Soil Vegetation Research Articles

Some strange substrates for macrofungi in the Pampa Biome - Brazil

Macrofungi usually are found growing on vegetal substrate, soil or dung, but sometimes they have the opportunity to explore some very unusual food. Studying mushrooms in São Gabriel municipality, southern Brazil (Pampa Biome) allow us to collect specimens in different substrata rarely or still not reported in the literature. The collections include Amanita muscaria subsp. flavivolvata growing in Eucalyptus plantation, Clitopilus argentinus growing on a wasp nest, Cheimonophyllum candidissimum growing on rock, Marasmius neosessilis on the shrub Asparagus officinalis (Angiosperm) and Panaeolina foenisecii in Capibara dung. The implications of such ecological relationship are discussed. To Clitopilus a review of the species reported to Brazil is presented.

Regional scale diversity and distribution of soil inhabiting Tetracladium

The genus Tetracladium has historically been regarded as an aquatic hyphomycete. However, sequencing of terrestrial ecosystems has shown that Tetracladium species might also be terrestrial soil and plant-inhabiting fungi. The diversity of Tetracladium species, their distribution across ecosystems, and the factors that shape community composition remain largely unknown. Using internal transcribed spacer (ITS) amplicon sequencing, we investigated the spatial distribution of Tetracladium in 970 soil samples representing the major ecosystems found across the British landscape. Species of the genus were found in 57% of the samples and across all vegetation types. The Tetracladium sequences we recovered included species common in aquatic ecosystems. However, we found five additional clades that clustered with environmental sequences previously found in terrestrial environments. The community composition of the Tetracladium OTUs was mainly related to vegetation type and soil pH. Strikingly, both taxon richness and overall abundance were highest in arable soils and showed positive relationships with soil pH. T. maxilliforme and a taxon of environmental sequences, Tetracladium group 1, was the biggest group, had the most relative abundance across ecosystems and was found in all vegetation types. Overall, this study provides insights into the community composition patterns of Tetracladium in terrestrial ecosystems and highlights the importance of vegetation characteristics in shaping Tetracladium communities.

Study of Constraints in the Management of Dry and Hilly Lands in Flores Bajawa

Dry land is a landscape where water is stagnant for a long time or all the time. The use of dry land usually occurs on land, dry land, or uncultivated land. Dry land has the potential to become agricultural land, with 45% of Indonesia's territory consisting of hills and dry land, which are ideal areas for development. However, many obstacles arise in arid, dry regions. In addition to dry land, hills, or areas above ground, they have a positive impact on the agricultural system. Bajawa is a region in the Flores plateau that has a very extensive dryland area. The purpose of writing this article is to facilitate the resolution of problems or obstacles that occur in dryland and hilly areas (upland areas) with a dry climate. The method used in this research is a literature approach. (library research). Data collection in the research was conducted by reviewing and exploring several journals, books, and documents (both printed and electronic) as well as other data sources and information deemed relevant to the research or study. To support sustainable agriculture, dryland management must be sustainable. In land management, there are several issues that often arise. This includes soil fertility decline due to erosion and leaching, very steep topography, water availability, and production issues. Able to identify obstacles and determine solutions. As an alternative, one can use vegetative or mechanical soil management methods, enhance rainwater harvesting, manage water resources comprehensively, and plant drought-resistant crops.

Assessment and mapping of water erosion by the integration of Gavrilovic's "EPM" model in the MENA region: case study of the Admer-Ezem sub-watershed, upper Oum-Rbaa basin, Morocco.

