Open AccessArticle

Land Degraded by Gold Mining in the Ecuadorian Amazon: A Proposal for Boosting Ecosystem Restoration Through Induced Revegetation

Fiodor N. Mena-Quintana

^1,2,

Willin Álvarez

^1,3,

Wilfredo Franco

⁴

Luis Moncayo

⁴,

Myriam Tipán

^3,5,*

and

Jholaus Ayala

Escuela de Posgrados, Universidad Regional Amazónica Ikiam, Kilómetro 7 vía a Alto Tena, Tena 150101, Ecuador

Colegio de Ingenieros en Gestión Ambiental del Ecuador (CIGAE), Tena 150101, Ecuador

Facultad de Ciencias Socio Ambientales, Universidad Regional Amazónica Ikiam, Kilómetro 7 vía a Alto Tena, Tena 150101, Ecuador

⁴

Facultad de Ciencias de la Tierra y Agua, Universidad Regional Amazónica Ikiam, Kilómetro 7 vía a Alto Tena, Tena 150101, Ecuador

⁵

Instituto de Ingeniería, Universidad Nacional Autónoma de México, C.U., Ciudad de México 04510, Mexico

Author to whom correspondence should be addressed.

Forests 2025, 16(2), 372; https://doi.org/10.3390/f16020372

Submission received: 9 November 2024 / Revised: 20 January 2025 / Accepted: 24 January 2025 / Published: 19 February 2025

(This article belongs to the Section Forest Ecology and Management)

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Figure 1
Geographic location and layout of the experimental site for revegetation in the Naranjalito sector, Napo province, Ecuador. The study area covers approximately 0.5 hectares along the Jatunyacu River and is divided into four experimental blocks (A, B, C, and D), each with treatment plots (T1, T2, T3, and T4) designated for different biocompost dosages and controls. Insets show the location of the Napo province within Ecuador and South America. "> Figure 2
Revegetation methods applied to 0.5 ha of degraded soil: overview of nine sequential stages. "> Figure 3
Experimental design showing four blocks and 16 plots (300 m2 each) with the distribution of Ochroma pyramidale and Arachis pintoi plants. "> Figure 4
Soil geochemical analysis showing elemental concentrations of 13 elements that exceed the maximum permissible limits (indicated by the red lines) according to Ecuadorian environmental standards. "> Figure 5
Evolution of organic matter in plots before and after treatment with plant biocompost. "> Figure 6
Temporal analysis of dasometric parameters in Ochroma pyramidale over a 6-month period, including basal area (A), height (B), stem volume (C), crown diameter (D), and number of leaves (E), with data recorded at three different time points. "> Figure 7
Biplot representation of the Ochroma pyramidale dasometric variables using the treatments and blocks as illustrative variables with records at 15 days, 90 days, and 180 days. "> Figure 8
Survival rate of Ochroma pyramidale according to generalized linear model where significant differences are observed between treatments. "> Figure 9
(A) Percentage of Arachis pintoi ground cover by plot. (B) Percentage of Arachis pintoi ground cover achieved by treatments over a 6-month period. "> Figure 10
Dry mass of Ochroma pyramidale after 3 months of planting according to vegetative tissue (A) and treatments (B). "> Figure 11
Dry mass biomass data of Arachis pintoi according to plots (A) and treatments (B) in a period of 3 months. "> Figure 12
Q–Q plot to evaluate the normality of the transformed residuals. "> Figure 13
Orthophoto images with the vegetation cover reached after 6 months (A). Remote sensing images with vegetation cover RGB (3.45 cm/pixel) in 6 months (B). "> Figure 14
(A) Vegetation coverage surface (m2) according to treatments and blocks. (B) Vegetative cover in the plots after 6 months of study. ">

Versions Notes

Abstract

Deforestation caused by gold mining in the Ecuadorian Amazon has increased by 300% in the last decade, leading to severe environmental degradation of water and land resources. Effective remediation and revegetation technologies are still needed to address this issue. This study aimed to foster revegetation on 0.5 hectares of degraded land in Naranjalito, a mining site in the Ecuadorian Amazon, by applying plant-based biocompost and biochar and planting Ochroma pyramidale and Arachis pintoi, two pioneer species. The project’s objective was to evaluate the impact of these treatments on vegetation cover recovery through physicochemical and microbiological improvements in the soil. Four blocks and sixteen experimental plots were established: eight plots received treatments with varying doses of biocompost+biol (BIOC), four plots included plantations without biocompost (Not-BIOC), and four served as control plots (bare land). Over six months, dasometric characteristics of O. pyramidale and the expansion of A. pintoi were recorded. The data were analyzed using multivariate methods. The results revealed significant differences between treatments, with BIOC plots T4 and T1 showing greater improvements in vegetation development compared to Not-BIOC plots T3 and T2, confirming the positive influence of biocompost+biol. The BIOC treatment favored not only the planted species but also the secondary successional plant communities including certain grasses, leguminous plants, and other shrub and tree species, thus accelerating the revegetation process. This study demonstrates that biocompost application is an effective strategy to enhance plant recolonization on land severely degraded by gold mining in the Ecuadorian Amazon.

Keywords:

soil enhancement; land rehabilitation; ecological recovery; vegetation cover; biocompost; climate change; forestry resources

1. Introduction

The Ecuadorian Amazon is known worldwide for its exceptional biodiversity, yet this richness is increasingly threatened by the expansion of conventional agriculture and extractive activities, particularly mining. These activities severely impact the environment, endangering the regional biodiversity and degrading critical ecosystems, with profound implications for native species and local communities [1,2,3]. In 2022 alone, Ecuador experienced a loss of 51.7 thousand hectares of natural forest, resulting in approximately 36.9 million tons of CO₂ emissions. This ongoing deforestation, largely driven by agriculture, mining, and oil activities, not only reduces forest cover but also degrades soil and water quality, which are essential to local biodiversity [3,4]. Such degradation is directly linked to species extinction, the decline of ecosystem services, and the exacerbation of greenhouse gas emissions, including CO₂.

In the Ecuadorian Amazon, gold mining has significantly expanded over recent years, especially between 2015 and 2021, when deforestation due to mining increased by 300%, encompassing 5616 additional hectares. As of 2021, mining activities affected a total of 7495 hectares, with the Napo province experiencing the largest growth in mining area. In 2015, Napo had 270 hectares dedicated to mining, and, by 2021, an additional 855 hectares were occupied, reaching 1125 hectares—a 316% increase [5]. This rapid expansion has caused extensive deforestation, soil erosion, and contamination of water and soil resources, resulting in environmental degradation and substantial threats to local biodiversity. These changes also disrupt the traditional lifestyles of local populations, including indigenous communities, as observed in Naranjalito, Puerto Napo parish. Here, alluvial gold mining has deforested approximately 40 hectares, stripping topsoil and subsoil layers, leading to serious environmental degradation with detrimental effects on the local population [6,7].

