Uncertainties of landslide susceptibility prediction:influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Erosion wear at the bend of pipe during tailings slurry transportation:Numerical study considering inlet velocity,particle size and bend angle

Pipeline hydraulic transport is a highly efficient and low energy-consumption method for transporting solids and is commonly used for tailing slurry transport in the mining industry.Erosion wear(EW)remains the main cause of failure in tailings slurry pipeline systems,particularly at bends.EW is a complex phenomenon influenced by numerous factors,but research in this area has been limited.This study performs numerical simulations of slurry transport at the bend by combining computational fluid dynamics and fluid particle tracking using a wear model.Based on the validation of the feasibility of the model,this work focuses on the effects of coupled inlet velocity(IV)ranging from 1.5 to 3.0 m·s^(-1),particle size(PS)ranging from 50 to 650μm,and bend angle(BA)ranging from 45°to 90°on EW at the bend in terms of particle kinetic energy and incidence angle.The results show that the maximum EW rate of the slurry at the bend increases exponentially with IV and PS and first increases and then decreases with the increase in BA with the inflection point at 60°within these parameter ranges.Further comprehensive analysis reveals that the sensitivity level of the three factors to the maximum EW rate is PS>IV>BA,and when IV is 3.0 m/s,PS is 650μm,and BA is 60°,the bend EW is the most severe,and the maximum EW rate is 5.68×10^(-6)kg·m^(-2)·s^(-1).In addition,When PS is below or equal to 450μm,the maximum EW position is mainly at the outlet of the bend.When PS is greater than 450μm,the maximum EW position shifts toward the center of the bend with the increase in BA.Therefore,EW at the bend can be reduced in practice by reducing IV as much as possible and using small particles.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Uncertainties of landslide susceptibility prediction considering different landslide types

Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects susceptibility prediction performance.To construct efficient susceptibility prediction considering different landslide types,Huichang County in China is taken as example.Firstly,105 rock falls,350 colluvial landslides and 11 related environmental factors are identified.Then four machine learning models,namely logistic regression,multi-layer perception,support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial landslide.Thirdly,three different landslide susceptibility prediction(LSP)models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility:(i)united method,which combines all landslide types directly;(ii)probability statistical method,which couples analyses of susceptibility indices under different landslide types based on probability formula;and(iii)maximum comparison method,which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of landslides.Finally,uncertainties of landslide susceptibility are assessed by prediction accuracy,mean value and standard deviation.It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County.The united method has the best susceptibility prediction performance,followed by the probability method and maximum susceptibility method.More cases are needed to verify this result in-depth.LSP considering different landslide types is superior to that taking only a single type of landslide into account.

Immobilization and leaching characteristics of fluoride from phosphogypsumbased cemented paste backfill

Phosphogypsum(PG)is a typical by-product of phosphoric acid and phosphate fertilizers during acid digestion.The application of cemented paste backfill(CPB)has been feasibly investigated for the remediation of PG.The present study evaluated fluorine immobilization mechanisms and attempted to construct a related thermodynamic and geochemical modeling to describe the related stabilization performance.Physico-chemical and mineralogical analyses were performed on PG and hardened PG-based CPB(PCPB).The correlated macro-and micro-structural properties were obtained from the analysis of the combination of unconfined compressive strength and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy imaging.Acid/base-dependent leaching tests were performed to ascertain fluoride leachab-ility.In addition,Gibbs Energy Minimization Software and PHREEQC were applied as tools to characterize the PCPB hydration and deduce its geochemical characteristics.The results proved that multiple factors are involved in fluorine stabilization,among which the calcium silicate hy-drate gel was found to be associated with retention.Although the quantitative comparison with the experimental data shows that further modi-fication should be introduced into the simulation before being used as a predictive implement to determine PG management options,the im-portance of acid/base concentration in regulating the leaching behavior was confirmed.Moreover,the modeling enabled the identification of the impurity phases controlling the stability and leachability.

The measure of friction angles for different types of granular material

The aim of this research is to deepen the knowledge of the role of friction on the dynamics of granular media; in particular the friction angle is taken into consideration as the physical parameter that drives stability, motion and deposition of a set of grains of any nature and size. The idea behind this work is a question: is the friction angle really that fundamental and obvious physical parameter which rules stability and motion of granular media as it seems from most works which deal with particle dynamics? The experimental study tries to answer this question with a series of laboratory tests, in which different natural and artificial granular materials have been investigated in dry condition by means of a tilting flume. The characteristic friction angles, both in deposition(repose) and stability limit(critical) conditions, were measured and checked against size, shape, density and roughness of the considered granular material. The flume tests have been preferred to "classical" geotechnical apparatuses(e.g. shear box) since the flume experimental conditions appear closer to the natural ones of many situations of slope stability interest(e.g. a scree slope). The results reveal that characteristic friction angles depend on size and shape of grains while mixtures of granules of different size show some sorting mechanism with less clear behaviour.