The main goal of the research is to assess soil erosion while analyzing the spatial distribution of its evolution using the EPM (erosion potential model). Situated northwest of the upper Oum-Rbaa watershed in Morocco, the Admer-Ezem watershed is part of the research area. Its climate is Mediterranean, ranging from semi-arid to subhumid bioclimate, which favors fairly scattered vegetation and poor soil. Separating the factors of complexity and interdependence in the analysis of erosion risk was made possible quickly and effectively by the Geographic Information System (GIS) integration of thematic maps of the various Gavrilovic Equation EPM factors with their databases. It also made it possible to evaluate each factor's influence and determine how much it contributed to soil loss. The priority of the various watershed regions was made possible by the use of Gavrilovic's calculations. This basin covers a total area of 197.59 km2, experiences relatively abundant rainfall (800 mm/year), and is subject to significant anthropogenic pressure. This will have an impact on the overexploitation of natural resources in general and soils in particular. Excessive use of agricultural land has led to its fragility and increased susceptibility to erosion. These natural and anthropogenic conditions have induced an intense erosive dynamic, which can be seen in its various forms (badlands, landslides, flows, concentrated active ravines…). The use of the "EPM" model for estimating soil losses approximates the severity of the erosion phenomenon. Soil loss due to water erosion according to the model used is estimated at 32.89 t/ha/year as the highest value and 0.11 t/ha/year as the lowest value. In addition, the analysis of these results made it possible, using GIS, to determine the factors that control water erosion.

Distribution, Accumulation Characteristics, and Risk Assessment of Heavy Metals in Vegetable Soils of Different Planting Years

To ascertain the influence of prolonged planting on the distribution and accumulation of heavy metals in protected vegetable soil, samples from typical vegetable planting areas in Shenyang were gathered to evaluate the distribution features of Cr, Ni, Cu, Zn, Cd, and Pb in such soil. The Hakanson index, heavy metal risk assessment standard, and secondary phase to primary ratio methods were employed to evaluate the ecological hazard of soil in the research area, while the human health risk model was utilized to evaluate the health of inhabitants. The findings indicated that：① The amount of heavy metals Zn, Cr, Cu, Ni, Pb, and Cd in the topsoil （0~20 cm） of protected vegetable land in the study region decreased successively, and the content of heavy metals in the topsoil increased with the extension of planting years, of which Cd, Zn, and Cu increased most obviously, with maximum increases of 219%, 189%, and 134%, respectively. ② Heavy metals Zn, Cr, Cu, Ni, and Pb were mainly in the residual state, whereas Cd was mainly in the weak acidic state, with a maximum proportion of 53%. ③ The potential ecological risk index of Cd in the topsoil of the study area was 95.77, showing a medium ecological risk； however, the comprehensive potential ecological risk of the six heavy metals was not high. ④ The HI values of the six heavy metals in the study area were less than 1 and the risk of non-carcinogenesis was negligible； however, the carcinogenic risk of heavy metals in some sampling sites should not be ignored.

Characterizing Soil and Bedrock Water Use of Native California Vegetation

The effective characterization of landscape water balance components—evapotranspiration, runoff, recharge, and soil storage—is critical for understanding the integrated effects of the water balance on vegetation dynamics, water availability, and associated environmental responses to climate change. An improved parameterization of these components can improve assessments of landscape stress and provide useful insights for predicting and managing vegetation responses to climate change. Hydrology models typically are not able to address water availability below the mapped soil profile, but we refined a landscape hydrology model, the Basin Characterization Model, by balancing measures of actual evapotranspiration (AET) with modeled subsurface soil water holding capacity, including bedrock storage. The purpose of this study was to characterize the effective rooting depth (the depth of soil and bedrock storage required to support AET) for 35 native vegetation types in California in order to quantify soil and bedrock water use, which ranged from 0 to 3.1 m for most vegetation types, exceeding mapped soil depths. This resulted in the quantification of bedrock water use, increasing available water 67% over that calculated by mapped soils alone. We found that mid-elevation vegetation types with lower water and energy limitations have the highest evapotranspiration rates and deepest effective rooting depth. We also evaluated the resilience to drought with this more spatially realistic characterization of water and vegetation interactions.

Response of Rhizosphere Soil Microbial Community Structure to the Natural Succession of Saline-Alkali Tolerant Vegetation on the Songnen Plain

