Next Article in Journal
Analysis of Soil Carbon Stock Dynamics by Machine Learning—Polish Case Study
Previous Article in Journal
The Relationship between Rural Spatial Form and Carbon Emission—A Case Study of Suburban Integrated Villages in Hunan Province, China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 12
Issue 8
10.3390/land12081586
land-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Water Quality Determination Using Soil and Vegetation Communities in the Wetlands of the Andes of Ecuador

by
Juan Carlos Carrasco Baquero
1,2,*,
Verónica Lucía Caballero Serrano
1,
Fernando Romero Cañizares
3,
Daisy Carolina Carrasco López
4,
David Alejandro León Gualán
5,
Rufino Vieira Lanero
2 and
Fernando Cobo-Gradín
2
1
Faculty of Natural Resources, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba EC 060155, Ecuador
2
Department of Zoology, Xenétic and Antropoloxía Física, Faculty of Bioloxía, Univesity Santiago de Compostela, 15782 Santiago de Compostela, Spain
3
Independent Researcher, Riobamba EC 060155, Ecuador
4
Investigation Institute, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba EC 060155, Ecuador
5
Campus Economics and Finance, University of Research and Innovation of Mexico, UIIX, Cuernavaca 62290, Mexico
*
Author to whom correspondence should be addressed.
Land 2023, 12(8), 1586; https://doi.org/10.3390/land12081586
Submission received: 11 June 2023 / Revised: 31 July 2023 / Accepted: 8 August 2023 / Published: 11 August 2023
Browse Figures
Versions Notes

Abstract

:
The bofedales are high Andean ecosystems of great socioeconomic and ecological importance. The Chimborazo Fauna Production Reserve has 15 bofedales in its jurisdiction, located in the provinces of Chimborazo, Bolívar, and Tungurahua. The objective of this study was to establish the relationship between plant species composition and the physicochemical characteristics of water and soil. To determine the floristic composition, destructive sampling of species was applied, and three sampling points of 1 m2 were established every 100 m per wetland. At each sampling point, physical-chemical variables were recorded in situ and in the laboratory for water and soil. The floristic analysis identified 78 riparian species of riparian plants (63 vascular, 12 bryophytes, 4 pteridophytes) and 1 lichen. In the aquatic environment, seven vascular plants, recognized as macrophytes, were recorded. The results show great heterogeneity in the soil, water, and vegetation characters because they respond to a mineralization gradient (as indicated by the high values of electrical conductivity and dissolved ions). Additionally, it was observed that the total amount of soluble solids that characterizes the Los Hieleros wetland (W11) is independent of hardness and chemical oxygen demand, which correlate with each other and, in turn, better describe the Pachancho wetland (W12). The highest degree of turbidity corresponds to the Cóndor Samana (W9) and Portal Andino (W10) wetlands. The Culebrillas (W6), Puente Ayora ANI (W14), and Pampas Salasacas (W1) wetlands are characterized by the presence of dissolved oxygen, so it is assumed that these are the wetlands with the best water quality. Consequently, it is imperative to double efforts to describe the ecology and status of these high Andean wetlands in order to promote their conservation.

1. Introduction

There is an urgent need to identify strategies for the preservation, restoration, and management of ecosystems [1]. Population growth, expansion of the agricultural and livestock frontiers, and industrial development worldwide are exerting strong pressures on natural ecosystems, especially aquatic ecosystems [2,3].
Wetlands are highly productive ecosystems [4,5] and comprise 8.5% of the Earth’s land surface [6]. They cover a total area of 12.1 million km2 and account for 40.6% of the total value of ecosystem services (ES) [7,8].
The 1999 wetland classification of the Ramsar Convention identifies wetlands as non-forested peatlands [9]. Their main functions include water pollution treatment, biogeochemical cycling adjustment, drought control, climate change mitigation [10], and contribution to the Earth’s sustainability [11,12]. The ecological characteristics of these ecosystems are grouped into components. Functions and properties [13]; the components being biotic and abiotic conditions such as soil, water, and animals [14,15,16], so they tend to be very dynamic with constantly changing energy reserves [17].
The wetlands of the Andean tropics are in the high Andes Mountain range, at an altitude of more than 3.000 m.a.s.l. [18]. In Ecuador, there are 13 Ramsar wetlands [19] and 59 peatland-type ecosystems (known as bofedales in the Ecuadorian Andes) covering an area of 286.659 hectares and distributed throughout the continent [20]. These ecosystems include those wetlands and wetland complexes that are part of the páramo, jalca, and puna ecosystems, as well as other high Andean and related ecosystems [21], most of which are in protected areas that seek to conserve biodiversity [22]. The coverage of terrestrially protected areas is increasing every year and currently covers just over 15% of the land area [23,24]. In Ecuador, protected areas represent approximately 20% of the national territory [25].
The Chimborazo Fauna Production Reserve (RPFCH) is part of the National System of Protected Areas of Ecuador and is located in the provinces of Chimborazo, Bolivar, and Tungurahua. Altitudes in the reserve range from 3.800 to 6.310 m.a.s.l. [25]. The RPFCH covers 58.560 hectares [20,26], of which 39% are wetland-type ecosystems: 24% of the wetland ecosystem is in the intervened category, 12% is moderately conserved, and the remaining 3% is conserved [20].
Abiotic conditions, such as soil. Plant hydrology and water chemistry are the decisive factors in the pattern of wetland ecology [14,15,16,27,28,29]. Plants are the main primary producers, playing an important role in the maintenance and stability of these ecosystems [30,31]. Submerged wetland plants (macrophytes) provide a variety of ecological functions and services, such as providing substrate for algae and invertebrates [32] and influencing biogeochemical cycles and productivity [33]. However, plants, similar to other components of aquatic ecosystems, currently face increasing anthropogenic threats [34].
Several studies have shown that soil nutrients are one of the main factors affecting plant productivity [35]. Changes in the types and variations of plant functional traits [36,37] are decisive factors in the regulation of soil functions [38], because coexisting species with contrasting trait values increase overall resource acquisition and use through complementary niche effects [39,40]. The loss of plant species diversity caused by changes in land-use intensity leads to a reduction in individual soil functions, such as soil nitrogen and water retention [41,42].
For some time, researchers have been trying to determine the influence of vegetation cover on soil and water conditions [43]. This study evaluates the relationship between biotic (plants) and abiotic (soil-water) components of the wetlands located in the Chimborazo Wildlife Production Reserve (RPFCH), thus understanding the correspondence between vegetation and the environment and allowing the development of programs focused on the protection and conservation of these high Andean ecosystems.

2. Materials and Methods

2.1. Study Area

The study was conducted in 15 bofedales of the RPFCH in the interior of the Andes, with a temperature ranging from −3 to 14 °C and an average annual precipitation of 1000 mm and a humidity percentage of 70–85% [25].
The vegetation cover is formed by mixed natural communities of peatlands, sporadic water quinielas, and buffer vegetation, resulting in a deep and peaty organic soil.
The bofedales are located between 3840 and 4314 m.a.s.l. in the provinces of Bolivar (6 bofedales), Chimborazo (3 bofedales), and Tungurahua (6 bofedales), where they cover areas ranging from 2 to 155 ha (Figure 1).

2.2. Floristic Sampling and Inventory

Field work was carried out in September 2018 and February 2019. Sampling units were distributed for each bofedal: point one (P1) in the upper zone of the bofedal, point two (P2) in the intermediate zone, and point three (P3) in the lower zone of the bofedal. They were then georeferenced (Appendix A) using a GARMIN OREGON 650 GPS (Garmin Iberia S.A.U., Barcelona, Spain).
Plots of 1 m2 [44] were established along the slope of the bofedales between 3825 and 4240 m.a.s.l. A total sweep was made within the plot, considering the edge error and keeping the conditions within the unit intact to carry out subsequent measurements.
Species identification was carried out in two authorized herbariums in Ecuador: the herbarium of the Department of Biological Sciences of the Pontificia Universidad Católica del Ecuador in Quito (QCA Herbarium) and the herbarium of the Escuela Superior Politécnica de Chimborazo (CHEP).