Control of wind-wave power on morphological shape of salt marsh margins

Salt marshes are among the most common morphological features found in tidal landscapes and provide ecosystem services of primary ecological and economic importance.However,the continued rise in relative sea level and increasing anthropogenic pressures threaten the sustainability of these environments.The alarmingly high rates of salt marsh loss observed worldwide,mainly dictated by the lateral erosion of their margins,call for new insights into the mutual feedbacks among physical,biological,and morphological processes that take place at the critical interface between salt marshes and the adjoining tidal flats.We combined field measurements,remote sensing data,and numerical modeling to investigate the interplays between wind waves and the morphology,ecology,and planform evolution of salt marsh margins in the Venice Lagoon of Italy.Our results confirm the existence of a positive linear relationship between incoming wave power density and rates of salt marsh lateral retreat.In addition,we show that lateral erosion significantly decreases when halophytic vegetation colonizes the marsh margins,and that different erosion rates in vegetated margins are associated with different halophytes.High marsh cliffs and smooth shorelines are expected along rapidly eroding margins,whereas erosion rates are reduced in gently sloped,irregular edges facing shallow tidal flats that are typically exposed to low wind-energy conditions.By highlighting the relationships between the dynamics and functional forms of salt marsh margins,our results represent a critical step to address issues related to conservation and restoration of salt marsh ecosystems,especially in the face of changing environmental forcings.

Reintegrating nanogranitoid inclusion composition to reconstruct the prograde history of melt-depleted rocks

A recent fascinating development in the study of high-grade metamorphic basements is represented by the finding of tiny inclusions of crystallized melt(nanogranitoid inclusions) hosted in peritectic phases of migmatites and granulites. These inclusions have the potential to provide the primary composition of crustal melts at the source. A novel use of the recently-published nanogranitoid compositional database is presented here. Using granulites from the world-renowned Ivrea Zone(NW Italy) on which the original melt-reintegration approach has been previously applied, it is shown that reintegrating melt inclusion compositions from the published database into residual rock compositions can be a further useful method to reconstruct a plausible prograde history of melt-depleted rocks. This reconstruction is fundamental to investigate the tectonothermal history of geological terranes.

Towards hydrometeorological thresholds of reservoir-induced landslide from subsurface strain observations

Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir areas.Landslide forecasting and early warning based on surface displacements have been widely investigated.However,the lack of direct subsurface real-time observations limits our ability to predict critical hydrometeorological conditions that trigger landslide acceleration.In this paper,we leverage subsurface strain data measured by high-resolution fiber optic sensing nerves that were installed in a giant reservoir landslide in the Three Gorges Reservoir(TGR)region,China,spanning a whole hydrologic year since February 2021.The spatiotemporal strain profile has preliminarily identified the slip zones and potential drivers,indicating that high-intensity short-duration rainstorms controlled the landslide kinematics from an observation perspective.Considering the time lag effect,we reexamined and quantified potential controls of accelerated movements using a data-driven approach,which reveals immediate response of landslide deformation to extreme rainfall with a zero-day shift.To identify critical hydrometeorological rules in accelerated movements,accounting for the dual effect of rainfall and reservoir water level variations,we thus construct a landslide prediction model that relies upon the boosting decision tree(BDT)algorithm using a dataset comprising daily rainfall,rainfall intensity,reservoir water level,water level fluctuations,and slip zone strain time series.The results indicate that landslide acceleration is most likely to occur under the conditions of mid-low water levels(i.e.,<169.700 m)and large-amount and high-intensity rainfalls(i.e.,daily rainfall>57.9 mm and rainfall intensity>24.4 mm/h).Moreover,this prediction model allows us to update hydrometeorological thresholds by incorporating the latest monitoring dataset.Standing on the shoulder of this landslide case,our study informs a practical and reliable pathway for georisk early warning based on subsurface observations,particularly in the context of enhanced extreme weather events.

Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models

The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.

Uncertainties of landslide susceptibility prediction:Influences of different spatial resolutions,machine learning models and proportions of training and testing dataset

This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.