Open-pit mining introduces toxic heavy metals into the environment, which are dangerous due to their toxicity, tendency to bioaccumulate, and slow natural recovery rates. Common toxic elements include mercury (Hg), lead (Pb), cadmium (Cd), zinc (Zn), and arsenic (As), all of which can contaminate ecosystems even at low concentrations, causing harmful effects on living organisms [8,9]. Mercury, in particular, is a contaminant of global concern with a history of severe environmental and health impacts, as seen in cases like Minamata disease in Japan and documented methylmercury exposure incidents in the Amazon [10].

Studies evaluating water quality in Andean-Amazonian streams impacted by gold mining (GM) in Ecuador have shown that mining reduces environmental quality by 30%–53% [7]. The absence of aquatic macroinvertebrates in 35% of the sampling sites serves as a strong indicator of severe contamination in rivers in the upper Napo River basin, with levels of heavy metals, including copper, iron, lead, aluminum, and manganese, frequently exceeding Ecuadorian regulatory limits for the conservation of aquatic and wildlife [11,12]. Further research on rivers affected by mining in the Anzu, Jatunyacu, and Napo watersheds has revealed high levels of toxic contaminants, with over 80% of sampled sites showing significant degradation in water quality and sediment toxicity [13].

The issue of solid waste management further complicates Ecuador’s environmental challenges. The country produces an estimated 13,372 tons of solid waste daily, totaling 4.88 million tons per year, of which 55.65% is organic. Despite this, only 3.7% of waste is used for recycling and composting, with the remaining 96.3% either buried or sent to co-processing facilities. This underutilization represents a missed opportunity for waste to be converted into biofertilizers that could support environmental restoration, specifically in mining-degraded areas [14,15]. Organic waste decomposition in landfills releases methane, a potent greenhouse gas, underscoring the environmental impact of inadequate waste disposal [16].

Given the rapid degradation of soil and ecosystems, alternative methods for environmental restoration are urgently needed. Transforming organic waste into soil amendments has been shown to improve soil fertility, structural stability, and biological activity, enhancing its productive and environmental functions [17]. Moreover, circular economy initiatives that encourage ecosystem regeneration through sustainable production and consumption can help mitigate solid waste issues [14]. Utilizing organic waste to promote revegetation of degraded lands offers a viable strategy for medium- to long-term ecological restoration, facilitating the recovery of environmental goods and services in impacted ecosystems [18].

Revegetation is an effective method for restoring soil fertility by improving its chemical, physical, and biological attributes, which are essential for delivering ecosystem services and supporting biodiversity. Studies have shown that vegetation cover substantially reduces runoff and erosion rates, providing soil protection and promoting aggregate stability [19,20]. Among the key techniques in environmental restoration, phytoremediation is widely recognized, as it leverages native plants, soil amendments, and agronomic practices to contain and reduce soil contaminants, thereby restoring ecosystem health [21,22,23].

Active restoration using species such as Ochroma pyramidale (balsa) and Arachis pintoi (forage peanut) has shown promise in accelerating carbon recovery in tropical forests impacted by human activities [24]. Over half of all tropical forests are now considered degraded due to human impact, which threatens their conversion to agricultural lands, risking substantial biodiversity and carbon losses [25]. Restoring these areas could accelerate aboveground carbon density (ACD) recovery, though challenges related to cost and effectiveness remain [24,26].

Ochroma pyramidale, a fast-growing tropical tree species, is extensively used in reforestation programs and mixed agroforestry systems in degraded areas, particularly for its high survival rates, agroforestry adaptability, and rapid growth [16,27,28,29]. Arachis pintoi, a perennial legume with unique reproductive traits and low growth habits, is widely utilized as ground cover due to its soil-decompacting abilities, pasture enhancement properties, and carbon sequestration potential [30,31,32].

The use of forest litter and mountain microorganisms plays an essential role in active restoration by contributing to soil biodiversity and bioremediation. These microorganisms facilitate organic matter decomposition and detoxification, improving soil health and supporting vegetation growth in degraded areas [33,34,35]. Biochar, a carbon-rich, fine-grained material, has demonstrated effectiveness in degraded soil restoration, enhancing carbon storage, nutrient retention, and soil quality while reducing irrigation and fertilizer needs [36,37,38].

This study underscores the urgent need to implement ecological restoration techniques in mining-degraded areas, particularly in Ecuador’s Amazon region. The integration of biofertilizers, revegetation with native species, and biochar application offers a promising approach to reversing soil degradation, restoring ecosystem function, and mitigating environmental impacts caused by extractive activities.

2. Materials and Methods

2.1. Study Area

2.1.1. Location and Characteristics

The project was located in the community Naranjalito, province of Napo, which, located within the Ecuadorian Amazon, is no exception to these challenges. As noted earlier, it has suffered severe environmental impacts from both legal and illegal alluvial gold mining activities, with an increase in this province of 300% compared to records from 2015 [5]. These activities have led to the deforestation of approximately 40 hectares of forest and chakras in the Naranjalito community, Puerto Napo parish, resulting in contamination and alteration of water sources and soil, loss of local biodiversity, and general environmental degradation. The described situation offered the opportunity to carry on this research. Actions to reverse degradation processes, foster revegetation, and restore soil health are urgently required as a means of promoting ecological restoration [7].

To address this situation, the restoration and revegetation of degraded soils in Napo, exploring new methodologies and appropriate procedures is crucial, as restoration activities and experiences remain limited. In this context, efficient microorganisms have demonstrated beneficial metabolic functions, especially in improving soil quality and managing agricultural waste [7]. Developed by Professor Teruo Higa in Japan, these microorganisms have shown potential to enhance soil quality, boost soil health, and increase crop yields.

Inadequate solid waste management is another significant environmental problem in Ecuador. Poor waste disposal practices in landfills lead to decomposition processes that release methane, a potent greenhouse gas [16]. The country is estimated to produce around 13,372 tons of solid waste daily, resulting in an annual production of 4.88 million tons, 55.65% of which is organic waste. However, only 3.7% of this total waste is utilized (for recycling and composting), while the remaining 96.3% is typically buried or sent to co-processing plants [14]. This situation indicates that most organic waste is not being utilized effectively, representing an opportunity to convert this waste into biofertilizers for application in revegetation efforts in degraded areas [22,23].