2.3. Measurement of Selected Physicochemical Variables: Collection and Analysis of Water Samples

All measurements and water sample collection were performed randomly in duplicate; in each wetland, two liters of water were taken. Values of pH, temperature (°C), dissolved oxygen DO (mg/L) and electrical conductivity EC (μS/cm), were measured in situ, using a pH meter (PCE-PH22—Apera Instruments, Wuppertal, Germany), a portable oximeter (HI9146-04—HANNA Instruments, Limena, Italy), and a portable multiparameter probe (MM40—Crison Instruments, Barcelona, Spain).
Water samples (2 L each) were collected using glass bottles (1000 mL) and placed in portable coolers at −10 °C without preservatives. They were then sent to the Water-Industrial Effluents Environmental Analysis Laboratory (LASA-Quito) to be analyzed according to the standard method APHA (Table 1) [45]. The parameters analyzed were pH; Temperature (Temp. °C); Ammonium (NH4. mg/L); Calcium (Ca. mg/L); Electrical conductivity (Cond. uS/cm); Biological oxygen demand (BDO. mg/L); Chemical oxygen demand (C.O.D. mg/L); Hardness (mg CaCO3/l); Phosphorus (P. mg/L); Magnesium (Mg. mg/L); Nitrates (mg/L); Nitrites (mg/L); Dissolved oxygen (Diss. O. mg/L and %); Totally suspended solids (TSS, mg/L); and Sulfates (mg/L).

2.4. Soil Sampling and Analysis

A two-dimensional sampling was performed since peatlands have an irregular shape of less than 1000 m2. Six samples were taken/peatland; the distribution per sample was 1 every 15 linear meters (4) and at the bottom of the peatland (2) at a depth of 30 cm. A total of 96 samples were collected for analysis of granulometry and organic matter. The samples were collected in wide-mouthed glass jars with lids and Teflon seals and transported to the Soil Laboratory of the Faculty of Natural Resources of the Escuela Superior Politécnica de Chimborazo, where they were analyzed following the methodology of the Soil Analysis Manual of the Soil Laboratory Network of Ecuador [46].

2.5. Data Analysis

Statistical analyses on the floristic and physical composition of wetland soil and water chemistry data were performed using R statistical software version 3.3.1 [47].
To detail the key variables explaining the high variability in the dataset. Principal Component Analysis (PCA) [48] with the packages FactoMineR version 2.8 [49] and ggbiplot version 3.4.2 [50] was applied to standardized soil and water variables. This allowed for the selection of physicochemical variables (e.g., Nitrites, Calcium, Magnesium, Conductivity, Hardness, pH, Electrical Conductivity, Phosphorus, and Potassium) and physical habitat attributes, while reducing the dimensionality of the data set. Biplots were made for the first two components based on the resulting scores and loadings that provided an overview of the relationships between multiple variables and sites with the highest level of intervention within the protected area [51,52].
With an integrating approach and with the desire to obtain greater discrimination of the data, the HJ-Biplot [53] multivariate analysis was carried out in MultBiplot Software.
Biplot analysis is a procedure for the simultaneous graphic representation of the rows and columns of a matrix, which allows summarizing the information of a matrix of rank r in a space of dimension q less than r. The Biplot that absorbs the greatest possible information, in terms of variability, of a matrix X of rank r is the one corresponding to the matrix of rank q, which constitutes the low-rank approximation of X, which is obtained from the decomposition in singular values of X [54] as:
X(q) = U(q) D(q)λVT(q)
where U(q) is the matrix, whose columns contain the first q eigenvectors of XXT. D(q)λ is the diagonal matrix with the first q singular values of X, and V(q) is the matrix containing the q first eigenvectors of XTX. This expression also corresponds to the singular value decomposition of X(q). There are two classic options to achieve better quality representation of either the columns (GH) or the rows (JK).
Galindo [53,54,55] proposes taking A = U(q) D(q)λ y B = D(q)λ VT(q). The Biplot thus constructed was called HJ-Biplot by its author, respecting the logic of the names proposed by Gabriel 1971. Its main characteristic is that both the rows and the columns reach the highest quality of representation. In this case, it is obvious that the internal product of the vector markers will not reproduce the data of the starting matrix, even retaining the q dimensions. However, this is not a problem since the objective is generally not to reproduce the original data but to obtain a simultaneous approximation of the rows and columns of X in which both are well represented.

3. Results

3.1. Floristic Inventory

The floristic inventory (Table 2) identified 85 plant species, of which 72 species (85%) are vascular and 13 species (15%) are non-vascular. Only seven of the species (7.5%) have aquatic characteristics. The most abundant family was Asteraceae, with 15 species. Poaceae with 7 species and Apiaceae with 5 species are among the most representative. The greatest percentage of the species identified (85%) are native, with a distribution area restricted to the moorlands of the center and south of the country; the place of origin of 7% of the species could not be identified; 4% of the species have been introduced in the areas; while the remaining 4% are endemic species of the country: Halenia pulchella, Gnaphalium chimboracense, and Nototriche hartwegii, the same ones that, according to the Red Book of Endemic Plants of Ecuador [56], are in states of least concern (with a population number of 30), vulnerable (with a population number of 2), and the last one in endangered status (with a population number of 2), respectively.

3.2. Physicochemical Analysis of Water

Most of the physicochemical variables (Table 3) showed variations between groups and differed by more than one or two orders of magnitude. The Unified Text of Secondary Environmental Legislation (TULSMA) mentions that the established value for this parameter should be between 6.5 and 9.0 pH units; in this study, all the samples analyzed met this value. remaining in the neutral range and demonstrating the absence of substances that could affect it. The water temperature at the sampling sites ranged from 7 to 11 °C (45 to 52 °F). Ammonium concentrations differed greatly in the wetlands in amounts varying between 0.05 and 9.9 (mg/L), while calcium (Ca. mg/L) ranged between 1 and 20 (mg/L). Conductivity remained in the range of 14–289 (µS/cm). being the highest reported in the Casa Condor BI wetland. The concentrations of the chemical parameters, including nitrate, remained in a range of <0.70 (mg/L) during the study period. Nitrite values were less than 0.002 (mg/L), and dissolved oxygen concentrations were between 6 and 7 (mg/L), which corresponded to elevated B.O.D. concentrations.
Concentrations of nutrients that potentially limit primary production, such as phosphorus (P) and nitrates, were very low and, in many cases, below the detection limit (less than 0.01 mg/L in 75% of the cases for total phosphorus and 93% for nitrates (Table 3). According to our physicochemical data, most of the bogs can be considered minerotrophic peatlands [57,58,59].

3.3. Soil Analysis

The variables analyzed show the values included in Table 4.

3.4. Characterization of Sites with Soil and Water Variables

The multivariate analysis of 31 variables related to physical and physical-chemical aspects of water and soil quality was analyzed with the HJ-Biplot methodology, showing that three axes explained 89.42% of the total variance (Table 5). Axes 1 and 2 explain 70.81% of the variance, so the results will be analyzed with these 2 axes (Figure 2).
The variables that best contribute to axis 1 are those corresponding to Altitude, Surface, and Nitrates, which make up the physical component. The other variables are located on axis 2, which we identify as the bio-physical component. The most outstanding water and soil quality indicators are: nitrites, calcium, magnesium, conductivity, hardness, pH, electrical conductivity, phosphorus, potassium content, and granulometries >1, >0.5, and >0.25 (Table 6).
According to the Biplot analysis of the Chimborazo Fauna Production Reserve wetlands, the variables hardness, conductivity, and electrical conductivity, which are highly correlated, are independent of altitude above sea level. The Mechahuasca (W3), Cruz del Arenal ANI (W5), Culebrillas (W6), and Puente Ayora ANI (W14) wetlands are effectively those located at an altitude above 4100 m.a.s.l. Pampas Salasacas (W1), Puente Ayora BNI (W13), Lazabanza (W8), and Cruz del Arenal BNI (W4) are the lowest and do not have representative variables that group them together except for altitude (Figure 3).
To find an additional configuration, we excluded the variables altitude, conductivity, and electrical conductivity, which were the most representative in the global analysis, and observed that the total amount of soluble solids that characterizes the Hieleros wetland (W11) is independent of hardness and chemical oxygen demand, which are correlated with each other and better describe the Pachancho wetland (W12). The highest degree of turbidity corresponds to the Cóndor Samana (W9) and Portal Andino (W10) wetlands. The Culebrillas (W6), Puente Ayora ANI (W14), and Pampas Salasacas (W1) wetlands are characterized by the presence of dissolved oxygen, so it is assumed that these are the wetlands with the best water quality. The other wetlands do not have outstanding variables that allow their discrimination since they share similar values (Figure 4).

