PeerJ Preprints: Soil Sciencehttps://peerj.com/preprints/index.atom?journal=peerj&subject=2600Soil Science articles published in PeerJ PreprintsA simple method for the selective quantification of polyethylene, polypropylene, and polystyrene plastic debris in soil by pyrolysis-gas chromatography/mass spectrometryhttps://peerj.com/preprints/279892019-12-052019-12-05Zacharias SteinmetzAaron KintziKatherine MuñozGabriele E. Schaumann
The lack of adequate analytical methods for the quantification of plastic debris in soil challenges a better understanding of their occurrence and fate in the terrestrial environment. With this proof-of-principle study, we developed a simple and fast method for the selective quantification of the three most environmentally relevant polymers polyethylene (PE), polypropylene (PP), and polystyrene (PS) in soil using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). In order to facilitate the preparation of calibration series and to better account for the heterogeneity of soil matrix, polymers were dissolved in 1,2,4-trichlorobenzene (TCB) at 120 °C. Thereby, liquid sample aliquots from up to 4 g of solid sample became amenable to pyrolysis without further preparation. To evaluate the performance of this approach, three reference soils with 1.73–5.16 % organic carbon (Corg) were spiked at 50 and 250 μg g−1 of each polymer and extracted with TCB. A prior cleanup step with methanol, flocculation with KAl(SO4)2, and Fenton digestion were tested for their suitability to reduce potentially interfering Corg. Calibration curves responded linearly (adj. R2 > 0.996) with instrumental detection limits of 1–86 ng corresponding to method detection limits of 1–86 μg g−1. The measurement repeatability was 3.2–7.2 % relative standard deviation. Recoveries of 70–128 % were achieved for plastic contents of 250 μg g−1 extracted with TCB without prior cleanup from soils with less than 2.5 % Corg. A higher Corg particularly interfered with the quantification of PE. The addition of non-target polymers (polyethylene terephthalate, polyvinyl chloride, poly(methyl methacrylate), and tire wear particles) did not interfere with the quantification of the analytes highlighting the selectivity of the method. Further research is needed to determine low plastic contents in soils exceeding 2.5 % Corg. With 1–3 h processing time per sample, our method has the potential for routine analyses and screening studies of agricultural systems to be complemented with microspectroscopic techniques for additional information on particle shapes and sizes.
The lack of adequate analytical methods for the quantification of plastic debris in soil challenges a better understanding of their occurrence and fate in the terrestrial environment. With this proof-of-principle study, we developed a simple and fast method for the selective quantification of the three most environmentally relevant polymers polyethylene (PE), polypropylene (PP), and polystyrene (PS) in soil using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). In order to facilitate the preparation of calibration series and to better account for the heterogeneity of soil matrix, polymers were dissolved in 1,2,4-trichlorobenzene (TCB) at 120 °C. Thereby, liquid sample aliquots from up to 4 g of solid sample became amenable to pyrolysis without further preparation. To evaluate the performance of this approach, three reference soils with 1.73–5.16 % organic carbon (Corg) were spiked at 50 and 250 μg g−1 of each polymer and extracted with TCB. A prior cleanup step with methanol, flocculation with KAl(SO4)2, and Fenton digestion were tested for their suitability to reduce potentially interfering Corg. Calibration curves responded linearly (adj. R2 > 0.996) with instrumental detection limits of 1–86 ng corresponding to method detection limits of 1–86 μg g−1. The measurement repeatability was 3.2–7.2 % relative standard deviation. Recoveries of 70–128 % were achieved for plastic contents of 250 μg g−1 extracted with TCB without prior cleanup from soils with less than 2.5 % Corg. A higher Corg particularly interfered with the quantification of PE. The addition of non-target polymers (polyethylene terephthalate, polyvinyl chloride, poly(methyl methacrylate), and tire wear particles) did not interfere with the quantification of the analytes highlighting the selectivity of the method. Further research is needed to determine low plastic contents in soils exceeding 2.5 % Corg. With 1–3 h processing time per sample, our method has the potential for routine analyses and screening studies of agricultural systems to be complemented with microspectroscopic techniques for additional information on particle shapes and sizes.Ethylene induced nitrile and VOC synthesis by soil microbes; Improved root elongation & reduced risk of fungal infection in plants.https://peerj.com/preprints/5432019-09-272019-09-27Guenevere PerryDiane Perry