川西北地区中三叠统顶部溶蚀型喀斯特特征及地质意义

中三叠统顶部溶蚀型喀斯特在什邡金河剖面及四川盆地大部分地区已有报道。在近期的野外工作中,在川西北地区绵竹汉旺观音崖、江油黄莲桥和江油马鞍塘剖面天井山组近顶部、顶部也识别出这一古特提斯面,其形成与强烈的构造活动或全球海平面下降造成的岩层长时间的暴露有关。其表现形式主要为:(1)暴露溶蚀间断沉积面;(2)溶沟、溶缝及其内的碳酸盐、陆源碎屑充填物;(3)暴露期黏土层;(4)喀斯特角砾。其中暴露溶蚀间断沉积面上的沉积构造以鸟眼、层状孔洞构造最为典型,在绵竹汉旺观音崖剖面最为发育,孔、洞充填物主要为亮晶方解石和白云石;溶沟、溶缝及其内的碳酸盐、陆源碎屑充填物在江油马鞍塘、汉旺观音崖、什邡金河等剖面均有发育,这些溶缝、溶沟一般近于竖直发育,宽度及深度不一,被碳酸盐角砾、亮晶方解石、白云石或陆源黏土等所充填;暴露期黏土层主要见于江油黄莲桥剖面,共发育2套,厚5~20 cm,与铁质矿物共生,其上下发育深灰色潟湖沉积;喀斯特角砾主要见于汉旺观音崖剖面,喀斯特角砾最大可达15×20cm大小,呈棱角状充填于富含泥质和有机质的暗色碳酸盐基质中。天井山组近顶部溶蚀型喀斯特的发现可为区域地层等时对比提供依据,与其有关的古岩溶作用可在天井山组(或雷口坡组)顶部碳酸盐岩中形成以溶蚀孔、洞、缝为主要储集空间的古岩溶型储集层。

Post-Orogenic Tectonic Evolution of the Jiangnan-Xuefeng Orogenic Belt:Insights from Multiple Geochronometric Dating of the Mufushan Massif,South China

The Mufushan massif, as continental intra-plate magmatites located in the Jiangnan-Xuefeng orogenic belt of the South China. The Mufushan massif constitutes the largest Mesozoic intrusive complex, intruded the Mesoproterozoic Lengjiaxi Formation. Multiple geochronometric dating was used to reconstruct their evolution from emplacement to exhumation. The Mufushan granitoids were emplaced at ~150 Ma(U-Pb zircon) as post-orogenic magmatites contributing to Triassic crustal thickening. Onset of regional extension at ~128 Ma(40Ar/39Ar white mica and biotite) manifests a tectonic regime switch. Intense exhumation prior to ~55 Ma was followed by slow denudation and peneplanation for the next 37 Ma(~55–18 Ma). Accelerated cooling since ~18 Ma may have been caused by a far-field effect of the collision between IndiaAsia Plate or the Pacific-Plate subduction. Through a multi-geochronometric approach, this study provides a new comprehensive model for the cause of the intra-plate magmatism formation in the South China, and also established a reliable geochronological framework of the post-orogenic tectonic evolutions of the Jiangnan-Xuefeng orogenic belt.

Revision of the Conodont Mockina slovakensis and Its Paleogeographic Implications for the Upper Triassic Intraplatform Basins of the Alps

Mockina slovakensis,thought to have evolved from Epigondolella praeslovakensis,is an important species of the Norian(Upper Triassic),generally considered as the representative of the uppermost Alaunian to upper Sevatian in the Tethys.The previous description of M.slovakensis was incomplete,thus has led to some misidentifications.We thus update the description of M.slovakensis and discuss its comparisons and occurrence based on the new conodont investigations in Dolomia di Forni and the data from previous literatures.The conodont assemblage in the succession of Dolomia di Forni is dominated by M.slovakensis,along with rare M.postera and E.praeslovakensis.We described two morphotypes of M.slovakensis(morphotypes A and B),on the basis of shape of the lateral profile.These two morphotypes can also be observed in the E.praeslovakensis.Moreover,M.slovakensis is usually documented as almost monospecific conodont association in intraplatform basins,thus its paleogeographic implications are also discussed.