4. Discussion

In general, the 16 bofedales of the Chimborazo Fauna Production Reserve present a similar number of species. with a total of (63 vascular, 12 bryophytes, and 4 pteridophytes) and 1 lichen, belonging to 64 genera and 35 families; a pattern typical of Andean paramos that are characterized by a floristic diversity richer in species than that of any other tropical-alpine ecosystem [60,61,62].
Bofedales are usually complexes of different plant communities whose composition and abundance are related to the amount and availability of water [63]. Vegetation is directly related to macroinvertebrate microenvironments [64]. Several authors have suggested that compositional changes in vegetation are mainly determined by the elevation gradient [65,66].
In this study, we determine the relative contribution of variables driven by natural impact [67] and the effect of environmental filters (water and soil), considered decisive factors in shaping plant diversity patterns and the ecology of these bofedales in general [14,15,16,27,28,29]. As shown by studies by Scheffer [68], macrophyte cover and diversity contribute to the structural heterogeneity of the aquatic environment and thus may be guiding factors for system functioning [69,70] and the abundance and diversity of higher trophic levels [71].
In this study, soil properties between habitats were markedly different. To find a possible structure in the variability of the database, a principal component analysis (PCA) was performed. This analysis showed that the first three dimensions explained 89.42% of the total variation in the data. Thus, it was shown that in the relative contribution of soil Sulfates and Biochemical Oxygen Demand had the lowest loadings in PC1, while Altitude, Electrical Conductivity, Potassium, and Phosphorus had the highest loadings in PC2.
The latter improves the efficiency of soil microbial decomposition [34]. Such a trend could be the result of higher plant biomass and high nutrient content [72]. In their study, Scheffer [68] determined that soil cover was a more useful indicator than diversity indices or plant community composition in terms of water requirements.
However, this result contrasts with the findings of research conducted in cultivated soils and with the presence of afforestation. For example. Yang et al. [73] showed that in cultivated land, OC. TP. C/N, and OP levels predominate, unlike the studies of Yu et al. [74] and Fang et al. [75].
A fundamental aspect of aquatic systems are the abiotic characteristics of the water, which are generally influenced by the nature of the substrate; however, some may have variations related to the increase of organic matter. In this study, in terms of water circulation, temperature did not vary significantly between wetlands. However, this parameter is closely related to dissolved oxygen and BOD; bacteria and microorganisms develop rapidly in warm water; at cold temperatures, the concentration of dissolved oxygen is higher and the probability of survival of aquatic species is greater [76].
Conductivity remained in the range 143.50–209 µS/cm being the highest reported in the Casa Condor BI wetland; it corresponds to the hardness of water with high calcium content [77]. The concentrations of n-nitrates in the samples analyzed were less than 0.70 mg/L suggesting that the contribution of discharges of this compound is minimal. Research carried out in Uruguay for surface waters reports concentrations of less than 2 mg/L of n-nitrate, thus reporting that levels of less than 3 mg/L could be considered characteristic of natural waters [78,79]. Nitrate showed a tendency to be negatively correlated with aquatic plant cover and aquatic plant species richness. For example, a study by Coronel [80] indicated that concentrations of this nutrient appeared to be determined by aquatic plants rather than the nutrient limiting vegetation growth.
Dissolved oxygen is an indicator of organic matter contamination; low concentrations of this parameter can be located where organic matter is decomposing, meaning that bacteria that use oxygen to break down waste are also low in warm, slow-moving waters [81]. Waters with dissolved oxygen concentrations above 4.1 mg/L are considered good quality; in the RPFCH wetlands, DO concentrations remained above 6.11 mg/L [75].
Practically, for the PCA with water parameters in the wetlands, Ca presented the highest loads, while pH showed the lowest loads, perhaps because the main sources of hydrogen ions supplied to the wetlands are the result of rainfall runoff input, nitrogen immobilization, carbonic acid dissolution, organic acid dissociation, and sulfur oxidation in low water conditions [28,82], as demonstrated in a study by Yabe et al. [83], where pH values gradually decreased due to the above factors.
Both the chemical characteristics of the water and the aquatic plant communities present in the bofedales of the Chimborazo Fauna Production Reserve seem to respond to a mineralization gradient (as indicated by high values of electrical conductivity and dissolved ions). From a conservation point of view, the wetlands studied harbor an important percentage of the country’s native plants. In addition, due to the geographic location of the wetlands of the RPFCH, these areas offer an ideal system for the study of meta-communities (dispersal-linked communities) [84].

5. Conclusions

This research focused on establishing the relationship between plant species composition and the physicochemical characteristics of water and soil. Seventy-nine plant species were identified (62 vascular, 12 bryophytes, 4 pteridophytes, and 1 lichen). In the aquatic environment, seven vascular plants, recognized as macrophytes, were recorded. The results show a great heterogeneity in the soil, water, and vegetation characters, as they respond to a mineralization gradient (as indicated by the high values of electrical conductivity and dissolved ions). Additionally, it was observed that the total amount of soluble solids that characterizes the Los Hieleros wetland (W11) is independent of hardness and chemical oxygen demand, which correlate with each other and better describe the Pachancho wetland (W12). The highest degree of turbidity corresponds to the Cóndor Samana (W9) and Portal Andino (W10) wetlands. The Culebrillas (W6), Puente Ayora ANI (W14), and Pampas Salasacas (W1) wetlands are characterized by the presence of dissolved oxygen, so it is assumed that these are the wetlands with the best water quality. Consequently, it is imperative to redouble efforts to describe the ecology and status of these high Andean wetlands in order to promote their conservation.

Author Contributions

Conceptualization. J.C.C.B., V.L.C.S. and D.C.C.L.; Formal analysis. J.C.C.B., D.A.L.G. and F.R.C.; Investigation J.C.C.B., V.L.C.S., D.C.C.L., R.V.L. and F.C.-G.; resources J.C.C.B. and V.L.C.S.; Writing—original draft preparation. J.C.C.B., V.L.C.S., D.C.C.L., R.V.L., F.R.C. and F.C.-G.; Writing—review and editing. J.C.C.B., V.L.C.S., D.A.L.G. and R.V.L.; Supervision. J.C.C.B. and F.C.-G.; Funding acquisition J.C.C.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The research was funded by the Escuela Superior Politecnica de Chimborazo.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors thank our collaborators at ESPOCH and the Departamento de Zooloxía. Xenética e Antropoloxía Física. Facultade de Bioloxía. Universidad de Santiago de Compostela.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Characterization of wetlands.
Table A1. Characterization of wetlands.
BofedalProvinceLatitudeLongitudeAltitude (m.a.s.l.)Total Area (ha)Ecological Classification
Los Hieleros ANIChimborazo745,7419,833,916444225.67Subnival evergreen moorland grassland and shrubland
Culebrillas IAChimborazo735,4469,831,848416013.31Páramo flooded grassland
Casa Cóndor BIChimborazo739,2449,831,67240089.40Páramo flooded grassland
Lazabanza BNITungurahua746,7349,850,338403926.46Subnival moorland humid grassland
Cóndor Samana BITungurahua751,1099,839,489382521.36Páramo upper montane moist upper montane grassland
Pampas Salasacas BITungurahua754,9729,845,2833854154.40Páramo upper montane moist upper montane grassland
Río Blanco AITungurahua746,1799,849,003401665.44Evergreen shrubland and moorland grassland
Mechahuasca ANITungurahua743,9549,844,037424035.48Páramo Grassland
Portal Andino AIChimborazo750,0199,837,89141207.62Subnival evergreen grassland and shrubland of the moorland
Cruz del Arenal ANIBolívar731,1629,844,778424057.75Subnival evergreen grassland and shrubland of the moorland
Puente Ayora ANIBolívar728,4789,841,941410512.19Subnival evergreen grassland and shrubland of the moorland
Puente Ayora BNIBolívar726,4869,839,40138420.29Evergreen shrubland and moorland grassland
Puente Ayora AIBolívar728,0139,841,127412012.84Subnival evergreen grassland and shrubland of the moorland
Pachancho BIBolívar728,3159,847,85440408.78Subnival evergreen grassland and shrubland of the moorland
Cruz del Arenal BNIBolívar732,6719,840,421412018.78Páramo upper montane moist upper montane grassland