The scope of the project was to develop a method to induce soil microbes to inhibit fungal infection and improve root elongation. The study was randomized. Gladiolus bulbs selected for the study were visibly inspected to for viability and visible signs of infection. Two trials were conducted from Aug. 5th – Sept. 5th 2014 with 4 replicates per condition over a 7-d period in damp outdoor conditions in late summer. A mixed culture of plant growth promoting rhizobacteria (PGPR) were collected from soil surrounding the roots of young fruit bearing trees. Microbes were mixed with minimal media (no-carbon source), and cultured with an ethylene and used as potting soil. Bulbs planted in ethylene induced soil displayed 0% visible fungal growth, while 38% of bulbs grown in control conditions displayed some form of fungal growth and/or infection. Ethylene induced soil increased root length by 225% in bulbs in 7-d period. GC Mass Spectrophotometry data suggest ethylene may induce soil microbes to synthesize several VOCs including (ethanol, 3-methyl-1-butanol, pentanol) and esters (ethyl acetate), that may have synergistic benefits to lower the risk of fungal infection by soil mold, while nitrile compounds improve root elongation. The findings are preliminary, additional studies are required to understand the mechanism.
The scope of the project was to develop a method to induce soil microbes to inhibit fungal infection and improve root elongation. The study was randomized. Gladiolus bulbs selected for the study were visibly inspected to for viability and visible signs of infection. Two trials were conducted from Aug. 5th – Sept. 5th 2014 with 4 replicates per condition over a 7-d period in damp outdoor conditions in late summer. A mixed culture of plant growth promoting rhizobacteria (PGPR) were collected from soil surrounding the roots of young fruit bearing trees. Microbes were mixed with minimal media (no-carbon source), and cultured with an ethylene and used as potting soil. Bulbs planted in ethylene induced soil displayed 0% visible fungal growth, while 38% of bulbs grown in control conditions displayed some form of fungal growth and/or infection. Ethylene induced soil increased root length by 225% in bulbs in 7-d period. GC Mass Spectrophotometry data suggest ethylene may induce soil microbes to synthesize several VOCs including (ethanol, 3-methyl-1-butanol, pentanol) and esters (ethyl acetate), that may have synergistic benefits to lower the risk of fungal infection by soil mold, while nitrile compounds improve root elongation. The findings are preliminary, additional studies are required to understand the mechanism.Revegetation pattern affecting accumulation of organic carbon and total nitrogen in reclaimed mine soilshttps://peerj.com/preprints/279362019-09-052019-09-05Ping P ZhangYan L ZhangJun C JiaYong X CuiXia WangXing C ZhangYun Q Wang
Selecting optimal revegetation patterns, i.e., patterns that are more effective for soil organic carbon (SOC) and total nitrogen (TN) accumulation is particularly important for mine land reclamation. However, there have been few evaluations of the effects of different revegetation patterns on the SOC and TN in reclaimed mine soils on the Loess Plateau, China. In this study, the SOC and TN stocks were investigated at reclaimed mine sites (RMSs), including artificially revegetated sites (ARSs) (arbors [Ar], bushes [Bu], arbor-bush mixtures [AB], and grasslands [Gr]) and a natural recovery site (NRS), as well as at undisturbed native sites (UNSs). Overall, the SOC and TN stocks in the RMSs were lower than those in the UNSs over 10–13 years after reclamation. Except for those in Ar, the SOC and TN stocks in the ARSs were significantly larger than those in the NRS. Compared with those in the NRS, the total SOC stocks in the 100 cm soil interval increased by 51.4%, 59.9%, and 109.9% for Bu, AB, and Gr, respectively, and the TN stocks increased by 33.1%, 35.1%, and 57.9%. The SOC stocks in the 0 – 100 cm soil interval decreased in the order of Gr (3.78 kg m –2) > AB (2.88 kg m–2) ≥ Bu (2.72 kg m–2), and the TN stocks exhibited a similar trend. These results suggest that grasslands were more favorable than woodlands for SOC and TN accumulation in this arid area, especially in Ar. Thus, in terms of the accumulation of SOC and TN, grassland planting is recommended as a revegetation pattern for areas with reclaimed mine soils.