First Records of Late Triassic Conodont Fauna andδ13Ccarb from the Dengdengqiao Section,Dangchang County,Gansu Province,Northwestern China

Based on a study of 49 conodont and 57 geochemical samples from the Upper Triassic,carbonate-dominated Dengdengqiao Formation,Qinling Basin,China,the Carnian conodonts and carbon isotope records are first reported.Two genera and four species have been identified amongst 87 conodont elements:Mosherella praebudaensis,Mo.longnanensis sp.nov.,Mo.sp.,and"Misikella"longidentata.The presence of Mo.praebudaensis indicates that the lower part(bed 2)of the formation is attributable to the Julian(lower Carnian)substage.A radiolarian fauna identified in a previous study belongs to the upper Carnian,but the sampling horizon is unclear.Theδ13Ccarb curve shows a~1.8‰negative excursion beginning from upper part of bed 3,but its stratigraphic location is uncertain.The Dengdengqiao Formation is clearly at least partly of Carnian age but could include younger strata.The abundant calcareous algae at the section is probably due to some transport rather than preserved in site.The unusual ecosystem with rare marine organisms may reflect long-term stressful and unfavorable conditions at Dengdengqiao.

滇西巍山地区上三叠统诺利阶三合洞组牙形石年代及沉积相分析

滇西兰坪–思茅地块晚三叠世诺利期(Norian)发育一套海相碳酸盐岩沉积(三合洞组)。对巍山三合洞剖面进行了实测与采样,分离了三合洞组碳酸盐岩中的牙形石,经鉴定其种属为Epigondolellavialovi,Epigondolella uniformis和Epigondolella aff. triangularis,时代为早诺利期(Lacian亚期)。通过野外剖面及室内显微薄片观察,采用微相分析的方法对剖面三合洞组碳酸盐岩的微相类型和沉积环境进行研究,将巍山三合洞剖面三合洞组沉积归纳为9种微相,并根据微相特征结合野外宏观特征,将三合洞剖面划分为3种沉积相类型,即局限台地相、开阔台地相和台地边缘礁相。在剖面不同层位的碳酸盐岩中发现的热液矿物石榴子石及发黑的牙形石化石(CAI指数约为5),表明该区曾遭受热液作用的影响,可能与三合洞组沉积后大陆边缘裂谷下陷或板块拼合引起的热卤水上涌有关。

云南保山上三叠统诺利阶南梳坝组和大水塘组的牙形石及沉积环境特征

云南保山地块晚三叠世诺利期(Norian)海相沉积发育。通过野外观察、实测保山大堡子剖面,对剖面南梳坝组中部以及大水塘组顶部灰岩进行了牙形石分析,经鉴定有1属3种,分别为Mockina bidentata,Mockina englandi和Mockina aff.englandi,时代为晚诺利期Sevatian1亚期。岩石显微薄片分析显示,南梳坝组中部灰岩和大水塘组顶部灰岩中含大量被方解石完全或部分交代的放射虫,陆源碎屑岩中见磨圆程度不同的碎屑。结合滑塌沉积构造,推测南梳坝组沉积时期大堡子地区为混积陆棚环境,发育了与岛弧(微板块)有关的特殊陆棚沉积模式。

Evolutionary Process from Mockina bidentata to Parvigondolella andrusovi:Evidence from the Pizzo Mondello Section,Sicily,Italy

During their last phase of evolution,the pectiniform conodont elements manifested an evident trend of simplification and miniaturization.This phase started from the late Norian(Sevatian)in the Late Triassic and the evolutionary process of genus Mockina to Parvigondolella,in particular between Mockina bidentata and Parvigondolella andrusovi,is one of the most significant examples.Parvigondolella has been reported worldwide since it was first described in the early 1970s.However,it has recently been suggested that genus Parvigondolella is an ecostratigraphic morphotype of genus Mockina,and thus a phenotype controlled by the environmental conditions,and not an independent taxon.In the Pizzo Mondello Section(Sicily,Italy),transitional forms between M.bidentata and P.andrusovi have been found at different evolutionary stages.We have investigated the oceanic conditions at the time by using redox-sensitive elements(Mn,Fe,V,Cr,and Ni)and seawater temperatures from biogenetic δ^(18)Ophos to understand the possible environmental influences on the phylogenetic evolution between Mockina and Parvigondolella.The geochemical and isotope analyses indicate that the redox condition and temperature were stable during the evolution of genus Parvigondolella in Pizzo Mondello,confirming that genus Parvigondolella is a real taxon and not a phenotype.A new conodont species named Parvigondolella ciarapicae n.sp.is described here for the first time.

Preface

Conodonts are elements of a feeding apparatus of jawless eel-like animals belonging to the clade Vertebrata.They are very important microfossils,ubiquitous in the Paleozoic and Early Mesozoic marine sequences,and they occurred in different habitats,from deep-ocean to shallow-shelf waters.