References

  1. Beller, E.E.; McClenachan, L.; Zavaleta, E.S.; Larsen, L.G. Past forward: Recommendations from historical ecology for ecosystem management. Glob. Ecol. Conserv. 2020, 21, e00836. [Google Scholar] [CrossRef]
  2. Allan, J.D. Landscapes and Riverscapes: The Influence of Land Use on Stream Ecosystems. Annu. Rev. Ecol. Evol. Syst. 2004, 35, 257–284. [Google Scholar] [CrossRef] [Green Version]
  3. Sánchez, O. Perspectivas Sobre Conservación de Ecosistemas Acuáticos en México; Instituto Nacional de Ecología: Mexico City, Mexico, 2007. [Google Scholar]
  4. Withey, P.; van Kooten, G.C. The effect of climate change on optimal wetlands and waterfowl management in Western Canada. Ecol. Econ. 2011, 70, 798–805. [Google Scholar] [CrossRef]
  5. Xia, S.; Liu, Y.; Chen, B.; Jia, Y.; Zhang, H.; Liu, G.; Yu, X. Effect of water level fluctuations on wintering goose abundance in Poyang Lake wetlands of China. Chin. Geogr. Sci. 2017, 27, 248–258. [Google Scholar] [CrossRef] [Green Version]
  6. Finlayson, C.M.; Davidson, N.C.; Spiers, A.G.; Stevenson, N.J. Global wetland inventory—Current status and future priorities. Mar. Freshw. Res. 1999, 50, 717–727. [Google Scholar] [CrossRef]
  7. Costanza, R.; de Groot, R.; Sutton, P.; van der Ploeg, S.; Anderson, S.J.; Kubiszewski, I.; Farber, S.; Turner, R.K. Changes in the global value of ecosystem services. Glob. Environ. Chang. 2014, 26, 152–158. [Google Scholar] [CrossRef]
  8. Global Wetland Outlook. (s.f.). Global Wetland Outlook. Recuperado 9 de junio de 2023. Available online: https://www.global-wetland-outlook.ramsar.org (accessed on 8 February 2022).
  9. Blanco, D. Los Turbales de la Patagonia. Bases Para su Inventario y la Conservación de su Biodiversidad; En Hemeroteca CEDOC CIREN; Wetlands International—América del Sur: Buenos Aires, Argentina, 2004; Available online: https://bibliotecadigital.ciren.cl/handle/20.500.13082/26803 (accessed on 8 February 2022).
  10. Moreno-Mateos, D.; Power, M.E.; Comín, F.A.; Yockteng, R. Structural and Functional Loss in Restored Wetland Ecosystems. PLoS Biol. 2012, 10, e1001247. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Costanza, R.; d’Arge, R.; de Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.; Naeem, S.; O’Neill, R.V.; Paruelo, J.; et al. The value of the world’s ecosystem services and natural capital. Nature 1997, 387, 6630. [Google Scholar] [CrossRef]
  12. Junk, W.J.; An, S.; Finlayson, C.M.; Gopal, B.; Květ, J.; Mitchell, S.A.; Mitsch, W.J.; Robarts, R.D. Current state of knowledge regarding the world’s wetlands and their future under global climate change: A synthesis. Aquat. Sci. 2013, 75, 151–167. [Google Scholar] [CrossRef] [Green Version]
  13. Barbier, E.; Acreman, M.; Knowler, D. Valoración Económica de los Humedales; Oficina de la Convención de Ramsar: Gland, Switzerland, 1997; Available online: https://portals.iucn.org/library/sites/library/files/documents/Ramsar-021-Es.pdf (accessed on 8 February 2022).
  14. Bragazza, L.; Rydin, H.; Gerdol, R. Multiple gradients in mire vegetation: A comparison of a Swedish and an Italian bog. Plant Ecol. 2005, 177, 223–236. [Google Scholar] [CrossRef]
  15. Sottocornola, M.; Laine, A.; Kiely, G.; Byrne, K.A.; Tuittila, E.-S. Vegetation and environmental variation in an Atlantic blanket bog in South-western Ireland. Plant Ecol. 2009, 203, 69–81. [Google Scholar] [CrossRef]
  16. Glina, B.; Piernik, A.; Hulisz, P.; Mendyk, Ł.; Tomaszewska, K.; Podlaska, M.; Bogacz, A.; Spychalski, W. Water or soil—What is the dominant driver controlling the vegetation pattern of degraded shallow mountain peatlands? Land Degrad. Dev. 2019, 30, 1437–1448. [Google Scholar] [CrossRef]
  17. Gallant, A.L. The Challenges of Remote Monitoring of Wetlands. Remote Sens. 2015, 7, 10938–10950. [Google Scholar] [CrossRef] [Green Version]
  18. Buytaert, W.; Célleri, R.; de Bièvre, B.; Cisneros, F. Hidrología del Páramo Andino: Propiedades. Importancia y Vulnerabilidad. 2023. Available online: https://www.researchgate.net/publication/228459137_HIDROLOGIA_DEL_PARAMO_ANDINO_PROPIEDADES_IMPORTANCIA_Y_VULNERABILIDAD (accessed on 8 February 2022).
  19. Flachier, A.; Chinchero, M.; Lima, P.; Villarroel, M. Caracterización Ecológica de las Turberas y Bofedales del Sistema de Humedales Amaluza. In Nudo de Sabanilla, Provincia de Loja, Ecuador; Ministerio del Ambiente: Lima, Peru, 2009; Available online: https://docplayer.es/57884650-Caracterizacion-ecologica-de-las-turberas-y-bofedales-del-sistema-de-humedales-amaluza-nudo-de-sabanilla-provincia-de-loja-ecuador.html (accessed on 15 July 2022).
  20. Jara, C.; Delegido, J.; Ayala, J.; Lozano, P.; Armas, A.; Flores, V. Estudio de bofedales en los Andes ecuatorianos a través de la comparación de imágenes Landsat-8 y Sentinel-2. Rev. Teledetec. 2019, 53, 45. [Google Scholar] [CrossRef] [Green Version]
  21. Conferencia de las Partes. Estrategia Regional de Conservación y Uso Sostenible de los Humedales Altoandinos. In Proceedings of the 9a Reunión de la Conferencia de las Partes Contratantes en la Convención sobre los Humedales, Kampala, Uganda, 8–15 November 2005; Available online: https://www.ramsar.org/sites/default/files/documents/pdf/cop9/cop9_doc26_s.pdf (accessed on 15 July 2022).
  22. Guo, Z.; Cui, G.; Zhang, M.; Li, X. Analysis of the contribution to conservation and effectiveness of the wetland reserve network in China based on wildlife diversity. Glob. Ecol. Conserv. 2019, 20, e00684. [Google Scholar] [CrossRef]
  23. Geldmann, J.; Coad, L.; Barnes, M.; Craigie, I.D.; Hockings, M.; Knights, K.; Leverington, F.; Cuadros, I.C.; Zamora, C.; Woodley, S.; et al. Changes in protected area management effectiveness over time: A global analysis. Biol. Conserv. 2015, 191, 692–699. [Google Scholar] [CrossRef] [Green Version]
  24. UNEP; WCMC; UICN. The Lag Effect in the World Database on Protected Areas. Protected Planet. 2018. Available online: https://www.protectedplanet.net/en/news-and-stories/the-lag-effect-in-the-world-database-on-protected-areas (accessed on 25 July 2022).
  25. Ministerio del Ambiente del Ecuador. Áreas Protegidas—Ministerio del Ambiente. Agua y Transición Ecológica. Áreas Protegidas. 2014. Available online: https://www.ambiente.gob.ec/areas-protegidas-3/ (accessed on 13 August 2022).
  26. McLaren, B.E.; MacNearney, D.; Siavichay, C.A. Livestock and the functional habitat of vicuñas in Ecuador: A new puzzle. Ecosphere 2018, 9, e02066. [Google Scholar] [CrossRef] [Green Version]
  27. Hill, B.H.; Jicha, T.M.; Lehto, L.L.P.; Elonen, C.M.; Sebestyen, S.D.; Kolka, R.K. Comparisons of soil nitrogen mass balances for an ombrotrophic bog and a minerotrophic fen in northern Minnesota. Sci. Total Environ. 2016, 550, 880–892. [Google Scholar] [CrossRef]