Soil salinization and alkalization is a key factor limiting the sustainable development of global agriculture. The natural succession of vegetation in saline-alkali soil is an effective method for improving of saline-alkali soil, and microorganisms play an important role in vegetation growth. This study analysed the microbial community structure and diversity of saline-alkali tolerant vegetation rhizosphere soil in northeastern China where natural succession of saline-alkali tolerant vegetation has occurred, using high-throughput sequencing technology. We found that the community structures of bacteria and eukaryotic microorganisms were substantially different with the vegetation natural succession. The bacterial community changed from Actinobacteria-dominated to Proteobacteria-dominated communities. The eukaryotic microorganismal community generally transitioned from alga-dominated (Streptophyta and Ochrophyta) to alga-dominated (Streptophyta and Ochrophyta) and fungi-dominated (Ascomycota) to fungi-dominated (Ascomycota) communities. Based on the difference analysis of microbial community compositions and basic physicochemical properties with the natural succession of vegetation, we found that the bacterial groups that played a key role in the natural succession of the vegetation were Actinobacteria and Proteobacteria and that the eukaryotic microbial groups were Ascomycota, Nematoda, and Streptophyta. The results provide theoretical guidance for microbial inoculation to promote plant succession and improve low-yield saline-alkali soils.

Impacts of phosphate-solubilizing bacterium strain MWP-1 on vegetation growth, soil characteristics, and microbial communities in the Muli coal mining area, China

Due to the cold climate and low soil nutrient content, high-altitude mining areas are challenging to restore ecologically. Their poor nutrient content may be ameliorated by introducing specific microorganisms into the soil. This study aims to evaluate the effects of a highly efficient phosphate solubilizing bacterium MWP-1, Pseudomonas poae, on plant growth, soil nutrients in remedying the soil of the high-altitude Muli mining area in Qinghai Province, and analyze its impact on microbial communities through high-throughput sequencing soil microbial communities. The results showed that MWP-1 significantly increased the content of soil available phosphorus by >50%, soil organic matter and total nitrogen by >10%, and significantly increased the height, coverage, and aboveground biomass of vegetation by >40% in comparison with the control (p < 0.05). MWP-1 mainly affected the composition of the soil bacterial communities at the taxonomic level below the phylum. Its impact on soil fungal communities occurred at the phylum and below taxonomic levels. In addition, MWP-1 also significantly improved the diversity of soil bacterial and fungal communities (p < 0.05), and changed their functions. It also significantly altered the relative abundance of genes regulating phosphorus absorption and transport, inorganic phosphorus dissolution and organic phosphorus mineralization in the bacterial community (p < 0.05). It caused a significant increase in the relative abundance of the genes regulating nitrogen fixation and nitrification in nitrogen cycling (p < 0.05), but a significant decrease in the genes regulating phospholipase (p < 0.05). Although sequencing results indicated that Pseudomonas poae did not become the dominant species, its dissolved phosphorus elements can promote plant growth and development, enrich soil nutrient content, and affect the succession of microbial communities, enhance ecosystem stability, with an overall positive effect on soil remediation in the mining area.

Advances in the detection technology of vegetable soil borne fungi and bacteria

Soil borne diseases are one of the most serious diseases which often results the decline of vegetables quality and loss of production. Moreover, it is difficult for plants to exhibit disease symptoms in the early stages attributing to strong concealment of soil borne pathogens. Therefore, early detection of pathogens and their physiological races plays an important role in reducing the harm of pathogens associated with diseases of vegetable crops. The traditional diagnostic techniques relied on the time consuming and less accurate methods like disease symptom observation, microscopic diagnosis, and culture techniques etc. The development of molecular biology technology has brought revolutionary changes to the diagnosis of vegetable soil borne diseases, improving the accuracy and efficiency of diagnosis. This paper reviews the various molecular detection techniques for vegetable soil borne pathogens (PCR, nested-PCR, multiplex PCR, etc.) and their physiological races (host identification, DNA molecular markers, transposon detection, etc.), explains the advantages and disadvantages of each detection technique. Furthermore, the paper comprehensively introduces the application of molecular detection technology for soil borne pathogen detection in soil, plants, and seeds. Finally, we put forward important perspectives for the future development of rapid detection methods, aiming to promote rapid diagnosis of soil pathogenic microorganisms and provide guidance for the control of biological risks.

Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index

Synthetic Aperture Radar (SAR) data, known for its high spatial resolution and all-weather observation capabilities, holds immense promise in soil moisture monitoring. The Water Cloud Model (WCM) is widely applied in soil moisture inversion using SAR data. However, the optical vegetation indices employed in traditional WCM cannot synchronize with SAR data, and the polarimetric scattering information contained in current SAR vegetation indices is incomplete, consequently compromising the accuracy of SAR-based soil moisture retrieval. Therefore, this study proposes a method for soil moisture retrieval over cropland using a novel dual-polarization SAR vegetation index. The method initially combines SAR data covariance elements with backscatter information to establish a polarization scattering contrast parameter (mcp). Then, based on mcp and incorporating the degree of polarization, a polarization scattering correlation contrast parameter (Rcp) is defined. Rcp integrates the distinctive features of scattering differences and polarization states. Building on Rcp, a novel dual-polarization SAR vegetation index (DRVIs) is introduced. Ultimately, DRVIs are utilized in the WCM to achieve surface soil moisture retrieval in cropland. This research conducts experiments in four crop cover areas of the Carman test site in Canada, namely soybean, wheat, canola, and corn. Under VV and VH polarizations, the overall correlation coefficients between measured in-situ data and SSM estimates reach 0.89 and 0.84, respectively. Compared to SSM estimates based on NDVI and LAI products, SSM estimates based on DRVIs exhibit a notable improvement in accuracy, with enhancements of approximately 7.2% and 12.7%, respectively. This novel DRVIs is poised to expand the utilization of SAR data in monitoring vegetation growth and soil moisture retrieval.

Groundwater Potential Mapping Using Random Forest and Extreme Gradient Boosting Algorithms

Abstract The availability of groundwater is a crucial solution to ensure the sustainability of water resources, including providing clean water. Therefore, efforts to map groundwater potential are required to enhance the efficiency of groundwater utilization and support achieving one of the Sustainable Development Goals (SDGs), particularly clean water and sanitation. This research aims to identify the distribution of the groundwater potential in Kediri Regency using the random forest (RF) and extreme gradient boosting (XGB) algorithms. This research utilizes 13 parameters, including elevation, slope, aspect, drainage density, river density, distance from rivers, lineament density, Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), land cover, soil type, lithology, and band 3 from Sentinel-2A satellite imagery. The coordinates of groundwater wells are used as training and testing data with ratios of 80:20, 70:30, and 60:40. Through the evaluation of each model’s performance using a confusion matrix, it is revealed that the best model is the RF 70:30 ratio model with Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), Positive Predictive Value (PPV) values of 0.978, Cohen’s Kappa (CK) and Matthew’s Correlation Coefficient (MCC) of 0.956, and Area Under Curve (AUC) of 0.994. In this model, the elevation has the highest influence on the model, with a significance level equal to 100.

High-risk driving factors of rain-induced flooding hazard events on the Loess Plateau and its ecological subregions

Rain-induced flooding hazards are prevalent on the Loess Plateau (LP). Descriptive statistics, kernel density estimation, and geographical detector methods were used to explore the spatial and temporal distribution, driving factors, and their high-risk intervals of rain-induced flooding hazard events (RFHEs) on the LP and whether they differ across the entire LP and its ecological subregions. The findings showed that 91 RFHEs occurred mainly in the south-central LP during 2004–2020. The daily rainfall, surface relief amplitude (SRA), elevation, normalized difference vegetation index (NDVI), soil texture, and gross domestic product (GDP) were identified as the driving factors of RFHEs on the LP. However, the driving factors of RFHEs in the Sandy and Agricultural Irrigation Regions (Subregion C), and Earth-rocky Mountainous Region and River Valley Plain Region (Subregion D) all had an added population factor compared to the entire LP, but they lacked NDVI and SRA factors, respectively. The driving factors for the Loess Plateau Gully Region (Subregion A) lacked SRA and soil texture factors. The Loess Hilly and Gully Region (Subregion B) lacked NDVI, soil texture, and population factors. There were also differences between high-risk intervals on the LP and its subregions. The high-risk daily rainfall for the entire LP was 64.5 mm, while it was 64.5, 82.3, 14.7, and 50.0 mm for subregions A, B, C, and D, respectively. Therefore, adopting uniform standards on the LP may over-estimate or under-estimate RFHE occurrence in ecological subregions. These findings contribute to guiding decision-makers involved in ecosystem management and hazard prevention.