Selecting optimal revegetation patterns, i.e., patterns that are more effective for soil organic carbon (SOC) and total nitrogen (TN) accumulation is particularly important for mine land reclamation. However, there have been few evaluations of the effects of different revegetation patterns on the SOC and TN in reclaimed mine soils on the Loess Plateau, China. In this study, the SOC and TN stocks were investigated at reclaimed mine sites (RMSs), including artificially revegetated sites (ARSs) (arbors [Ar], bushes [Bu], arbor-bush mixtures [AB], and grasslands [Gr]) and a natural recovery site (NRS), as well as at undisturbed native sites (UNSs).Overall, the SOC and TN stocks in the RMSs were lower than those in the UNSs over 10–13 years after reclamation. Except for those in Ar, the SOC and TN stocks in the ARSs were significantly larger than those in the NRS. Compared with those in the NRS, the total SOC stocks in the 100 cm soil interval increased by 51.4%, 59.9%, and 109.9% for Bu, AB, and Gr, respectively, and the TN stocks increased by 33.1%, 35.1%, and 57.9%. The SOC stocks in the 0 – 100 cm soil interval decreased in the order of Gr (3.78 kg m –2) > AB (2.88 kg m–2) ≥ Bu (2.72 kg m–2), and the TN stocks exhibited a similar trend. These results suggest that grasslands were more favorable than woodlands for SOC and TN accumulation in this arid area, especially in Ar. Thus, in terms of the accumulation of SOC and TN, grassland planting is recommended as a revegetation pattern for areas with reclaimed mine soils.Four newly documented species of earthworms in a coastal mangrove ecosystem of Guyana, S.A.https://peerj.com/preprints/278972019-08-132019-08-13Reshma Persaud
Mangrove ecosystems are harsh environments due to their high levels of salinity and constant disturbance from the shifts in tides and actions of waves. An investigative study was conducted to establish the presence or absence of earthworms in this environment and if present, to determine the population dynamic exhibited. Sampling was done along a 120m horizontal transect which yielded 4 new species. Of the 4 species Pontodrilus litoralis showed a high affinity for this high salinity environment which allowed it to access resources unhindered, establishing dominance. Drawida barwelli’s population, however, was suppressed by the presence of Amynthas sp. and Eukerria saltensis, the latter of which has a higher sensitivity to high salinity.The presence of high levels of salinity and sulphur, along with the disturbance of waves, are responsible for the low ecological diversity in this ecosystem. However, the presence of these organisms is still astounding given the intensity of significant soil chemical parameters.