  28. Nicia, P.; Bejger, R.; Zadrożny, P.; Sterzyńska, M. The impact of restoration processes on the selected soil properties and organic matter transformation of mountain fens under Caltho-Alnetum community in the Babiogórski National Park in Outer Flysch Carpathians. Poland. J. Soils Sediments 2018, 18, 2770–2776. [Google Scholar] [CrossRef] [Green Version]
  29. Griffiths, N.A.; Sebestyen, S.D.; Oleheiser, K.C. Variation in peatland porewater chemistry over time and space along a bog to fen gradient. Sci. Total Environ. 2019, 697, 134152. [Google Scholar] [CrossRef]
  30. White, D.A.; Weiss, T.E.; Trapani, J.M.; Thien, L.B. Productivity and Decomposition of the Dominant Salt Marsh Plants in Louisiana. Ecology 1978, 59, 751–759. [Google Scholar] [CrossRef]
  31. Simas, T.; Ferreira, J. Nutrient enrichment and the role of salt marshes in the Tagus estuary (Portugal). Estuar. Coast. Shelf Sci. 2007, 75, 393–407. [Google Scholar] [CrossRef]
  32. Van Donk, E.; van de Bund, W.J. Impact of submerged macrophytes including charophytes on phyto- and zooplankton communities: Allelopathy versus other mechanisms. Aquat. Bot. 2002, 72, 261–274. [Google Scholar] [CrossRef]
  33. Wetzel, R.; Hough, R. Productivity and Role of Aquatic Macrophytes in Lakes: An Assessment; COO-1599-54; CONF-720620-1; Michigan State University: Lansing, MI, USA, 1972. Available online: https://www.osti.gov/servlets/purl/4639975 (accessed on 17 August 2022).
  34. Zhang, Y.; Jeppesen, E.; Liu, X.; Qin, B.; Shi, K.; Zhou, Y.; Thomaz, S.M.; Deng, J. Global loss of aquatic vegetation in lakes. Earth-Sci. Rev. 2017, 173, 259–265. [Google Scholar] [CrossRef]
  35. Gong, H.; Gao, J. Soil and climatic drivers of plant SLA (specific leaf area). Glob. Ecol. Conserv. 2019, 20, e00696. [Google Scholar] [CrossRef]
  36. Carreño-Rocabado, G.; Peña-Claros, M.; Bongers, F.; Díaz, S.; Quétier, F.; Chuviña, J.; Poorter, L. Land-use intensification effects on functional properties in tropical plant communities. Ecol. Appl. 2016, 26, 174–189. [Google Scholar] [CrossRef] [Green Version]
  37. Chillo, V.; Vázquez, D.P.; Amoroso, M.M.; Bennett, E.M. Land-use intensity indirectly affects ecosystem services mainly through plant functional identity in a temperate forest. Funct. Ecol. 2018, 32, 1390–1399. [Google Scholar] [CrossRef] [Green Version]
  38. Peco, B.; Navarro, E.; Carmona, C.P.; Medina, N.G.; Marques, M.J. Effects of grazing abandonment on soil multifunctionality: The role of plant functional traits. Agric. Ecosyst. Environ. 2017, 249, 215–225. [Google Scholar] [CrossRef]
  39. Díaz, S.; Lavorel, S.; de Bello, F.; Quétier, F.; Grigulis, K.; Robson, T.M. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl. Acad. Sci. USA 2007, 104, 20684–20689. [Google Scholar] [CrossRef]
  40. Wen, Z.; Zheng, H.; Smith, J.R.; Zhao, H.; Liu, L.; Ouyang, Z. Functional diversity overrides community-weighted mean traits in linking land-use intensity to hydrological ecosystem services. Sci. Total Environ. 2019, 682, 583–590. [Google Scholar] [CrossRef]
  41. Klaus, V.H.; Kleinebecker, T.; Busch, V.; Fischer, M.; Hölzel, N.; Nowak, S.; Prati, D.; Schäfer, D.; Schöning, I.; Schrumpf, M.; et al. Land use intensity. rather than plant species richness. affects the leaching risk of multiple nutrients from permanent grasslands. Glob. Chang. Biol. 2018, 24, 2828–2840. [Google Scholar] [CrossRef] [Green Version]
  42. Fischer, C.; Leimer, S.; Roscher, C.; Ravenek, J.; de Kroon, H.; Kreutziger, Y.; Baade, J.; Beßler, H.; Eisenhauer, N.; Weigelt, A.; et al. Plant species richness and functional groups have different effects on soil water content in a decade-long grassland experiment. J. Ecol. 2019, 107, 127–141. [Google Scholar] [CrossRef] [Green Version]
  43. Villegas, J.C. Análisis del Conocimiento en la Relación Agua-Suelo-Vegetación Para el Departamento de Antioquia. Rev. EIA 2004, 1, 73–79. [Google Scholar]
  44. Matteucci, S.; Colma, A. Metodología Para el Estudio de la Vegetación; En Serbiula (Sistema Librum 2.0); Universidad Nacional Experimental Francisco de Miranda: Falcón, Venezuela, 1982. [Google Scholar]
  45. American Public Health Association (APHA). Standard Methods for the Examination of Water and Wastewater; American Public Health Association: Washington, DC, USA, 1999. [Google Scholar]
  46. Red de Laboratorios de Suelos del Ecuador (RELASE). Manual para Análisis de Suelo; 2001. Available online: https://www.agrocalidad.gob.ec/wp-content/uploads/2021/08/FICHA-INIAP-2021-1.pdf (accessed on 13 August 2022).
  47. R Core Team. (2020) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available online: https://www.r-project.org/ (accessed on 13 August 2022).
  48. Wold, S.; Esbensen, K.; Geladi, P. Principal component analysis. Chemom. Intell. Lab. Syst. 1987, 2, 37–52. [Google Scholar] [CrossRef]
  49. Husson, F.; Josse, J.; Le, S.; Mazet, J. FactoMineR: Multivariate Exploratory Data Analysis and Data Mining, Version 2.8; RStudio: Boston, MA, USA, 2023; Available online: https://cran.r-project.org/web/packages/FactoMineR/index.html (accessed on 13 August 2022).
  50. Wickham, H. Ggplot2. Rev. Comput. Stat. 2011, 3, 180–185. [Google Scholar] [CrossRef]
  51. Muriithi, F.K.; Yu, D. Understanding the Impact of Intensive Horticulture Land-Use Practices on Surface Water Quality in Central Kenya. Environments 2015, 2, 521–545. [Google Scholar] [CrossRef] [Green Version]
  52. Jabbar, F.K.; Grote, K. Statistical assessment of nonpoint source pollution in agricultural watersheds in the Lower Grand River watershed. MO. USA. Environ. Sci. Pollut. Res. 2019, 26, 1487–1506. [Google Scholar] [CrossRef] [Green Version]
  53. Galindo-Villardón, P. Contribuciones a la Representación Simultánea de Datos Multidimensionales. Ph.D. Thesis, Universidad de Salamanca, Salamanca, Spain, 1985; p. 1. Available online: https://dialnet.unirioja.es/servlet/tesis?codigo=287930 (accessed on 12 August 2022).
  54. Eckart, C.; Young, G. The approximation of one matrix by another of lower rank. Psychometrika 1936, 1, 211–218. [Google Scholar] [CrossRef]
  55. Galindo-Villardón, P. Una alternativa de representación simultánea. HJ-Biplot 1986, 10, 13–23. [Google Scholar]
  56. León-Yánez, S.; Valencia, R.; Pitman, N.; Endara, L.; Ulloa, C.; Navarrete, H. Libro Rojo de las Plantas Endémicas del Ecuador; Pontificia Universidad Católica del Ecuador. Quito. 2011. Available online: https://bioweb.bio/floraweb/librorojo/home (accessed on 12 August 2022).
  57. Heinselman, M.L. Landscape Evolution. Peatland Types. and the Environment in the Lake Agassiz Peatlands Natural Area. Minnesota. Ecol. Monogr. 1970, 40, 235–261. [Google Scholar] [CrossRef]
  58. Verhoeven, J.T.A.; Maltby, E.; Schmitz, M.B. Nitrogen and Phosphorus Mineralization in Fens and Bogs. J. Ecol. 1990, 78, 713–726. [Google Scholar] [CrossRef] [Green Version]
  59. Miserere, L.; Montacchini, F.; Buffa, G. Ecology of some mire and bog plant communities in the Western Italian Alps. J. Limnol. 2003, 62, 88. [Google Scholar] [CrossRef] [Green Version]