Improved estimation of non-photosynthetic vegetation cover using a novel multispectral slope difference index with soil information, Sentinel-1 data, and machine learning

Non-photosynthetic vegetation (NPV) plays a crucial role in vegetation–soil ecosystems by influencing the dynamic uptake of carbon, water, and nutrients. The accurate estimation of the fractional cover of NPV (FNPV) is vital for managing natural resources, modeling carbon dynamics, and monitoring vegetation systems. NPV indices (NPVIs) are widely utilized for estimating FNPV over large areas. However, distinguishing NPV from bare soil (BS) remains challenging owing to subtle differences in their multispectral reflectance and the interference of the soil background. To address this, we propose a novel NPV spectral slope difference index (NSSDI) based on the distinct spectral curve shapes of NPV and BS. The NSSDI incorporates three spectral slopes: two between the visible and near-infrared bands and one between the shortwave infrared 1 (SWIR1) and SWIR2 bands from Sentinel-2 (S2) and Landsat-8 (L8). Additionally, we investigate the integration of soil information and radar data to improve the FNPV estimation using three machine learning algorithms. Validation against in situ measurements reveals that the NSSDI from S2 is more sensitive to FNPV than the recently published NPV–soil separation index (NSSI), whereas the NSSDI from L8 outperforms traditional NPVIs. Incorporating soil properties such as soil organic matter and soil moisture improves the FNPV estimation accuracy compared with using S2 or L8 NPVIs alone. The combination of Sentinel-1 (S1) synthetic aperture radar (SAR) data with either optical satellite also enhances the retrieval accuracy. The Gaussian process regression (GPR) model, which integrates S2 NSSDI, soil data, and S1 SAR data, achieves an R2 of 0.78 and the lowest RMSE of 0.109. Similarly, the GPR model based L8 NPVIs, soil data, and SAR data attains an R2 of 0.71 and an RMSE of 0.128. Both GPR models outperform the random forest and XGBoost models. Monthly FNPV estimates from April to August 2019 in the Northern Territory, Australia demonstrates strong spatial consistency across both satellites using the GPR models in the Google Earth Engine. These results suggest that combining NPVIs with soil and SAR data can facilitate accurate large-scale FNPV estimations.

Strength and stiffness anisotropy of 3D-printed coarse root analogues for small-scale physical modelling

Plant root–soil mechanical interaction in the application of soil bioengineering such as tree and slope stability has been investigated by way of centrifuge modelling, utilising root analogues to replicate vegetated soils. Three-dimensional (3D) printing can be used to model complex root architecture, but the nature of the layer-upon-layer printing process may lead to printed parts of differing tensile behaviour depending on orientation and, consequently, unrealistic simulation of root mechanical reinforcement. This study aimed to assess the strength and stiffness anisotropy of straight root analogues built at varying orientations by way of three different 3D printing methods and compare the measured properties with those of real roots. The tensile strength ratios between horizontal- and vertical-printed samples were up to 3·90, 1·27 and 2·57 for fused deposition modelling (FDM), liquid-crystal display and PolyJet methods, respectively. Stiffness anisotropy was also more significant in FDM. The relatively higher anisotropy in FDM-printed samples could overestimate the strength and stiffness of most roots in a hypothetical heart-shaped root system, depending on the diameter distribution. Such a physical model may be improved by using 45° inclined PolyJet-printed rods.

Grassland irrigation and grazing prohibition have significantly affected vegetation and microbial diversity by changing soil temperature and moisture, evidences from a 6 years experiment of typical temperate grassland

Grasslands are characterized by high primary productivity and offer a diverse array of ecosystem services that contribute to human well-being. The dynamic balance between vegetation-soil in grassland ecosystems is being affected by anthropogenic activities and climate change, in which soil microbial communities play a critical regulating role. However, how microbial biodiversity interacts with vegetation-soil and responds to environmental change remains unclear. We conducted a six-year field experiment in the grasslands of Inner Mongolia to study the effects of grazing and altered precipitation on major vegetation types (or species), soil properties, and microbial community composition. The results showed that increased precipitation influenced positive associations within microbial communities, which helped to increase vegetation diversity. The biomass of Stipa.sareptana increased by 0.054 % under reduced precipitation, while it significantly increased by 2.07 % under the combination of grazing ban and reduced precipitation. Grazing prohibition had a significant negative effect on bacterial diversity and Shannon's index, but a significant positive effect on fungal diversity and abundance. Increasing precipitation had no significant effect on bacterial diversity under grazing conditions, while decreasing precipitation significantly reduced the Shannon index of bacteria. Fungal communities were very sensitive to changes in precipitation, and both increasing and decreasing precipitation significantly affected the structure of fungal communities. In summary, our results highlight how grassland irrigation and moderate grazing can be employed as a management strategy to promote plant diversity and thereby improve ecosystem functioning and resilience.