The research site is located on the banks of the Jatunyacu River, south of Tena, in the Naranjalito community, Puerto Napo parish, Napo province. It lies approximately 10 km south of from the city of Tena, at an altitude of 456.29 masl, within the metallic gold mining concession known as Confluencia [15] (Figure 1), with geographical coordinates WGS 84 UTM 17 S: (854,647.6271 E; 9,881,467.952 N).

The majority of Puerto Napo’s rural parish landscape corresponds to the flat relief of the lower Amazon basin, bordering the Jatunyacu, Anzu, Puni, and Napo rivers. The area experiences a tropical semi-humid to humid climate, with over 3000 mm annual rainfall and temperatures ranging from a minimum of 18 °C to a maximum of 34 °C with an average temperature of 26 °C. Relative humidity levels vary between 80% and 90%, and annual rainfall ranges between 800 mm in some high mountain areas and over 3125 mm in lowland [39].

2.1.2. Mining Area

The operational phase of the “Confluencia” mining concession involves simultaneous exploration and exploitation of metallic gold ores in alluvial deposits along the Jatunyacu River. This process uses an open-pit mining method with parallel or transverse strips relative to the river [15]. During operations, vegetation cover is cleared, and 30 × 30 m pits are excavated to reach bedrock at depths between 5 and 8 m. Heavy machinery is used to remove and displace organic soil and rocky sedimentary subsoil materials.

After extracting and processing the free gold through gravimetric methods, smelting, and commercialization, the mining operator reclaims the mined land by filling the pits with the processed material, including sand, gravel, and stones [14,15]. Usually, the previously removed A1 horizon of the soil is not replaced.

2.2. Experimental Design

Treatments and Controls

An experimental model based on a complete randomized block design was applied over an area of approximately 0.5 ha (Figure 1) [40]. This area was divided into four blocks (A, B, C, D), each covering around 1200 m². Each block was further subdivided into four plots of approximately 300 m², with a 3 m buffer strip included around each plot as a protective zone.

Three dosages of mixed organic amendments, consisting of biocompost and biochar (BIOC) supplemented with biol from mountain microorganisms (BIOL), were applied. In Treatment T1, Ochroma pyramidale received 6.1 kg/plant of biocompost, while Arachis pintoi received 0.5 kg/plant. Additionally, 80 kg of biocompost was sprayed per plot, along with 25 L of BIOL per plot, totaling 100 L per treatment. This dosage resulted in a total application of 1128 kg of biocompost per treatment, equivalent to an application rate of 13.54 t/ha; see Table 1 and Table 2.

For Treatment T4, Ochroma pyramidale received 15.65 kg/plant of biocompost, and Arachis pintoi received 1.0 kg/plant. An additional 240 kg of biocompost was sprayed per plot, along with 50 L of BIOL, yielding a total of 200 L per treatment. This resulted in 2852 kg of biocompost per treatment, corresponding to an application rate of 34.24 t/ha.

Treatment T3 involved planting Ochroma pyramidale and Arachis pintoi without the application of either biocompost or BIOL (BIOC: 0.00 t/ha; BIOL: 0.00 L/plot). In the control treatment, T2, no biocompost or BIOL was applied, and no plant species were introduced (BIOC: 0.00 t/ha; BIOL: 0.00 L/plot).

2.3. Soil and Vegetation Analysis

2.3.1. Soil Physical Chemical Analysis

The physicochemical analysis of the soil was conducted following both the Instituto Nacional de Investigaciones Agropecuarias (INIAP) methodology [40] and the regulations of Ministerial Agreement 097-A MAATE [41], ensuring compliance with the standards established in the Ecuadorian technical norm. Soil texture (sand, silt, and clay) was determined using the Bouyoucos method; pH was measured through the potentiometric method (soil-to-water ratio of 1:2.5), and C was determined using the Walkley and Black method. The result was converted into soil organic matter by multiplying by 1.72 and again by 1.1. The hypothesis is that, in most Ecuadorian soils, the average C content in the soil organic matter is 58%, and the analytical error is about 10%. Elements such as N-NH4, P, K, Ca, Mg, Fe, Cu, Mn, and Zn were quantified using Olsen extraction solution, while S and B were analyzed with a monobasic calcium phosphate extraction solution. Analyses of P, N-NH4, S, and B were performed via UV–Visible spectrophotometry, and those of K, Ca, Mg, Fe, Cu, Mn, and Zn were conducted using atomic absorption spectrophotometry. Sampling was carried out randomly, employing a “zig-zag” approach in accordance with the Surface Sampling Method outlined in the Ministerial Agreement. Three subsamples were collected per plot at a depth of 0–40 cm to create composite samples at two different time points. Tools such as rods, shovels, zip-lock bags, and buckets were used, obtaining 1.5 kg of soil per plot, which were properly labeled. Additionally, four samples were collected from the reference ecosystem: two from farmland and two from secondary forest areas. Both methodologies complemented each other to ensure the precision and validity of the analyses.

2.3.2. Temporary Forest Nursery

Seeds and seedlings were cultivated in a temporary nursery for a period of two months under controlled conditions of light, humidity, temperature, and ventilation. The substrate was homogeneously prepared with a mix of organic soil (60% coffee husks, 20% sand, 20% ash) and placed in 10 × 15 cm polyethylene bags. The Ochroma pyramidale plants were propagated from seeds collected from selected trees with favorable phenotypic traits, while Arachis pintoi was propagated using local vegetative material.

2.3.3. Organic Waste Processing

To produce plant-based biocompost, 5 tonnes of municipal organic waste were collected from the Cooperativa de Consumo La Maná Ltd.a. market (Figure 2, Phase 4). This waste comprised 76.6% vegetables, 17.2% fruits, 3% cereals, 2.2% other organic materials, and 1% flowers, which were further supplemented with forestry and agro-industrial residues.

For physical treatment, a motor-driven metal mill (14 HP, 180 kg/h) was used to grind and crush the biomass. The leachate, containing humic and fulvic acids from the grinding process, was collected to enrich the biocompost and enhance its microbiota. Biochar was produced by pyrolysis (500–600 °C) using locally sourced forest residues and processed with artisanal equipment (Figure 2, Phase 5 and 6) [35]. To accelerate the degradation of shredded organic waste, a biological treatment was applied under controlled aerobic conditions using a combination of forest litter and leachates produced during the shredding process, with environmental factors such as humidity, oxygenation, and temperature carefully regulated.