  60. Smith, J.M.B.; Cleef, A.M. Composition and Origins of the World’s Tropicalpine Floras. J. Biogeogr. 1988, 15, 631–645. [Google Scholar] [CrossRef]
  61. Sklenář, P.; Dušková, E.; Balslev, H. Tropical and Temperate: Evolutionary History of Páramo Flora. Bot. Rev. 2011, 77, 71–108. [Google Scholar] [CrossRef]
  62. Madriñán, S.; Cortés, A.J.; Richardson, J.E. Páramo is the world’s fastest evolving and coolest biodiversity hotspot. Front. Genet. 2013, 4, 192. Available online: https://www.frontiersin.org/articles/10.3389/fgene.2013.00192 (accessed on 24 August 2022). [CrossRef] [Green Version]
  63. Fonkén, M.S.M. An introduction to the bofedales of the Peruvian High Andes. Mires Peat 2014, 15, 1–13. [Google Scholar]
  64. Passuni, E.O.; Fonkén, M.S.M. Relationships between aquatic invertebrates. water quality and vegetation in an Andean peatland system. Mires Peat 2015, 15, 1–4. [Google Scholar]
  65. Troll, C. The cordilleras of the Tropical Americas. aspects of climatic. phytogeographical and agrarian ecology. Colloq. Geogr. 1968, 9, 15–56. Available online: https://cir.nii.ac.jp/crid/1572543025020420352 (accessed on 24 August 2022).
  66. Baruch, Z. Ordination and classification of vegetation along an altitudinal gradient in the Venezuelan páramos. Vegetatio 1984, 55, 115–126. [Google Scholar] [CrossRef]
  67. Vásquez, D.L.A.; Balslev, H.; Sklenář, P. Human impact on tropical-alpine plant diversity in the northern Andes. Biodivers. Conserv. 2015, 24, 2673–2683. [Google Scholar] [CrossRef]
  68. Scheffer, M. Ecology of Shallow Lakes; Springer: Heidelberg, The Netherlands, 2004. [Google Scholar] [CrossRef]
  69. Jeppesen, E.; Søndergaard, M.; Søndergaard, M.; Christoffersen, K. The Structuring Role of Submerged Macrophytes in Lakes; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012. [Google Scholar]
  70. Wood, K.A.; O’Hare, M.T.; McDonald, C.; Searle, K.R.; Daunt, F.; Stillman, R.A. Herbivore regulation of plant abundance in aquatic ecosystems. Biol. Rev. 2017, 92, 1128–1141. [Google Scholar] [CrossRef] [Green Version]
  71. Declerck, S.; Vandekerkhove, J.; Johansson, L.; Muylaert, K.; Conde-Porcuna, J.M.; Van der Gucht, K.; Pérez-Martínez, C.; Lauridsen, T.; Schwenk, K.; Zwart, G.; et al. Multi-Group Biodiversity in Shallow Lakes Along Gradients of Phosphorus and Water Plant Cover. Ecology 2005, 86, 1905–1915. [Google Scholar] [CrossRef] [Green Version]
  72. Fayiah, M.; Dong, S.; Li, Y.; Xu, Y.; Gao, X.; Li, S.; Shen, H.; Xiao, J.; Yang, Y.; Wessell, K. The relationships between plant diversity. plant cover. plant biomass and soil fertility vary with grassland type on Qinghai-Tibetan Plateau. Agric. Ecosyst. Environ. 2019, 286, 106659. [Google Scholar] [CrossRef]
  73. Yang, X.; Shao, M.; Li, T.; Gan, M.; Chen, M. Community characteristics and distribution patterns of soil fauna after vegetation restoration in the northern Loess Plateau. Ecol. Indic. 2021, 122, 107236. [Google Scholar] [CrossRef]
  74. Yu, D.; Zhou, L.; Zhou, W.; Ding, H.; Wang, Q.; Wang, Y.; Wu, X.; Dai, L. Forest Management in Northeast China: History, Problems. and Challenges. Environ. Manag. 2011, 48, 1122–1135. [Google Scholar] [CrossRef]
  75. Fang, X.; Wang, Q.; Zhou, W.; Zhao, W.; Wei, Y.; Niu, L.; Dai, L. Land use effects on soil organic carbon. microbial biomass and microbial activity in Changbai Mountains of Northeast China. Chin. Geogr. Sci. 2014, 24, 297–306. [Google Scholar] [CrossRef] [Green Version]
  76. Castro, M.; Almeida, J.; Ferrer, J.; Diaz, D. Indicadores de la Calidad de Agua: Evolución y tendencias a nivel global. Ing. Ambient. 2014, 10, 111–124. [Google Scholar] [CrossRef] [Green Version]
  77. Calvo-Díaz, M.; Sardinas, O.; Cana, R. Evaluacion de las concentraciones de oligoelementos y dureza total del agua. In Instituto Nacional de Higiene. Epidemiologia y Microbiologia. Agua y Salud; 2002; pp. 115–129. Available online: https://pesquisa.bvsalud.org/portal/resource/pt/lil-228662 (accessed on 24 August 2022).
  78. Perdomo, C.H.; Casanova, O.N.; Ciganda, V.S. Contaminación de aguas subterráneas con nitratos y coliformes en el litoral sudoeste del Uruguay. Agrocienc. Sitio Repar. 2001, 5, 10–22. [Google Scholar]
  79. Melvin, S.; Baker, J.; Hickman, J.; Moncrief, J.; Wollenhaupt, N. Water quality. In Mindwest Plan Service. Conservation Tillage Systems and Management; Iowa State University: Ames, IA, USA, 1992; pp. 48–55. [Google Scholar]
  80. Coronel, J.S.; Declerck, S.; Maldonado, M.; Ollevier, F.; Brendonck, L. Temporary shallow pools in high-Andes ‘bofedal’ peatlands. Arch. Sci. 2004, 57, 85–96. [Google Scholar]
  81. Picone, L.I.; Andreoli, Y.E.; Costa, J.L.; Crespo, L.; Nannini, J.; Tambascio, W. Evaluación de nitratos y bacterias coliformes en pozos de la Cuenca Alta del Arroyo Pantanoso (BS. AS.). Rev. Investig. Agropecu. 2003, 32, 99–110. Available online: https://dialnet.unirioja.es/servlet/articulo?codigo=3995875 (accessed on 24 August 2022).
  82. McLaughlin, J.W.; Webster, K.L. Alkalinity and acidity cycling and fluxes in an intermediate fen peatland in northern Ontario. Biogeochemistry 2010, 99, 143–155. [Google Scholar] [CrossRef]
  83. Yabe, K.; Nakatani, N.; Yazaki, T. Cause of decade’s stagnation of plant communities through16-years successional trajectory toward fens at a created wetland in northern Japan. Glob. Ecol. Conserv. 2021, 25, e01424. [Google Scholar] [CrossRef]
  84. Leibold, M.A.; Norberg, J. Biodiversity in metacommunities: Plankton as complex adaptive systems? Limnol. Oceanogr. 2004, 49, 1278–1289. [Google Scholar] [CrossRef] [Green Version]
Land 12 01586 g001
Figure 1. Map of the study area. (A) Geographic location of the wetlands of the RPFCH (BNI: Low Intervened Level. BI: Low Intervention. AI: High Intervention. ANI: High Intervened Level (B) Location in relation to South America and Ecuador.
Figure 1. Map of the study area. (A) Geographic location of the wetlands of the RPFCH (BNI: Low Intervened Level. BI: Low Intervention. AI: High Intervention. ANI: High Intervened Level (B) Location in relation to South America and Ecuador.
Land 12 01586 g001
Land 12 01586 g002
Figure 2. Contributions of wetlands to the multivariate analysis of variables related to physical and physical-chemical aspects of water and soil quality.
Figure 2. Contributions of wetlands to the multivariate analysis of variables related to physical and physical-chemical aspects of water and soil quality.
Land 12 01586 g002
Land 12 01586 g003
Figure 3. Biplot outcome of bofedals and physiochemical variables displayed in the 1 and 2 axes.
Figure 3. Biplot outcome of bofedals and physiochemical variables displayed in the 1 and 2 axes.
Land 12 01586 g003
Land 12 01586 g004
Figure 4. Biplot result of wetlands and physicochemical variables shown in axes 1 and 2, excluding the most representative variables.
Figure 4. Biplot result of wetlands and physicochemical variables shown in axes 1 and 2, excluding the most representative variables.