Effects of a Vegetable Eel Co-Culture System on the Soil Ammonia-Oxidizing Microbial Community

(1) Background: A vegetable eel co-culture system is an economically efficient way of agricultural cultivation, which can have an impact on the soil microbial environment and play a pivotal role in the soil nutrient cycle, but there is little research on its impact on soil ammonia-oxidizing microorganisms. (2) Methods: NovaSeq platform sequencing was employed to investigate the richness, structure, and diversity of soil ammonia-oxidizing microbial communities, exploring the effects of a vegetable eel co-culture system on soil nitrogen cycling. Four different planting treatments were set up: unfertilized without vegetable eel (CCK), fertilized without vegetable eel (CRT), unfertilized with vegetable eel (ICK), and fertilized with vegetable eel (IRT). (3) Results: A vegetable eel co-culture system significantly increased soil pH and decreased bulk density under fertilization conditions. The soil nitrification potential rate was enhanced by a vegetable eel co-culture system to an average of 26.3%. A vegetable eel co-culture system significantly altered the community structure of all ammonia-oxidizing microorganisms, with a significant increase in the richness and diversity of ammonia-oxidizing bacteria (AOB) and comammox clade-A, while fertilization significantly increased the diversity of all ammonia-oxidizing microbial communities. Structural equation modeling (SEM) analysis showed that the main environmental factors affecting the structure of the ammonia-oxidizing microbial community were nitrate and total nitrogen. The number of amoA genes in AOB and comammox clade-B was significantly positively correlated with the soil potential N nitrification rate (PNR), which played a leading role in the nitrification of alkaline vegetable soil. The network analysis revealed that a vegetable eel co-culture system improved the modularity of AOB and comammox clade-B by 13.14% and 5.66%. (4) Conclusions: This study showed that the vegetable eel co-culture system stimulated the evolution of ammonia-oxidizing microbial communities by changing the physicochemical properties, which in turn promoted the soil nitrification reaction.

Hyperspectral Remote Sensing Combined with Ground Vegetation Surveys for the Study of the Age of Rodent Mounds

Background: Rodents severely damage the ecological environment of grasslands, and rodent mounds of different ages require distinct management strategies. Understanding the age of these mounds aids in formulating targeted restoration measures, which can enhance grassland productivity and biodiversity. Current surveys of rodent mounds rely on ground exposure and mound height to determine their age, which is time-consuming and labor-intensive. Remote sensing methods can quickly and easily identify the distribution of rodent mounds. Existing remote sensing images use ground exposure and mound height for identification but do not distinguish between mounds of different ages, such as one-year-old and two-year-old mounds. According to the existing literature, rodent mounds of different ages exhibit significant differences in vegetation structure, soil background, and plant diversity. Utilizing a combination of vegetation indices and hyperspectral data to determine the age of rodent mounds aims to provide a better method for extracting rodent hazard information. This experiment investigates and analyzes the age, distribution, and vegetation characteristics of rodent mounds, including total coverage, height, biomass, and diversity indices such as Patrick, Shannon–Wiener, and Pielou. Spectral data of rodent mounds of different ages were collected using an Analytical Spectral Devices field spectrometer. Correlation analysis was conducted between vegetation characteristics and spectral vegetation indices to select key indices, including NDVI670, NDVI705, EVI, TCARI, Ant, and SR. Multiple stepwise regression and Random Forest (RF) inversion models were established using vegetation indices, and the most suitable model was selected through comparison. Random Forest modeling was conducted to classify plateau zokor rat mounds of different ages, using both vegetation characteristic indicators and vegetation indices for comparison. The rodent mound classification models established using vegetation characteristic indicators and vegetation indices through Random Forest could distinguish rodent mounds of different ages, with out-of-bag error rates of 36.96% and 21.74%, respectively. The model using vegetation indices performed better. Conclusions: (1) Rodent mounds play a crucial ecological role in alpine meadow ecosystems by enhancing plant diversity, biomass, and the stability and vitality of the ecosystem. (2) The vegetation indices SR and TCARI are the most influential in classifying rodent mounds. (3) Incorporating vegetation indices into Random Forest modeling facilitates a precise and robust remote sensing interpretation of rodent mound ages, which is instrumental for devising targeted restoration strategies.

Soil Ecosystem Services in Urban Parks as a Basis for Better Urban Planning: The Case of Mexico City

Until now, the contribution of soils in urban spaces has been underestimated and, as a result, the services they provide have been severely undervalued in urban planning. This article aims to address this issue through a study of Chapultepec Park in Mexico City. We provide two methodological proposals for the analysis of soil ecosystem services characterized by the interaction of natural and anthropic processes: the morpho-pedological landscapes (MpL), in which we quantify soil carbon sequestration (SOC Mg ha−1) and the comparison of two methods for the analysis of hydraulic conductivity: Pedotransfer functions (PTF) and direct measurement with a double ring infiltrometer. Among the 12 MpL, we found the highest SOC content in slopes with mixed tree vegetation and Phaeozem soils. However, SOC retention decreased by 40%–50% due to sealing surfaces. For infiltration measures, despite the diversity of soils and vegetation, direct measurements values are highly homogeneous, while values calculated using PTFs better reflect the morphopedological landscape heterogeneity. In all the MpLs, the hydraulic conductivity was higher than the maximum rainfall intensity report, indicating that the soils in Chapultepec Park, despite the differences in soils and intensity of use, provide the ecosystem service of infiltration. These results would allow the establishment of a baseline for monitoring these services and provide information to decision makers and urban planners seeking to reduce the construction of gray infrastructure that seals soils and reduces their capacity to provide these ecosystem services.

Research on Wildfire and Soil Water: A Bibliometric Analysis from 1990 to 2023

In the context of climate change, wildfires occur more frequently and significantly impact the vegetation–soil–water continuum. Soil water is a critical factor for understanding wildfire occurrence and predicting wildfire hazards. However, there is a lack of specific bibliometric analysis of the research on the mechanisms by which soil water influences wildfire occurrence. Therefore, this study conducted a bibliometric analysis of wildfire and soil water, aiming to understand their relationship, research characteristics, and future development trends. We used the Bibliometrix software package in R 4.4.0, which provides different methods for analyzing bibliometric data. A total of 1585 publications were analyzed from 1990 to 2023. The results of the study showed that the number of publications showed an overall growth trend during the period, with an average annual increase rate of 4.4%. The average annual citations per paper exhibited a pattern of rapid increase, followed by slow growth, and then rapid decrease. Ten highly productive authors in the field contributed 12.2% of the total publications during this period. Over the past 30 years, the University of Aveiro has consistently ranked first in terms of paper quantity. Most of the top ten productive institutions are in the United States, Australia, and several European countries. Fifty-eight countries engage in research related to wildfires and soil water, with close collaboration observed between the United States, Canada, and Spain. The four most frequently used keywords are “wildfire”, “fire”, “water repellency”, and “runoff” (with a total frequency of 1385). Water properties relevant to soil characteristics in the word cloud primarily include hydrophobicity, runoff, erosion, and infiltration. Erosion, wildfires, and runoff are crucial in the field but have yet to receive substantial development. The correlation of post-wildfire soil water properties with infiltration, runoff, and erosion processes is most likely to be addressed in future research. The findings will help researchers assess the post-wildfire disaster chain and its impact on the ecological environment, with clear trends, gaps, and research directions in the areas.

Effects of Land Use Land Cover Change on Gully Development in Lugari Sub Catchment, Kakamega County in Kenya

Effects of Land Use Land Cover Change on Gully Development in Lugari Sub Catchment, Kakamega County in Kenya