The shredded municipal organic waste was mixed with various biomass substrates in predefined quantities: rice hulls (0.41 tonnes), sawdust (0.21 tonnes), biochar (0.18 tonnes), molasses (0.11 tonnes), and sodium bentonite (0.05 tonnes). This mixture had a C:N ratio of 42.29:1 [42]. It was placed in ventilated metal containers to facilitate the different stages of biodegradation (mesophilic, thermophilic, cooling, and maturation), resulting in plant biocompost after approximately 90 days.

2.3.4. Preparation of Plant Biocompost and Biochar

The shredded municipal organic waste was combined with biomass substrates in predetermined dosages, rice hulls (0.41 tonnes), sawdust (0.21 tonnes), biochar (0.18 tonnes), molasses (0.11 tonnes), and sodium bentonite (0.05 tonnes), achieving a carbon-to-nitrogen ratio of 42.29:1 [42]. The mixture was placed in ventilated metal containers to enable various biodegradation phases (mesophilic, thermophilic, cooling, and maturation) over approximately 90 days [43], yielding plant-based biocompost.

Liquid biocompost (BIOL) was produced from plant biocompost amendments, utilizing nutrients and beneficial microorganisms within the substrate [43]. In two 55-gallon water tanks, 10 kg of solid biocompost (contained in a perforated bag) was added to each tank, along with 1 L of humic and fulvic acids from the leachates of the mechanical pre-treatment and 1 L of molasses per tank [44] (Figure 2, Phase 7). This mixture was allowed to ferment for 21 days before its first application. Although the recommendation specifies a period of 5 to 15 days [44], the decision was made to extend it to 21 days to enhance the load of beneficial microorganisms.

2.3.5. Physicochemical and Microbiological Properties of Biocompost

Two composite samples of plant-based biocompost and a 500 mL composite sample of BIOL were randomly collected to analyze their physical, chemical, and microbiological properties. The analyses were conducted at the Central Experimental Station of the Amazon (INIAP) laboratory. Microbiological analysis quantified the colony-forming units (CFU) per gram of soil for the microorganisms present, including fungi, yeasts, and bacteria. Serial dilution techniques were applied for fungi quantification [45], while specific nutrient agar media were used for beneficial bacteria, and Pseudomonas F agar was used for pathogenic bacteria. CFU quantification was performed using an illuminated colony counter [46]. For the physicochemical analysis of the biocompost, the same methodology established by INIAP [40] was applied, which was also used for the soil analysis. Additionally, five predetermined doses of BIOL were applied via fumigation in the revegetation area.

2.3.6. Ochroma pyramidale and Arachis pintoi Plantations

The Ochroma pyramidale trees were planted with a spacing of 4 × 4 m (20 plants per plot), totaling 240 plants across treatments T1, T3, and T4. Holes were dug manually with dimensions of 40 × 40 × 40 cm, with larger stones and gravel removed (Figure 2, Phase 8). The extracted subsoil was mixed with biocompost in the prescribed treatment dosages (Table 1 and Table 2), forming a mound about 20 cm above the soil surface [47,48].

Arachis pintoi seedlings were planted with a spacing of 1 × 1 m (160 plants per plot), resulting in 640 plants per treatment, totaling 1920 seedlings across treatments T1, T3, and T4; see Table 1. Manual holes measuring 15 × 15 × 15 cm were dug, and large stones and gravel were removed (Figure 2, Phase 8) [45]. The extracted subsoil was mixed with biocompost in the specified treatment dosages (Table 2) for planting.

2.3.7. Post-Planting Management

Post-planting management included applying solid (BIOC) and liquid (BIOL) biocompost amendments to each plant, with uniform sprays applied to the soil and foliage across the plot. This was conducted according to the treatment dosages, along with replanting to replace mortality. Additionally, we applied a homemade organic repellent to protect against leaf-mining and leaf-cutting insects. This repellent was made using chili (Capsicum annuum, 50 g), garlic (Allium sativum, 250 g), onion (Allium cepa, 500 g), tobacco (Nicotiana tabacum, 20 g), vegetable oil (20 g), and soap (100 g). Both the vegetable oil and soap are combined to create an emulsion that functions as an adhesive, ensuring the product adheres effectively to the plant foliage. The dosage guidelines were determined following the recommendations outlined in the Ecotechnology Manual for Food and Nutritional Security [49]. The ingredients were blended and macerated for 24 h before foliar application, ensuring minimal impact on biodiversity [49]. This treatment was applied six times over four months.

2.4. Temporal Assessment Records of Dasometric Variables in Ochroma pyramidale

2.4.1. Evaluation of Dasometric Variables in Ochroma pyramidale

For the species Ochroma pyramidale, dasometric variables were measured over a six-month period. The data collected included age, basal diameter (BD cm), basal area (BA cm²), terminal diameter (TD cm), total height (TH cm), stem volume (SV cm³), leaf count (LC), and crown diameter (CD cm), with three separate data recordings. The first measurements were taken at 15 days, the second at 90 days, and the third at 180 days after planting (Figure 3).

2.4.2. Evaluation of Vegetative Cover in Arachis pintoi

Vegetative cover was assessed for Arachis pintoi and colonizing species three times over a six-month period. Vegetative cover data for Arachis pintoi and pioneer species were sampled randomly using the 1 m² quadrat method, with two replicates per plot. The coverage percentages of the four segments within each 1 m² quadrat (25% each) were visually estimated [44].

2.4.3. Biomass Analysis: Dry Mass of Ochroma pyramidale and Arachis pintoi

The dry mass assessment of Ochroma pyramidale was conducted across all experimental units three months after planting, using random sampling. Forty plants were extracted, distributed across 16 plots in the treatments (T1, T3, T4) and in the T2 control group, which included naturally regenerating seedlings (Figure 2, Phase 8). The biomass samples were dried for 72 h and then divided into root, stem, and leaf segments, which were labeled and placed in sterile zip-lock bags for grinding and weighing [50,51]. For the dry mass measurement of Arachis pintoi, plants were randomly sampled across all experimental units three months after planting using the 1 m² quadrat method, with three replicates per plot (Figure 2, Phase 8) [50]

2.4.4. Analysis of Vegetative Cover Using Aerial Imagery

At the end of the six-month research period, an aerial imagery analysis was conducted to assess vegetative cover. This was achieved using a Phantom 4 Pro V2 drone, which provided images with a resolution of 3.45 cm/pixel, offering sufficient detail to differentiate and classify areas of bare soil and those with vegetative cover (Figure 3, Phase 9) [49]. After capturing the aerial images, an orthophoto with corrected georeferencing in “UTM 17S” was created using Agisoft Metashape software version 10.8 [49]. Vegetative cover classification was performed in ArcGIS version 2.2.0, applying supervised classification techniques to distinguish between bare soil and vegetated areas.