Land 12 01586 g004
Table 1. Methods and parameters used for the analysis of physicochemical samples from the bofedales of the RPFCH.
Table 1. Methods and parameters used for the analysis of physicochemical samples from the bofedales of the RPFCH.
ParametersUnitsMethod
Fecal ColiformsNMP/100 mLSM 9221 E
Ammonium (NH4)mg/LSM 4500-NH3 EPA 350.2/350.3
Calcium (Ca)mg/LSM 3111 B
Electrical conductivityuS/cmHACH 8160
Biological oxygen demand (B.O.D.)(mg/L)SM 5210 B
Chemical oxygen demand (C.O.D.)mg/LSM 5220 D
Hardnessmg/LSM 2340 C
Phosphorus (P)mg/LSM 4500-P
Magnesium (Mg)mg/LSM 3111 B
Nitrates (NO3−)mg/LSM 4500 NO3-E
Nitrites (NO2−)mg/LHACH 8507
Sulfates (SO42−)mg/LSM 4500 SO42−
Dissolved oxygen%SM 4500–O G
TurbidityNTUSM 2130 B
Totally suspended solidsmg/LGravimetric 2540-D
Table 2. Floristic diversity of the wetlands of the Chimborazo Fauna Production Reserve (* = aquatic species. N/N = No name. N/D = No data).
Table 2. Floristic diversity of the wetlands of the Chimborazo Fauna Production Reserve (* = aquatic species. N/N = No name. N/D = No data).
OrderFamilyCientific NameOrigenNumber of Individuals
ApialesApiaceaeAzorella pedunculata (Spreng.) Mathias & Constance. 1995Native11,988
Eryngium humile Cav. (1800)Native1085
Oreomyrrhis andicola (Kunth) Endl. ex Hook. f. (1846)Native156
Azorella biloba (Schltdl.) Wedd. (1860)Native351
Azorella aretioides (Spreng.) Willd. ex DC. (1830)Native88
AlismatalesHydrocharitaceaeElodea canadensis Michx. (1803) *Native1405
PotamogetonaceaePotamogeton filiformis Pers. (1805) *Native148
AsteralesAsteraceaeBaccharis caespitosa (Ruiz & Pav.) Pers. (1807)Native1421
Bidens andicola Kunth. 1820Native115
Achyrocline alata (Kunth) DC. (1837)Native64
Gamochaeta americana (Mill.) Wedd. (1855)Native8
Gnaphalium spicatum (Forssk.) Vahl. 1788Native19
Hypochaeris sessiliflora Kunth. 1820Native2022
Monticalia arbutifolia (Kunth) C. Jeffrey. 1992Native79
Oritrophium peruvianum (Lam.) Cuatrec. (1961)Native5
Werneria nubigena Kunth. 1820Native71
Xenophyllum humile (Kunth) V.A. Funk. 1997Native131
Erigeron ecuadoriensis Hieron. (1896)Native18
Erigeron L. (1753)N/D10,360
Culcitium Bonpl. (1808)N/D5
Gnaphalium purpureum L.(1753)Native84
Gnaphalium chimboracense Hieron. ex Sodiro. (1900)Native4
BrassicalesBrassicaceaeRorippa pinnata (Sessé y Moc.) Rollins. 1960 *Native5609
BartramialesBartramiaceaeBreutelia chrysea (Müll. Hal.) A. JaegerNative4307
Bartramia potosica Mont. (1838)Native40,579
BryalesBryaceaeRhodobryum (Schimp.) Limpr. (1892)N/DI255
MniaceaePlagiomnium rhynchophorum (Harv.) T.J. Kop. (1971)Native491
CyathealesCyateaceae Alsophila R. Br. (1810)N/D39
DipsacalesValerianaceaeValeriana microphylla Kunth. 1818Native178
Valeriana rigida Ruiz & Pav. (1798)Native23
EphedralesEphedraceaeEphedra rupestris Benth. (1846)Native129
EquisetalesEquisetaceaeEquisetum bogotense Kunth. 1815Native177
EricalesEricaceae Disterigma empetrifolium (Kunth) Drude. 1889Native483
Pernettya prostrata (Cav.) Sleumer. 1935Native122
Vaccinium floribundum Kunth. 1819Native89
CaryophyllalesCaryophyllaceaeDrymaria ovata Humb. & Bonpl. ex Schult. (1819)Native48
PolygonaceaeRumex acetosella L. (1753)Introduced26
FabalesFabaceaeLupinus microphyllus Desr. (1792)Native24
Lupino pubescens Benth. (1845)Native18
Trifolium repens L. (1753)Introduced125
GentianalesGentianaceaeGentiana cerastioides Kunth. 1819Native2716
Gentiana sedifolia Kunth. 1819Native487
Gentianella corymbosa (Kunth) Weaver & Ruedenberg. 1975Native9
Halenia pulchella Gilg. 1916Endemic22
RubiaceaeGalium hypocarpium (L.) Fosberg. 1966Native620
Galium pumilio Standl. (1929)Native783
Nertera granadensis (Mutis ex L. f.) Druce. 1916Native244
GeranialesGeraniaceaeGeranium diffusum Kunth. 1821Native2800
HookerialesPilotrichaceae Cyclodictyon roridum (Hampe) Kuntze. 1891Native28,598
MalpighialesHypericaceaeHypericum laricifolium Juss. (1804)Native9
HypnalesThuidiaceaThuidium peruvianum Mitt. (1869)Native36,412
BrachytheciaceaeBrachythecium austroglareosum (Müll. Hal.) Kindb. (1891)Native290
LamialesOrobanchaceaeBartsia laticrenata Benth. (1989)Native4
Castilleja fissifolia Sessé & Moc. (1995)Native1
Plantaginaceae Sibthorpia repens (L.) Kuntze. 1898Native40
Plantago australis Lam. (1791)Native8
Plantago rigida Kunth. 1817Native7482
LycopodialesLycopodiaceaeHuperzia crassa (Humb. & Bonpl. ex Willd.) Rothm. (1944)Native36
MarchantialesMarchantiaceae Marchantia L. (1753)N/D10
MalvalesMalvaceaeNototriche hartwegii A.W. Hill. 1909Endemic1008
MyrtalesOnagraceaeEpilobium denticulatum Ruiz & Pav. (1802)Native49
PolypodialesDryopteridaceaeElaphoglossum engelii (H. Karst.) Christ. 1899Native4185
Polystichum orbiculatum (Desv.) J. Rémy & Fée. 1853Native8
PolypodiaceaeMelpomene moniliformis (Lag. ex Sw.) A.R. Sm. & R.C. Moran. 1992Native30
PoalesJuncaeaeDistichia musczoides Nees. & Meyen. (1843)Native9
CyperaceaeCarex bonplandii Kunth. 1837Native2584
Eleocharis albibracteata Nees & Meyen ex Kunth. 1837 *Native2971
Eleocharis albibracteata Nees & Meyen ex Kunth. 1837Native696
PoaceaeAgrostis foliata Hook. f. (1844)Native22
Agrostis breviculmis Hitchc. (1905)Native25,418
Bromus pitensis Kunth. 1816Native5406
Cortaderia sericantha (Steud.) Hitchc. (1927)Native31
Eragrostis nigricans (Kunth) Steud. (1840)Native481
Muhlenbergia angustata (J. Presl) Kunth. 1833Native4
Phalaris minor Retz. (1783)Introduced4545
PottialesPottiaceaeLeptodontium longicaule (Müll.Hal.) Hampe ex Lindb. (1869) Native1576
Leptodontium ulocalyx (Müll. Hal.) Mitt.(1869)Native30,634
Leptodontium wallisii (Müll. Hal.) Kindb. (1888)Native1900
PorellalesLejeuneaceaeLejeunea Lib. (1820)Native11
RanunculalesRanunculaceaeRanunculus flagelliformis Sm. (1815) *Native1075
Ranunculus peruvianus Pers. (1806) *Native14
RosalesRosaceaeLachemilla andina (L.M. Perry) Rothm. (1937)Native531
Lachemilla galioides (Benth.) Rothm. (1938)Native27
Lachemilla orbiculata (Ruiz & Pav.) Rydb. (1908)Native4086
SaxifragalesHaloragaceaeMyriophyllum quitense Kunth.1823 *Native514
Table 3. Physico-chemical analysis of the water of the RPFCH bofedales.
Table 3. Physico-chemical analysis of the water of the RPFCH bofedales.
Physicochemical
Parameters:		W1W2W3W4W5W6W7W8W9W10W11W12W13W14W15Water Quality Criteria According to TULSMA for:
Human and Domestic ConsumptionWildlife PreservationAgricultural Irrigation
pH9.709.907.7010.209.7011.207.9010.7011.308.908.809.108.708.508.606–96.5–96.9
Temp (°C)0.003.601.800.000.003.701.800.001.901.901.900.000.000.000.00Natural conditionNatural conditionNatural condition
F. Col. NMP/100 mL1.991.040.891.001.410.760.931.011.221.221.222.110.920.001.20---
NH4 (mg/L)5.307.388.342.252.672.918.374.4210.4510.452.407.662.796.153.870.05--
Ca (mg/L)139.20345.10224.3385.6390.40115.10312.3397.83270.00270.0098.00287.6785.7363.40107.73---
Cond. (uS/cm)5.371.866.0915.907.800.092.643.423.873.873.8725.5031.500.7411.70--700