Mangrove ecosystems are harsh environments due to their high levels of salinity and constant disturbance from the shifts in tides and actions of waves. An investigative study was conducted to establish the presence or absence of earthworms in this environment and if present, to determine the population dynamic exhibited. Sampling was done along a 120m horizontal transect which yielded 4 new species. Of the 4 species Pontodrilus litoralis showed a high affinity for this high salinity environment which allowed it to access resources unhindered, establishing dominance. Drawida barwelli’s population, however, was suppressed by the presence of Amynthas sp. and Eukerria saltensis, the latter of which has a higher sensitivity to high salinity.The presence of high levels of salinity and sulphur, along with the disturbance of waves, are responsible for the low ecological diversity in this ecosystem. However, the presence of these organisms is still astounding given the intensity of significant soil chemical parameters.The effect of reverse transcription enzymes and conditions on high throughput amplicon sequencing of the 16S rRNAhttps://peerj.com/preprints/277802019-07-152019-07-15Adam ŠťovíčekSmadar Cohen-ChalamishOsnat Gillor
It is assumed that the sequencing of ribosomes better reflects the active microbial community than the sequencing of the ribosomal RNA encoding genes. Yet, many studies exploring microbial communities in various environments, ranging from the human gut to deep oceans, questioned the validity of this paradigm due to the discrepancies between the DNA and RNA based communities. Here we focus on an often neglected key step in the analysis, the reverse transcription (RT) reaction. Previous studies showed that RT may introduce biases when expressed genes and ribosmal rRNA are quantified, yet its effect on microbial diversity and community composition was never tested. High throughput sequencing of ribosomal RNA is a valuable tool to understand microbial communities as it better describes the active population than DNA analysis. However, the necessary step of RT may introduce biases that have so far been poorly described. In this manuscript, we compare three RT enzymes, commonly used in soil microbiology, in two temperature modes to determine a potential source of bias due to non-standardized RT conditions. In our comparisons, we have observed up to 6 fold differences in bacterial class abundance. A temperature induced bias can be partially explained by G-C content of the affected bacterial groups, thus pointing towards a need for higher reaction temperatures. However, another source of bias was due to enzyme processivity differences. This bias is potentially hard to overcome and thus mitigating it might require the use of one enzyme for the sake of cross-study comparison.
It is assumed that the sequencing of ribosomes better reflects the active microbial community than the sequencing of the ribosomal RNA encoding genes. Yet, many studies exploring microbial communities in various environments, ranging from the human gut to deep oceans, questioned the validity of this paradigm due to the discrepancies between the DNA and RNA based communities. Here we focus on an often neglected key step in the analysis, the reverse transcription (RT) reaction. Previous studies showed that RT may introduce biases when expressed genes and ribosmal rRNA are quantified, yet its effect on microbial diversity and community composition was never tested. High throughput sequencing of ribosomal RNA is a valuable tool to understand microbial communities as it better describes the active population than DNA analysis. However, the necessary step of RT may introduce biases that have so far been poorly described. In this manuscript, we compare three RT enzymes, commonly used in soil microbiology, in two temperature modes to determine a potential source of bias due to non-standardized RT conditions. In our comparisons, we have observed up to 6 fold differences in bacterial class abundance. A temperature induced bias can be partially explained by G-C content of the affected bacterial groups, thus pointing towards a need for higher reaction temperatures. However, another source of bias was due to enzyme processivity differences. This bias is potentially hard to overcome and thus mitigating it might require the use of one enzyme for the sake of cross-study comparison.Earthworm population ecology; Guyana, S Ahttps://peerj.com/preprints/278182019-06-242019-06-24Reshma Persaud
Earthworms are regarded as the bio-indicators of soil quality and are perhaps the most significant regulators of soil structure and organic matter content in a variety of terrestrial soil ecosystems, paving the way for sustainable green agriculture and land rehabilitation. Due to the steady increase in industrialization and shifts in global climate, their population is now more susceptible to change/decline as a result of the strains placed on soil ecosystems by agriculture, mining and deforestation. This research aimed to and successfully established the composition of earthworm populations present in Guyana while exploring their relationship with the biogeographical regions and pedobiological components of their respective ecosystem. Earthworms and soil samples were collected from 15 sites per natural region after which they were taxonomically identified following methodological dissections which yielded 68 distinct species. Of the four natural regions, the earthworm population of Highland Region was found to be the most diverse, rich, even and dense. Earthworm abundance, epigeic abundance, endogeic abundance, anecic abundance and species richness among the four natural regions of Guyana, were all of statistical significant difference, likewise, earthworm abundance in the various climate and soil types along with disturbance were of statistical significant difference. It was found that epigeic earthworms were significantly affected by phosphorus (0.01), moisture (0.01) and calcium (0.02) while anecic earthworms were significantly affected by magnesium (0.04), and the degree at which these affect the various ecotype is different among natural regions. This study has proven with conviction that earthworm population structure varies depending on the biogeographical and pedobiological factors present within any respective terrestrial ecosystem.
Earthworms are regarded as the bio-indicators of soil quality and are perhaps the most significant regulators of soil structure and organic matter content in a variety of terrestrial soil ecosystems, paving the way for sustainable green agriculture and land rehabilitation. Due to the steady increase in industrialization and shifts in global climate, their population is now more susceptible to change/decline as a result of the strains placed on soil ecosystems by agriculture, mining and deforestation. This research aimed to and successfully established the composition of earthworm populations present in Guyana while exploring their relationship with the biogeographical regions and pedobiological components of their respective ecosystem. Earthworms and soil samples were collected from 15 sites per natural region after which they were taxonomically identified following methodological dissections which yielded 68 distinct species. Of the four natural regions, the earthworm population of Highland Region was found to be the most diverse, rich, even and dense. Earthworm abundance, epigeic abundance, endogeic abundance, anecic abundance and species richness among the four natural regions of Guyana, were all of statistical significant difference, likewise, earthworm abundance in the various climate and soil types along with disturbance were of statistical significant difference. It was found that epigeic earthworms were significantly affected by phosphorus (0.01), moisture (0.01) and calcium (0.02) while anecic earthworms were significantly affected by magnesium (0.04), and the degree at which these affect the various ecotype is different among natural regions. This study has proven with conviction that earthworm population structure varies depending on the biogeographical and pedobiological factors present within any respective terrestrial ecosystem.Towards optimized viral metagenomes for double-stranded and single-stranded DNA viruses from challenging soilshttps://peerj.com/preprints/276402019-04-062019-04-06Gareth TrublSimon RouxNatalie SolonenkoYueh-Fen LiBenjamin BolducJosué Rodríguez-RamosEmiley A. Eloe-FadroshVirginia I. RichMatthew B. Sullivan
Soils impact global carbon cycling and their resident microbes are critical to their biogeochemical processing and ecosystem outputs. Based on studies in marine systems, viruses infecting soil microbes likely modulate host activities via mortality, horizontal gene transfer, and metabolic control. However, their roles remain largely unexplored due to technical challenges with separating, isolating, and extracting DNA from viruses in soils. Some of these challenges have been overcome by using whole genome amplification methods and while these have allowed insights into the identities of soil viruses and their genomes, their inherit biases have prevented meaningful ecological interpretations. Here we experimentally optimized steps for generating quantitatively-amplified viral metagenomes to better capture both ssDNA and dsDNA viruses across three distinct soil habitats along a permafrost thaw gradient. First, we assessed differing DNA extraction methods (PowerSoil, Wizard mini columns, and cetyl trimethylammonium bromide) for quantity and quality of viral DNA. This established PowerSoil as best for yield and quality of DNA from our samples, though ~1/3 of the viral populations captured by each extraction kit were unique, suggesting appreciable differential biases among DNA extraction kits. Second, we evaluated the impact of purifying viral particles after resuspension (by cesium chloride gradients; CsCl) and of viral lysis method (heat vs bead-beating) on the resultant viromes. DNA yields after CsCl particle-purification were largely non-detectable, while unpurified samples yielded 1–2-fold more DNA after lysis by heat than by bead-beating. Virome quality was assessed by the number and size of metagenome-assembled viral contigs, which showed no increase after CsCl-purification, but did from heat lysis relative to bead-beating. We also evaluated sample preparation protocols for ssDNA virus recovery. In both CsCl-purified and non-purified samples, ssDNA viruses were successfully recovered by using the Accel-NGS 1S Plus Library Kit. While ssDNA viruses were identified in all three soil types, none were identified in the samples that used bead-beating, suggesting this lysis method may impact recovery. Further, 13 ssDNA vOTUs were identified compared to 582 dsDNA vOTUs, and the ssDNA vOTUs only accounted for ~4% of the assembled reads, implying dsDNA viruses were dominant in these samples. This optimized approach was combined with the previously published viral resuspension protocol into a sample-to-virome protocol for soils now available at protocols.io, where community feedback creates ‘living’ protocols. This collective approach will be particularly valuable given the high physicochemical variability of soils, which will may require considerable soil type-specific optimization. This optimized protocol provides a starting place for developing quantitatively-amplified viromic datasets and will help enable viral ecogenomic studies on organic-rich soils.