2.5. Statistical Analysis

2.5.1. Temporal Multivariate Analysis

A multivariate analysis of variance based on permutations (PerMANOVA) was used to analyze the dasometric data [52]. This analysis was conducted using the R software version 4.3.1, specifically with the adonis2 function from the vegan package, employing 9999 unrestricted permutations to ensure the statistical robustness of the results. To quantify differences between treatments, a post-hoc Tukey’s test was applied at a 95% confidence level [53].

2.5.2. Vegetative Cover Analysis

The analysis of variance (ANOVA) for vegetative cover was also performed using the R software. Assumptions of normality and homogeneity of variances were tested: residual normality was evaluated with the Shapiro–Wilk test, and homogeneity of variances between treatments was assessed using Bartlett’s test, both in R [52,54].

2.5.3. Statistical Model Used

y_{i j k} = M + τ_{i} + β_{j} + ε_{i j k}, i = 1,2, 3,4 . j = 1,2, 3,4 . j = 1, \cdot \cdot \cdot, 16 .

(1)

where:

y_ijk represents the k-th observation of one of the measured variables due to the i-th treatment in the j-th block.

Μ: the global mean of the variable studied.

τ_i: the effect of the i-th treatment.

β_j: the effect of the j-th block.

ε_ijk: the random error term.

Model (1) is applied to explain the measured soil variables and the dasometric data of the study plants. This model corresponds to a randomized block design, (Figure 3). Given the multiple dasometric measurements, a biplot principal component analysis is recommended, using both blocks and treatments as illustrative variables.

2.5.4. Survival and Mortality Analysis of Ochroma pyramidale

A generalized linear model (GLM) analysis was used to evaluate the survival and mortality rates of the forest species Ochroma pyramidale within treatments T1, T3, and T4. For post-hoc comparisons between treatments, the dplyr and multcomp libraries were used in R [52,54].

3. Results

3.1. Heavy Metal Contamination

The Bruker S1 TITAN X-ray fluorescence (XRF) spectrometer detected thirteen elements in the soil samples (concentration in parenthesis): As (13 ppm), S (393 ppm), Ba (774 ppm), Cd (5 ppm), Co (10 ppm), Cu (66 ppm), Cr (84 ppm), Sn (7 ppm), Hg (2 ppm), Pb (22 ppm), Se (1 ppm), V (124 ppm), and Zn (101 ppm). Here, “ppm” refers to parts per million, equivalent to mg/kg (1 ppm = 1 mg/kg). All of these concentrations exceed the maximum permissible limits specified by Ecuadorian environmental regulations (Figure 4), particularly under the Ministerial Agreement 097-A MAATE [41]. According to the Soil Quality Guideline and the Remediation Guideline for Contaminated Soils (Book VI, Annex 2), this contamination level is classified as a degree 3 disturbance, indicating a “very severe disturbance” within the framework of these regulations. Once the contamination was confirmed, native plants with potential for heavy metal bioaccumulation were selected, specifically Ochroma pyramidale and Arachis pintoi. According to the literature, Ochroma pyramidale has been identified as a phytoremediator species [55].

3.2. Physicochemical and Microbiological Analysis of Biocompost

The physicochemical analysis of the solid biocompost (BIOC) revealed concentrations exceeding the optimal reference levels for nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), zinc (Zn), copper (Cu), iron (Fe), manganese (Mn), and organic matter. Boron (B) remains within the optimal range of 0.5 to 1, as established by the Instituto Nacional de Investigaciones Agropecuarias (INIAP) [40]. The pH values are within acceptable levels, in accordance with Table 1 of Ministerial Agreement 097-A [41].

As shown in Table 3, the physical analysis of solid-state biocompost indicates that it is composed of 86% organic matter. Of this, 24.05% corresponds to the fine fraction, while 75.95% represents the coarse fraction. The remaining 14% is composed of mineral components such as sand, silt, and clay.

In contrast, the analysis of liquid biocompost (BIOL) revealed that nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), iron (Fe), and copper (Cu) levels are well below the optimal reference values. However, zinc (Zn) falls within the optimal range of 2 to 7, and boron (B) exceeds the optimal reference levels, as defined by the INIAP parameters [40].

The microbiological analysis (Table 3) showed the presence of beneficial bacteria from the genera Bacillus spp. and Pseudomonas spp., along with beneficial fungi from the genus Penicillium spp. and significant quantities of yeasts. (Note: the abbreviation “spp.” denotes multiple species within the same genus.) The analysis revealed the presence of macronutrients, micronutrients, and beneficial microorganisms, which significantly enhanced the physicochemical and microbiological properties of the soil, supporting improved plant growth and development.

3.3. Physicochemical Analysis of Soil

Nitrogen decreased in the second record, possibly due to leaching and the absorption of the established plants; however, the T4 and T1 treatments where bio-compost was implemented remained elevated compared to the T3 and T2 (control) treatments. Calcium and magnesium levels had a decrease in the second recording in all treatments, but these elements remained high in the two treatments where biocompost was implemented (T1 and T4). In the second record, the potassium level decreased in the T1, T2, and T3 treatments, and, in T4, it remained significantly elevated; these concentrations exceeded the values of the reference ecosystems. As expected in a soil with a very coarse texture and very low cation exchange capacity, the values of NH4, Ca, Mg, and K were reduced in the 6-month period, except where the greatest amount of biocompost and biol (T4) was applied. In Figure 5, the presence of organic matter in the first record within the research area was low compared to the second record after the application of biocompost. Treatment T4 showed the highest concentration of organic matter, followed by T3 and T1, exceeding the amount of organic matter present in the reference ecosystem “Chacra”.

3.4. Analysis of the Temporal Evolution of the Dasometric Variables of Ochroma pyramidale

The relationships of the dasometric variables of the species Ochroma pyramidale were evaluated across three records, enabling the determination of the temporal evolution of characteristics such as basal area, height, stem volume, crown diameter, and number of leaves. The objective of employing contrasting experimental treatments with three dosages was to identify the most effective dose for achieving significant differences in the dasometric variables of Ochroma pyramidale and the vegetative cover of Arachis pintoi.

The results indicate significant growth in the T4 BIOC treatment (34.24 t/ha), followed by the T1 BIOC treatment (13.54 t/ha), with the lowest values observed in the T3 BIOC treatment (0.00 t/ha). While plant growth is influenced by the higher amount of biocompost applied, determining an optimal minimum or maximum dose is essential to ensure the success of revegetation and to optimize resource use.