B.O.D. (mg/L)9.0012.0037.0025.0015.007.004.0019.0027.0027.0027.00156.0041.003.0535.00<220-
C.O.D. (mg/L)46.00111.0086.0026.0030.0043.00111.0041.0096.0096.0043.00110.0022.0023.4128.00<440-
Hardness (mg CaCO3/l)0.000.000.000.420.490.5480.6750.000.6360.6360.6360.620.000.070.00400--
P (mg/L)1.902.502.301.801.901.602.301.602.002.000.513.201.301.962.20---
Mg (mg/L)0.500.400.000.000.300.500.000.600.600.600.300.300.800.300.50---
NO3− (mg/L)0.000.0020.0000.0040.0020.0190.0000.0000.0100.0100.010.000.000.000.001013-
NO2− (mg/L)1.004.002.004.002.001.002.003.002.002.003.0029.001.003.3041.0010.20.5
SO42− (mg/L)77.4089.6059.5090.3097.0077.1076.1070.1042.1042.1070.0053.4074.80101.2067.50>6>6>3
Diss. O. (%)7.555.060.994.440.399.622.754.21106.60102.60106.6059.007.516.002.00>80%>80%-
Tur. (NTU)60.0022.0025.0029.007.0066.0019.0019.0036.0066.00215.004.0036.002.007.00-1000-
T.S.S. (mg/L)9.709.907.7010.209.7011.207.9010.7011.308.908.809.108.708.508.60400-250
Physicochemical Parameters: Temperature (Temp. °C); Fecal Coliforms (F. Col. NMP/100 mL); Ammonium (NH4. mg/L); Calcium (Ca. mg/L); Electrical conductivity (Cond. uS/cm); Biological oxygen demand (B.O.D. mg/L); Chemical oxygen demand (C.O.D. mg/L); Hardness (mg CaCO3/l); Phosphorus (P. mg/L); Magnesium (Mg. mg/L); NO3− (Nitrates. mg/L); NO2− (Nitrites. mg/L); Sulfates (SO42− mg/L); Dissolved oxygen (Diss. O. mg/L and %); Turbidity (Tur. NTU); Totally suspended solids (TSS. mg/L). Bofedales: W1 (Pampa Salasacas BI). W2 (Río Blanco AI). W3 (Mechahuasca ANI). W4 (Cruz del Arenal BNI). W5 (Cruz del Arenal ANI). W6 (Culebrillas AI). W7 (Casa Cóndor BI). W8 (Lazabanza BNI). W9 (Cóndor Samana BI). W10 (Portal Andino AI). W11 (Los Hieleros ANI). W12 (Pachancho BI). W13 (Puente Ayora BNI). W14 (Puente Ayora AI). W15 (Puente Ayora ANI).
Table 4. Granulometry and organic matter analysis (indicate the meaning of each abbreviation).
Table 4. Granulometry and organic matter analysis (indicate the meaning of each abbreviation).
BofepHElec. Cond (uS)Organic Matter (%)NH4
(mg/kg)		P
(mg/kg)		K
(mg/kg)		TextureOrganic Carbon (%)Gran > 2.0 (mm)Gran > 1 (mm)Gran > 0.5 (mm)Gran > 0.25 (mm)Gran > 0.1 (mm)Gran < 0.1 (mm)
W15.10 L.Ac.177.5 Non-saline1.4%23.78 B35.24 A0.47 BLoamy sand0.81%0.36 gr13.64 gr22.73 gr29.03 gr25.66 gr18.58 gr
W25.32 L.Ac.285.0 Non saline2.9%9.11 B29.68 M1.12 ASandy loam1.68%1.10 gr21.52 gr23.84 gr17.83 gr20.33 gr15.38 gr
W35.37 L.Ac.292.0 Non saline2.6%27.95 B25.96 M0.57 MLoamy sand1.50%0.13 gr4.40 gr14.62 gr25.89 gr36.88 gr18.08 gr
W45.97 L.Ac.136.7 Non saline1.1%7.26 B38.02 A0.36 BLoamy sand0.63%0.73 gr12.44 gr14.14 gr17.17 gr37.75 gr17.77 gr
W55.65 L.Ac.324.0 Non saline5.0%15.60 B27.12 M0.65 ALoamy sand2.90%0.76 gr9.54 gr8.79 gr7.05 gr8.01 gr9.90 gr
W65.76 L.Ac.217.0 Non saline1.3%8.92 B30.37 A0.67 ALoamy sand0.75%0.10 gr1.82 gr1.91 gr9.13 gr67.25 gr19.79 gr
W75.32 L.Ac.603.0 Non saline3.4%15.68 B41.04 A1.25 ALoamy sand1.97%0.95 gr15.12 gr14.82 gr17.94 gr33.27 gr17.90 gr
W85.07 L.Ac.214.0 Non saline4.5%24.21 B26.43 M0.86 ALoamy sand2.61%0.45 gr2.44 gr8.6 gr14.65 gr42.72 gr31.14 gr
W95.36 L.Ac.149.1 Non saline3.4%21.89 B37.09 A0.61 MLoamy sand1.97%0.35 gr8.25 gr29.59 gr8.67 gr18.29 gr34.85 gr
W105.32 L.Ac.140.6 Non saline1.3%12.70 B25.73 M0.58 MLoamy sand0.75%1.72 gr16.68 gr19.40 gr15.35 gr23.76 gr23.09 gr
W115.20 L.Ac.231.0 Non saline1.3%10.85 B32.00 A0.78 ALoamy sand0.75%1.04 gr8.19 gr8.88 gr17.92 gr40.75 gr23.22 gr
W125.66 L.Ac.252.0 Non saline2.5%11.47 B49.62 A1.21 ALoamy sand1.45%1.02 gr0.7 gr3.79 gr11.49 gr47.69 gr35.31 gr
W135.44 L.Ac.164.0 Non saline3.7%21.78 B30.84 A0.78 ASandy loam2.14%0.30 gr9.95 gr11.75 gr13.76 gr35.37 gr28.87 gr
W145.46 L.Ac.197.7 Non saline3.1%12.26 B32.92 A0.95 ASandy loam1.79%2.61 gr19.60 gr16.92 gr7.97 gr22.08 gr30.82 gr
W155.47 L.Ac.223.0 Non saline3.4%15.50 B33.16 A0.81 ASandy loam1.97%0.21 gr2.80 gr5.61 gr8.51 gr 33.56 gr49.31 gr
Parameters: Elec. Cond: Electrical conductivity; % OM: Percentage of Organic Matter; NH4: Ammonium; P: Phosphorus: K: Potassium; Soil pH: slightly acidic (L.Ac.) Presence level: A: High; M: Medium; B: Low; Gran: Granulometry.
Table 5. Eigenvalues and percentages of explained and cumulative variances.
Table 5. Eigenvalues and percentages of explained and cumulative variances.
AxesEigenvalues (Inertia)Explained Variance (%)Cumulative Variance (%)
1302,578.1343.4343.43
2190,750.4427.3870.81
3129,685.8818.6189.42
438,703.435.5794.99
517,163.432.4697.45
Table 6. Contributions of variables to the multivariate analysis of variables related to physical and physical-chemical aspects of water and soil quality.
Table 6. Contributions of variables to the multivariate analysis of variables related to physical and physical-chemical aspects of water and soil quality.
VariablesAxis 1Percentage Contribution Axis 1 (%)Axis 2Percentage Contribution Axis 2 (%)
Altitude66719.2%3329.5%
Ha37610.8%1394.0%
Tc1012.9%441.3%
B.O.D1604.6%90.3%
C.O.D00.0%80.2%
NH440.1%681.9%
P1494.3%120.3%
NO3−2517.2%100.3%
NO2−80.2%1133.2%
Sulfates10.0%20.1%
Ca792.3%1584.5%
Mg551.6%1032.9%
Electrical conductivity3399.8%2597.4%
Hard2717.8%3118.9%
Diss.O20.1%200.6%
Turbidity110.3%30.1%
T.S.S90.3%190.5%
pH10.0%1333.8%
EC43512.5%3479.9%
Organic Matter140.4%60.2%
NH4411.2%230.7%
P50.1%2637.5%
K1343.9%3199.1%
OCs140.4%60.2%
Organic Carbon240.7%130.4%
Gran > 2621.8%782.2%
Gran > 1571.6%35610.1%
Gran > 0.5902.6%1785.1%
Gran > 0.2530.1%1213.4%
Gran > 0.1140.4%210.6%
Gran < 0.1942.7%391.1%
Total3471100.0%3513100.0%
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Carrasco Baquero, J.C.; Caballero Serrano, V.L.; Romero Cañizares, F.; Carrasco López, D.C.; León Gualán, D.A.; Vieira Lanero, R.; Cobo-Gradín, F. Water Quality Determination Using Soil and Vegetation Communities in the Wetlands of the Andes of Ecuador. Land 2023, 12, 1586. https://doi.org/10.3390/land12081586

AMA Style

Carrasco Baquero JC, Caballero Serrano VL, Romero Cañizares F, Carrasco López DC, León Gualán DA, Vieira Lanero R, Cobo-Gradín F. Water Quality Determination Using Soil and Vegetation Communities in the Wetlands of the Andes of Ecuador. Land. 2023; 12(8):1586. https://doi.org/10.3390/land12081586

Chicago/Turabian Style

Carrasco Baquero, Juan Carlos, Verónica Lucía Caballero Serrano, Fernando Romero Cañizares, Daisy Carolina Carrasco López, David Alejandro León Gualán, Rufino Vieira Lanero, and Fernando Cobo-Gradín. 2023. "Water Quality Determination Using Soil and Vegetation Communities in the Wetlands of the Andes of Ecuador" Land 12, no. 8: 1586. https://doi.org/10.3390/land12081586

APA Style

Carrasco Baquero, J. C., Caballero Serrano, V. L., Romero Cañizares, F., Carrasco López, D. C., León Gualán, D. A., Vieira Lanero, R., & Cobo-Gradín, F. (2023). Water Quality Determination Using Soil and Vegetation Communities in the Wetlands of the Andes of Ecuador. Land, 12(8), 1586. https://doi.org/10.3390/land12081586

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop