transfer. C. intermedia, R. acetosella, Eleocharis sp., and C. nubigenum exhibited moderate tolerance to lead (BAI 6.7–17.3) and arsenic (BAI 0.7–1.3), with variable distribution in tissues, indicating combined strategies of retention and controlled translocation. All species showed low accumulation of arsenic despite its high concentration in water, suggesting exclusion mechanisms, and high accumulation of iron (BAI 63.1–100.7), reflecting its abundance and metabolic role. Linear discriminant analysis (LDA) confirmed the differentiation between species based on As and Pb concentrations, highlighting the specificity of their physiological responses.

These pioneering contributions highlight the phytoremediation potential of C. bonplandii and L. orbiculata for chromium and mercury, respectively, due to their high tolerance and selective accumulation in roots, offering viable solutions to mitigate contamination in wetlands. Lead tolerance in T. officinale and P. australis, although promising for phytoextraction, requires caution due to trophic risk. The lack of soil analysis and isotopic tracers to confirm sources of contamination represents a limitation that will be addressed in future phases of this project through geochemical and chemical speciation studies. This work establishes a solid foundation for wetland conservation, providing critical data for the design of phytoremediation strategies and the sustainable management of Andean protected areas, with implications for the protection of biodiversity and water services essential to local communities.