The relationships of the dasometric variables of the species Ochroma pyramidale were evaluated in three records, see Figure 6, allowing the determination of the temporal evolution of its characteristics, including basal area (BA, cm²), height (H, cm), stem volume (SV, cm³), crown diameter (CD, cm), and number of leaves. The results show significant growth in all variables in the T4 BIOC treatment (34.24 t/ha), followed by T1 BIOC (13.54 t/ha), with the lowest values consistently recorded in the T3 treatment, which corresponds to the application without BIOC (0.00 t/ha). This analysis highlights the effectiveness of higher doses of biocompost (T4) in promoting the dasometric development of Ochroma pyramidale compared to intermediate doses (T1) and treatments without BIOC (T3).

Figure 7 shows the results of the multivariate analysis (PerMANOVA) for the dasometric variables of Ochroma pyramidale, evaluated at 15, 90 and 180 days. Highly significant differences are observed among treatments: at 180 days, T4 (34 t/ha) with p-value < 0.01, and T1 (13.54 t/ha) with p value = 0.0205 show greater increase in the dasometric variables, with emphasis on blocks C and D, in comparison with T3 (without biocompost), which presents the lowest values. This suggests that treatment T4, like treatment T1, contributed significantly to the revegetation of the degraded area, both at the plot and block level.

3.5. Survival and Mortality Rate of Ochroma pyramidale

Treatments where bio-compost was applied (T4 and T1) had a level close to one, which showed there is a high probability of survival of the forest species (Figure 8). On the other hand, T3 treatment resulted in the lowest probability of survival for blocks A and C, in contrast to blocks B and D, which are close to one. In addition, there is no significant difference between the T4 and T1 treatments where biocompost was applied (p-value: 0.56). However, there were significant differences between the T3 and T4 treatments, with a p-value of 0.027, and between T1 and T3, with a p-value of 0.041 (the T3 treatment did not receive bio-compost).

The percentage of vegetation cover achieved in the species Arachis pintoi in the treatment plots T1, T3, and T4, evaluated during a period of 6 months, recorded higher values in the plots T4D, T4B, and T4C, followed by T1C, T1D, T3D, and T1B; the lowest being T1A, T3C, T3B, T4A, and T3A (Figure 9). We observe the vegetation coverage achieved by blocks and treatments, treatment T4 BIOC (34.24 t/ha), resulted in 81.87%, which was the highest value at the third record, followed by T1 BIOC (13.54 t/ha) with 70.31%, and the lowest value being T3 BIOC (0.00 t/ha) with 50.31%.

3.6. Dry Mass of Ochroma pyramidale and Arachis pintoi Biomass

Regarding the accumulated dry mass of Ochroma pyramidale after three months of plantation, the highest biomass accumulation was recorded in the leaves, as shown in Figure 10A. Treatment T4 resulted in the highest dry mass at 70.20 g/plant, followed by T1 with 41.66 g/plant, while the lowest values were observed in T2 (3.03 g/plant) and T3 (2.45 g/plant) (Figure 10B). Regarding the dry mass of Arachis pintoi after three months of plantation, treatment T4 resulted in 28.49 g/m², followed by T1 with 26.41 g/m², and the lowest value being T3 with 24.60 g/m² (Figure 11A,B).

3.7. Vegetation Cover Study

3.7.1. ANOVA Analysis

The ANOVA analysis establishes that there are significant differences due to the effect of the treatments on the coverage area (p-value = 0.0002390); however, regarding the effect of the blocks, it is determined that they do not have a significant effect on the response variable (p-value = 0.2929605); see Table 4. The means of the areas for treatments T1, T3, and T4 show significant differences compared to the control T2 but do not exhibit significant differences among themselves.

The Q–Q Plot confirms the homogeneity of the grouped data, as most points align with the theoretical line, indicating that the residuals follow a normal distribution (Figure 12). This supports the validity of the applied ANOVA analysis.

3.7.2. Aerial Images and Supervised Classification Techniques Analysis

Treatment T1 achieved 93.57% vegetation cover, with only 6.43% corresponding to bare soil. This result suggests that the plots under Treatment T1 exhibit a high level of vegetation cover. In contrast, Treatment T2 presented a different scenario, with only 19.18% vegetation cover and 80.82% bare soil. Similarly, Treatment T3 showed that 74.63% of the surface was covered by vegetation, while 25.37% remained as bare soil. Although vegetation is still predominant in Treatment T3, one-quarter of the plots’ surface remains uncovered. Finally, Treatment T4 demonstrated vegetation cover levels similar to those of Treatment T1, with 89.68% vegetation cover and only 10.32% bare soil. These results suggest that Treatments T4 and T1 significantly contributed to the revegetation of the degraded area (Figure 13).

Vegetation cover classification was performed using the ArcGIS 10.8 software, applying supervised classification techniques that differentiated bare soil from vegetated areas [56]. Aerial images, obtained using orthophotogrammetry and remote sensing tools, illustrate the vegetation cover and bare soil achieved six months after planting in the treatment plots T1, T3, and T4, across blocks A, B, C, and D, including the T2 control plots. These images reveal greater vegetation cover in treatments T1 BIOC (13.54 t/ha) and T4 BIOC (34.24 t/ha) compared to T3 BIOC (0.00 t/ha), with T2 BIOC (0.00 t/ha) being the control treatment showing the lowest vegetation cover value.

Significantly higher vegetation cover was recorded in the T3B, T3C, and T3D plots compared to the T3A plot. This increase in vegetation is attributed to the treatments applied in the adjacent plots T4B, T4D, and T1C, which were treated with biocompost and microbiota. The positive effects of these treatments extended to the surrounding plots due to their proximity.

Additionally, the influence of the terrain’s elevation gradient played a crucial role in vegetation development. Elevation was higher in T4B (460.11 m), T4D (460.46 m), and T1B (460.57 m) compared to T3B (459.63 m), T3D (459.81 m), and T2C (458.86 m). This elevation advantage favored vegetation growth not only within the respective plots but also improved vegetation cover in the neighboring plots. Figure 14 presents the percentage of vegetation cover achieved across the different treatments.

