Native multispecies and fast-growing forest root biomass increase C and N stocks in a reclaimed bauxite mining area

Valente, Fernanda Daniele de Almeida; Castro, Marllon Fialho de; Filho, José Ferreira Lustosa; Gomes, Lucas de Carvalho; Neves, Júlio César Lima; Silva, Ivo Ribeiro da; Oliveira, Teógenes Senna de

doi:10.1007/s10661-022-10720-6

Cited by 4 publications

(2 citation statements)

References 70 publications

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“…The negative effect of the distance from the forest edge was significantly greater in heavy machinery mining due to these lands being subject to high degrees of landscape fragmentation [74]. Meanwhile, in areas recovering from suction pumping mining, tree species diversity had a positive effect on AGB, as found previously in many studies [37,60,75]. This result indicates that these relationships are context-dependent [24], because the effect of the distance from the forest matters significantly in heavy machinery mining but not in suction pumping.…”

Section: Effects Of Mining Type Stand Age and Distance From The Fores...supporting

confidence: 57%

Resilience of Aboveground Biomass of Secondary Forests Following the Abandonment of Gold Mining Activity in the Southeastern Peruvian Amazon

Garate-Quispe,

Herrera-Machaca,

Pareja Auquipata

et al. 2024

Diversity

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Amazon rainforests are critical for providing a wide range of ecosystem services. In the Southeastern Peruvian Amazon; however, goldmining activities are causing severe soil degradation and forest loss. We analyzed aboveground biomass (AGB), forest structure, and species diversity recovery during secondary succession in 179 forest plots. Our study provides the first field-based quantification of AGB recovery following the abandonment by two types of goldmining (heavy machinery and suction pumping) in Madre de Dios (Peru). We found that successional secondary forests in areas subjected to suction pumping were more resilient than those in areas subjected to heavy machinery. After 20 years, mean AGB in suction pumping mining areas had reached 56% of reference forest AGB, while in areas of heavy machinery mining it was only 18%. Mining type, stand age, and distance from the forest edge had a significant effect on AGB. The influence of the distance from the forest edge on AGB varies according to mining type because the effects of species diversity on AGB are mediated by the distance from the forest edge. Our results clearly showed the dynamics of AGB recovery across a secondary succession after goldmining, and the contrasting responses of AGB between the two mining types. Our study disentangles the importance of key factors in forest recovery after mining and improves understanding of the resilience of biomass accumulation in these highly degraded ecosystems.

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Section: Effects Of Mining Type Stand Age and Distance From The Fores...supporting

confidence: 57%

Resilience of Aboveground Biomass of Secondary Forests Following the Abandonment of Gold Mining Activity in the Southeastern Peruvian Amazon

Garate-Quispe,

Herrera-Machaca,

Pareja Auquipata

et al. 2024

Diversity

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“…Three of these are particularly popular: the Clay Ratio (CR), the Modified Clay Ratio (MCR), and the Critical Level of Soil Organic Matter (CLOM; Olaniya et al, 2020;Babur et al, 2021;Senanayake and Pradhan, 2022). Soil conservation priorities can be set with the help of indices like the clay ratio, the modified clay ratio, and the CLOM (Vitali et al, 2019;Olaniya et al, 2020;de Almeida Valente et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia

Alqadhi,

Mallick,

Talukdar

et al. 2023

Front. Ecol. Evol.

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Soil erosion is a major problem in arid regions, including the Abha-Khamis watershed in Saudi Arabia. This research aimed to identify the soil erosional probability using various soil erodibility indices, including clay ratio (CR), modified clay ratio (MCR), Critical Level of Soil Organic Matter (CLOM), and principle component analysis based soil erodibility index (SEI). To achieve these objectives, the study used t-tests and an artificial neural network (ANN) model to identify the best SEI model for soil erosion management. The performance of the models were then evaluated using R2, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE), with CLOM identified as the best model for predicting soil erodibility. Additionally, the study used Shapley additive explanations (SHAP) values to identify influential parameters for soil erosion, including sand, clay, silt, soil organic carbon (SOC), moisture, and void ratio. This information can help to develop management strategies oriented to these parameters, which will help prevent soil erosion. The research showed notable distinctions between CR and CLOM, where the 25–27% contribution explained over 89% of the overall diversity. The MCR indicated that 70% of the study area had low erodibility, while 20% had moderate and 10% had high erodibility. CLOM showed a range from low to high erodibility, with 40% of soil showing low CLOM, 40% moderate, and 20% high. Based on the T-test results, CR is significantly different from CLOM, MCR, and principal component analysis (PCA), while CLOM is significantly different from MCR and PCA, and MCR is significantly different from PCA. The ANN implementation demonstrated that the CLOM model had the highest accuracy (R2 of 0.95 for training and 0.92 for testing) for predicting soil erodibility, with SOC, sand, moisture, and void ratio being the most important variables. The SHAP analysis confirmed the importance of these variables for each of the four ANN models. This research provides valuable information for soil erosion management in arid regions. The identification of soil erosional probability and influential parameters will help to develop effective management strategies to prevent soil erosion and promote agricultural production. This research can be used by policymakers and stakeholders to make informed decisions to manage and prevent soil erosion.

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Dynamics and fractions of soil organic carbon in response to 35 years of afforestation in subtropical China

Yang,

Song,

Lyu

et al. 2024

Plant Soil

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Native multispecies and fast-growing forest root biomass increase C and N stocks in a reclaimed bauxite mining area

Cited by 4 publications

References 70 publications

Resilience of Aboveground Biomass of Secondary Forests Following the Abandonment of Gold Mining Activity in the Southeastern Peruvian Amazon

Resilience of Aboveground Biomass of Secondary Forests Following the Abandonment of Gold Mining Activity in the Southeastern Peruvian Amazon

An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia

Dynamics and fractions of soil organic carbon in response to 35 years of afforestation in subtropical China

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