Soils impact global carbon cycling and their resident microbes are critical to their biogeochemical processing and ecosystem outputs. Based on studies in marine systems, viruses infecting soil microbes likely modulate host activities via mortality, horizontal gene transfer, and metabolic control. However, their roles remain largely unexplored due to technical challenges with separating, isolating, and extracting DNA from viruses in soils. Some of these challenges have been overcome by using whole genome amplification methods and while these have allowed insights into the identities of soil viruses and their genomes, their inherit biases have prevented meaningful ecological interpretations. Here we experimentally optimized steps for generating quantitatively-amplified viral metagenomes to better capture both ssDNA and dsDNA viruses across three distinct soil habitats along a permafrost thaw gradient. First, we assessed differing DNA extraction methods (PowerSoil, Wizard mini columns, and cetyl trimethylammonium bromide) for quantity and quality of viral DNA. This established PowerSoil as best for yield and quality of DNA from our samples, though ~1/3 of the viral populations captured by each extraction kit were unique, suggesting appreciable differential biases among DNA extraction kits. Second, we evaluated the impact of purifying viral particles after resuspension (by cesium chloride gradients; CsCl) and of viral lysis method (heat vs bead-beating) on the resultant viromes. DNA yields after CsCl particle-purification were largely non-detectable, while unpurified samples yielded 1–2-fold more DNA after lysis by heat than by bead-beating. Virome quality was assessed by the number and size of metagenome-assembled viral contigs, which showed no increase after CsCl-purification, but did from heat lysis relative to bead-beating. We also evaluated sample preparation protocols for ssDNA virus recovery. In both CsCl-purified and non-purified samples, ssDNA viruses were successfully recovered by using the Accel-NGS 1S Plus Library Kit. While ssDNA viruses were identified in all three soil types, none were identified in the samples that used bead-beating, suggesting this lysis method may impact recovery. Further, 13 ssDNA vOTUs were identified compared to 582 dsDNA vOTUs, and the ssDNA vOTUs only accounted for ~4% of the assembled reads, implying dsDNA viruses were dominant in these samples. This optimized approach was combined with the previously published viral resuspension protocol into a sample-to-virome protocol for soils now available at protocols.io, where community feedback creates ‘living’ protocols. This collective approach will be particularly valuable given the high physicochemical variability of soils, which will may require considerable soil type-specific optimization. This optimized protocol provides a starting place for developing quantitatively-amplified viromic datasets and will help enable viral ecogenomic studies on organic-rich soils.Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoringhttps://peerj.com/preprints/276302019-04-032019-04-03Xiangyu GeJingzhe WangJianli DingXiaoyi CaoZipeng ZhangJie LiuXiaohang Li
Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the spaceborne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland ( 2.5 ×104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477 and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.
Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the spaceborne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland ( 2.5 ×104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477 and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.Variation in the nitrous oxide reductase gene (nosZ)-denitrifying bacterial community in different primary succession stages in the Hailuogou Glacier retreat area, Chinahttps://peerj.com/preprints/274812019-01-122019-01-12Yan BaiXiying HuangXiangrui ZhouQuanju XiangKe ZhaoXiumei YuQiang ChenHao JiangNyima TashiXue GaoYunfu Gu
Background: The Hailuogou Glacier in the Gongga Mountain region (SW China), on the southeastern edge of the Tibetan Plateau, is well known for its low-elevation modern glaciers. Since the end of the Little Ice Age (LIA), the Hailuogou Glacier has retreated continuously due to global warming, primary vegetation succession and soil chronosequence have developed in this retreat area. The retreated area of Hailuogou Glacier has not been strongly disturbed by human activities, thus it is an ideal models for exploring the biological colonization of nitrogen in the primary successional stages of ecosystem. The nosZ gene encodes the catalytic center of nitrous oxide reductase and is an ideal molecular marker in studying the variation in the denitrifying bacterial community.