4. Discussion

In the soil research area, we found 13 elements exceeding the maximum permissible limits according to the Ecuadorian environmental regulation: As (13 ppm), S (393 ppm), Ba (774 ppm), Cd (5 ppm), Co (10 ppm), Cu (66 ppm), Cr (84 ppm), Sn (7 ppm), Hg (2 ppm), Pb (22 ppm), Se (1 ppm) V (124 ppm), and Zn (101 ppm). The most toxic elements in nature derived from open pit mining are arsenic, cadmium, lead, and mercury, which have no known biological function and represent a danger due to their high tendency to bioaccumulate [8,9]. However, recent studies have demonstrated the benefits of using Ochroma pyramidale as a potential bioaccumulator of heavy metals through the absorption and accumulation of nutrients in its biomass [55], which, when managed in association with soil microorganisms, can reduce the concentration of contaminating elements in the environment, which is the case for revegetation in Naranjalito [57,58].

The content of organic matter and concentrations of N, P, K, Ca, Mg, S, Zn, Cu, Fe, and Mn of the solid bio-compost exceeded the optimal reference levels established by the INIAP [41], while B remains at the optimal value between 0.5 and 1. Soil organic matter content increased after 3 months of planting, and treatment T4 obtained the highest value, followed by treatment T3 and similar values in T1 and T2 (controls), even exceeding the “Chacra” reference farm ecosystem.

The PerMANOVA results for dasometric data of Ochroma pyramidale show there are highly significant differences (p-value < 0.01) due to the organic amendments applied in the treatments T4 and T1. These treatments showed the highest dasometric values, compared to treatment T3 that showed the lowest values, which significantly influenced the growth and development of the forest species, both at the beginning and after three months of study. After six months of study, the blocks show significant differences with a p-value = 0.0205, in which the applied treatments cause an increase in the development of the plants. The previous information demonstrates that the ecological characteristics of the selected species, plus the biocompost dosages applied in the treatments and the management practices implemented [57], were decisive for the success of revegetation in soils degraded by mining.

In a period of six months, the legume species Arachis pintoi reached greater plant coverage in treatment T4, with 81.87%, followed by T1, with 70.3%, compared to T3, which reached 50.31% vegetable coverage, which shows the effectiveness of the biocompost dosages and the management practices applied [56]. On the other hand, after three months of intervention, the colonizing species of natural regeneration were denser in the treatment plots T4, T1, and T3, where the dasometric characteristics, percentages of vegetation cover, and number of plant species present higher values after the application of organic amendments compared to the control T2 treatment. However, the proximity of the reference forest ecosystem causes a positive effect on the increase in colonizing species in the revegetation area, which favored its growth and development. Although it was also proven that, due to the competition generated with the planted cover species Arachis pintoi, this tends to displace the colonizing herbaceous species as it grows but not the woody shrub colonizing species that form mutualistic associations, which together play a vital function to colonize bare land and restore areas degraded by mining [58].

5. Conclusions

According to the geochemical analysis carried out on the elemental composition of the soil before treatment, 13 elements, identified as Zn, V, Se, Pb, Hg, Sn, Cr, Cu, Co, Cd, Ba, S, and As, resulted in concentrations above the maximum permissible limits according to the standards of the Ecuadorian Technical Regulation [41]. Therefore, it is concluded that the soils present contamination with a disturbance level of 3, categorized as very severe.

The results of the PerMANOVA for the dasometric data of the species Ochroma pyramidale show us there are highly significant differences between the treatments (p-value < 0.01) at 15 days and 3 months after planting that favored T4 and T1, in comparison with T3 and T2. After 6 months of study, the blocks show differences with a higher p-value, but still significant (p-value = 0.0205), in which the treatments continue to show significant differences.

The most effective contrasting experimental treatment in the growth and development of the Ochroma pyramidale species was T4 BIOC (34.24 t/ha), followed by T1 BIOC (13.54 t/ha), with the lowest values being T3 BIOC (0.00 t/ha), where significant differences were observed in the temporal evolution of the dasometric variables. Similarly, for the same species, treatment T4 obtained the highest biomass volume yields (dry mass g/plant) obtained at 90 days with 70.20 g/plant, followed by T1 with 41.66 g /plant, and well below the control T2 with 3.03 g/plant in naturally regenerative O. pyramidale plants and T3 with 2.45 g/plant. In the case of the Arachis pintoi species, the biomass data (dry mass g/plant) for T4 obtained 28.49 g/m², followed by T1 with 26.41 g/m², with the lowest value being T3 with 24.60 g/m². For the vegetation cover after six months, T4 resulted in 81.87%, while T1 resulted in 71.31% and T3 in 50.31%, which demonstrates the success of revegetation and its effectiveness in the applied treatments, due to the application of organic vegetable biocompost amendments and the management practices implemented in the area degraded by mining activities.

The soil organic matter analysis carried out in the research area demonstrated an increase in its value for T4 and T3 treatments, compared to T1 and T2 treatments, surpassing the reference farm ecosystem “Chacra”. According to the aerial image of the research area, after 6 months of Ochroma pyramidale and Arachis pintoi plantations, it is observed that T4 and T1 treatments presented an extension of the vegetation cover to the neighboring plots in the treatments T2B, T2C, T3B, T3C, and T3D, exceeding the buffer zone and the limits established in each plot. This extension of the vegetation cover generated a positive impact on the gain of vegetation cover in the neighboring plots.

In treatment T1, a high vegetation cover was observed, reaching 93.57% of the surface, while only 6.43% corresponds to bare soil. On the contrary, treatment T2 showed a low vegetation cover of 19.18%, and 80.82% of bare soil. In treatment T3, an intermediate situation is observed, with 74.63% of the surface covered by vegetation and 25.37% bare soil. Finally, treatment T4 presented a vegetation cover similar to the treatment T1, with 89.68% vegetation and only 10.32% bare soil. This suggests that treatment T4, like treatment T1, contributed significantly to the revegetation of the degraded area and enhanced the growth of naturally regenerating colonizing species.

Author Contributions

Conceptualization F.N.M.-Q., W.F., W.Á. and L.M.; methodology F.N.M.-Q. and W.F.; software, W.Á., F.N.M.-Q. and J.A.; validation, W.F., W.Á. and L.M.; formal analysis, F.N.M.-Q., W.Á. and J.A.; investigation, F.N.M.-Q.; resources, F.N.M.-Q.; data curation, F.N.M.-Q., W.F. and W.Á., writing—original draft preparation, F.N.M.-Q. and M.T.; writing—review and editing, M.T., F.N.M.-Q., W.F., W.Á. and L.M.; visualization, F.N.M.-Q., M.T., W.Á. and J.A.; supervision, F.N.M.-Q., W.F., W.Á. and L.M.; project administration, F.N.M.-Q.; funding acquisition, F.N.M.-Q. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Due to privacy and ethical concerns, the data from this study cannot be shared. The research was conducted in areas currently affected by illegal mining activities, and disclosing specific locations or related information could pose risks to individuals and communities involved. We appreciate your understanding regarding these restrictions.

Acknowledgments

During the preparation of this manuscript the authors used OpenAI’s ChatGPT (version 4) for the purpose of translation of certain words and phrases from Spanish to English. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographic location and layout of the experimental site for revegetation in the Naranjalito sector, Napo province, Ecuador. The study area covers approximately 0.5 hectares along the Jatunyacu River and is divided into four experimental blocks (A, B, C, and D), each with treatment plots (T1, T2, T3, and T4) designated for different biocompost dosages and controls. Insets show the location of the Napo province within Ecuador and South America.

Figure 2. Revegetation methods applied to 0.5 ha of degraded soil: overview of nine sequential stages.

Figure 3. Experimental design showing four blocks and 16 plots (300 m² each) with the distribution of Ochroma pyramidale and Arachis pintoi plants.

Figure 4. Soil geochemical analysis showing elemental concentrations of 13 elements that exceed the maximum permissible limits (indicated by the red lines) according to Ecuadorian environmental standards.

Figure 5. Evolution of organic matter in plots before and after treatment with plant biocompost.

Figure 6. Temporal analysis of dasometric parameters in Ochroma pyramidale over a 6-month period, including basal area (A), height (B), stem volume (C), crown diameter (D), and number of leaves (E), with data recorded at three different time points.

Figure 7. Biplot representation of the Ochroma pyramidale dasometric variables using the treatments and blocks as illustrative variables with records at 15 days, 90 days, and 180 days.

Figure 8. Survival rate of Ochroma pyramidale according to generalized linear model where significant differences are observed between treatments.

Figure 9. (A) Percentage of Arachis pintoi ground cover by plot. (B) Percentage of Arachis pintoi ground cover achieved by treatments over a 6-month period.

Figure 10. Dry mass of Ochroma pyramidale after 3 months of planting according to vegetative tissue (A) and treatments (B).

Figure 11. Dry mass biomass data of Arachis pintoi according to plots (A) and treatments (B) in a period of 3 months.

Figure 12. Q–Q plot to evaluate the normality of the transformed residuals.

Figure 13. Orthophoto images with the vegetation cover reached after 6 months (A). Remote sensing images with vegetation cover RGB (3.45 cm/pixel) in 6 months (B).

Figure 14. (A) Vegetation coverage surface (m²) according to treatments and blocks. (B) Vegetative cover in the plots after 6 months of study.

Table 1. Plant quantities per treatment and plot.

Treatments	Plot/Area m²	Ochroma pyramidale	Ochroma pyramidale	Arachis pintoi	Arachis pintoi	Total Plants/Treatment
Treatments	Plot/Area m²	Plants/Plot	Plants/Treatment	Plants/Plot	Plants/Treatment	Total Plants/Treatment
T1	300	20	20 × 4 = 80	80	160 × 4 = 640	720
T2	300	0	0	0	0	0
T3	300	20	20 × 4 = 80	80	160 × 4 = 640	720
T4	300	20	20 × 4 = 80	80	160 × 4 = 640	720
Total Plants			240	-	1920	2160

Table 2. Biol and biocompost application rates per treatment.

Treatments	Biol	Biol	Biocompost		Biocompost	Biocompost
Treatments	L/Plot	L/Treatments	Kg/Plant Ochroma piramidale	Kg/Plant Arachis pintoi	Kg/Treatments	t/ha
T1	25	100	6.1	0.5	1128	13.54
T2	0	0	0	0	0	0
T3	0	0	0	0	0	0
T4	50	200	15.65	1	2852	34.24

Table 3. Physicochemical and microbiological analysis of solid and liquid biocompost.

Physical—Solid Biocompost
86%						14%
Organic matter (MO)						Minerals
Fine Fraction			24.05%			Sand		Silt			Clay
Coarse Fraction			75.95%			65%		24%			11%
Chemical—Solid Biocompost
pH	ppm			meq/100 mL			ppm
	N	P	S	K	Ca	Mg	Zn	Cu	Fe		Mn	B
8.55	87.05	210.1	22.46	11.36	17.71	5.12	57.36	30.75	245.1		128.5	0.5
Chemical—Liquid Biocompost
gr/100 mL (%)						ppm
N	P	K	Ca	Mg	S	Zn	Cu		Fe	Mn		B
87.05	210.1	22.46	11.36	17.71	5.12	57.36	30.75		245.1	128.5		0.5
Microbiological Biocompost
Solid						Liquid
Bacillus spp.		Pseudomonas spp.	Penicillium spp.	Yeast		Bacillus spp.		Pseudomonas spp.		Penicillium spp.		Yeast
71,000,000		2650	2650	30,000		600,000,000		490,000,000		0		5000

Table 4. ANOVA results for transformed data for vegetation cover.

	Df	Sum.Sq	Mean.Sq	F.Value	Pr..F.
Treatment	3	7.5785291	2.5261764	20.350589	0.0002390
Blocks	3	0.5386399	0.1795466	1.446407	0.2929605
Residuals	9	1.1171955	0.1241328	NA	NA

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Mena-Quintana, F.N.; Álvarez, W.; Franco, W.; Moncayo, L.; Tipán, M.; Ayala, J. Land Degraded by Gold Mining in the Ecuadorian Amazon: A Proposal for Boosting Ecosystem Restoration Through Induced Revegetation. Forests 2025, 16, 372. https://doi.org/10.3390/f16020372

AMA Style

Mena-Quintana FN, Álvarez W, Franco W, Moncayo L, Tipán M, Ayala J. Land Degraded by Gold Mining in the Ecuadorian Amazon: A Proposal for Boosting Ecosystem Restoration Through Induced Revegetation. Forests. 2025; 16(2):372. https://doi.org/10.3390/f16020372

Chicago/Turabian Style

Mena-Quintana, Fiodor N., Willin Álvarez, Wilfredo Franco, Luis Moncayo, Myriam Tipán, and Jholaus Ayala. 2025. "Land Degraded by Gold Mining in the Ecuadorian Amazon: A Proposal for Boosting Ecosystem Restoration Through Induced Revegetation" Forests 16, no. 2: 372. https://doi.org/10.3390/f16020372

APA Style

Mena-Quintana, F. N., Álvarez, W., Franco, W., Moncayo, L., Tipán, M., & Ayala, J. (2025). Land Degraded by Gold Mining in the Ecuadorian Amazon: A Proposal for Boosting Ecosystem Restoration Through Induced Revegetation. Forests, 16(2), 372. https://doi.org/10.3390/f16020372

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