Methods: Soil properties as well as abundance and composition of the denitrifying bacterial community were determined via chemical analysis, quantitative polymerase chain reaction (qPCR), and terminal restriction fragment length polymorphism (T-RFLP), respectively. The relationships between the nosZ denitrifying bacterial community and soil properties were determined using redundancy analysis (RDA). Soil properties, potential denitrify activity (PDA), and the nitrous oxide reductase gene (nosZ)-denitrifying bacterial communities significantly differed among successional stages.
Results: Soil properties, potential denitrify activity (PDA), and the nitrous oxide reductase gene (nosZ)-denitrifying bacterial communities significantly differed among successional stages. Soil pH in the topsoil decreased from 8.42 to 7.19 in the course of primary succession, while soil organic carbon (SOC) and total nitrogen (TN) gradually increased with primary succession. Available phosphorus (AP) and available potassium (AK), as well as potential denitrify activity (PDA), increased gradually and peaked at the 40-year-old site. The abundance of the nosZ denitrifying bacterial community followed a similar trend. The variation in the denitrifying community composition was complex; Mesorhizobium dominated the soil in the early successional stages (0-20 years) and in the mature phase (60 years), with a relative abundance greater than 55%. Brachybacterium was increased in the 40-year-old site, with a relative abundance of 62.74%, while Azospirillum dominated the early successional stages (0-20 years). Redundancy analysis (RDA) showed that the nosZ denitrifying bacterial community correlated with soil available phosphorus and available potassium levels (P < 0.01).
Background: The Hailuogou Glacier in the Gongga Mountain region (SW China), on the southeastern edge of the Tibetan Plateau, is well known for its low-elevation modern glaciers. Since the end of the Little Ice Age (LIA), the Hailuogou Glacier has retreated continuously due to global warming, primary vegetation succession and soil chronosequence have developed in this retreat area. The retreated area of Hailuogou Glacier has not been strongly disturbed by human activities, thus it is an ideal models for exploring the biological colonization of nitrogen in the primary successional stages of ecosystem. The nosZ gene encodes the catalytic center of nitrous oxide reductase and is an ideal molecular marker in studying the variation in the denitrifying bacterial community.Methods: Soil properties as well as abundance and composition of the denitrifying bacterial community were determined via chemical analysis, quantitative polymerase chain reaction (qPCR), and terminal restriction fragment length polymorphism (T-RFLP), respectively. The relationships between the nosZ denitrifying bacterial community and soil properties were determined using redundancy analysis (RDA). Soil properties, potential denitrify activity (PDA), and the nitrous oxide reductase gene (nosZ)-denitrifying bacterial communities significantly differed among successional stages.Results: Soil properties, potential denitrify activity (PDA), and the nitrous oxide reductase gene (nosZ)-denitrifying bacterial communities significantly differed among successional stages. Soil pH in the topsoil decreased from 8.42 to 7.19 in the course of primary succession, while soil organic carbon (SOC) and total nitrogen (TN) gradually increased with primary succession. Available phosphorus (AP) and available potassium (AK), as well as potential denitrify activity (PDA), increased gradually and peaked at the 40-year-old site. The abundance of the nosZ denitrifying bacterial community followed a similar trend. The variation in the denitrifying community composition was complex; Mesorhizobium dominated the soil in the early successional stages (0-20 years) and in the mature phase (60 years), with a relative abundance greater than 55%. Brachybacterium was increased in the 40-year-old site, with a relative abundance of 62.74%, while Azospirillum dominated the early successional stages (0-20 years). Redundancy analysis (RDA) showed that the nosZ denitrifying bacterial community correlated with soil available phosphorus and available potassium levels (P < 0.01).Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIPhttps://peerj.com/preprints/274472018-12-242018-12-24Haifeng WangYinwen ChenZhitao ZhangHaorui ChenXianwen LiMingxiu WangHongyang Chai
Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl-, Mg2+ and SO42- was very high, that of CO32- was high and K+ was relatively lower, but HCO3- failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision.
Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl-, Mg2+ and SO42- was very high, that of CO32- was high and K+ was relatively lower, but HCO3- failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision.