Linear Model In Order Research Articles

Bayesian approach to inverse problems: an application to NNPDF closure testing

We discuss the Bayesian approach to the solution of inverse problems and apply the formalism to analyse the closure tests performed by the NNPDF collaboration. Starting from a comparison with the approach that is currently used for the determination of parton distributions (PDFs), we discuss some analytical results that can be obtained for linear problems and use these results as a guidance for more complicated non-linear problems. We show that, in the case of Gaussian distributions, the posterior probability density of the parametrized PDFs is fully determined by the results of the NNPDF fitting procedure. Building on the insight that we obtain from the analytical results, we introduce new estimators to assess the statistical faithfulness of the fit results in closure tests. These estimators are defined in data space, and can be studied analytically using the Bayesian formalism in a linear model in order to clarify their meaning. Finally we present results from a number of closure tests performed with current NNPDF methodologies. These further tests allow us to validate the NNPDF4.0 methodology and provide a quantitative comparison of the NNPDF4.0 and NNPDF3.1 methodologies. As PDFs determinations move into precision territory, the need for a careful validation of the methodology becomes increasingly important: the error bar has become the focal point of contemporary PDFs determinations. In this perspective, theoretical assumptions and other sources of error are best formulated and analysed in the Bayesian framework, which provides an ideal language to address the precision and the accuracy of current fits.

Predicting the risk of Alaria alata infestation in wild boar on the basis of environmental factors.

Alaria alata is an emerging parasite that poses a potential risk for those consuming game, pork, snails and frogs. One paratenic host of A. alata that is known to play an important role in its spread through its feeding habitats is the wild boar. However, no statistical analysis of the influence of aquatic environments and carnivores on the occurrence of A. alata in wild boars has yet been performed. The present study combines a small-scale analysis based on hunting districts in the Mazowieckie province with a large-scale analysis based on data for all provinces in Poland. We applied various modeling approaches, including logistic regression and a generalized linear model in order to determine the presence, intensity and prevalence of A. alata. We used the Alaria mesocercariae migration technique (AMT) to estimate the risk of A. alata among wild boar in a given hunting district or province. The small-scale analysis found that mesopredators (red fox (Vulpes vulpes)) and racoon dog (Nyctereutes procyinoides) were likely to influence A. alata infestation of wild boar; however, the effect was weak, probably as a result of the large home range size of these animals. The large-scale analysis found that wetlands influence the prevalence of A. alata in wild boar, with the estimated risk increasing in the north of the country; this finding is consistent with other studies. Our findings indicate that the occurrence of A. alata in wild boar requires analysis on many levels, and environmental factors play a key role in risk assessment.

Non-invasive hormone monitoring as a robust method for determining adrenocortical activity in injured, emaciated and oil-contaminated African penguins undergoing rehabilitation

Anthropogenic activity is a major driver of seabird injury and mortality in the 21st century. Although most seabirds perish within the natural environment as a result of human activities, some are rescued and admitted to rehabilitation centres. Despite the considerable number of admissions, little is known regarding the physiological response seabirds have to specific admission reasons and the rehabilitation process. In this study, we aimed to determine the effect of injury, emaciation, oiling, individual removal from the natural environment and the rehabilitation process on the physiological stress response of the African penguin (Spheniscus demersus). Urofaecal samples were collected from African penguins throughout a three-stage rehabilitation process and quantified for glucocorticoid metabolites (ufGCM). The three stages included an initial ICU crate stage (Stage 1), an ICU pen stage (Stage 2) and a rehabilitation stage (Stage 3). Data were analysed using a generalised linear model in order to determine the effect of admission reason, age and rehabilitation stage (Stage 1, 2 and 3) on ufGCM levels. Although the model indicated that only Stage 1 was a significant driver of adrenocortical activity in the study population, ufGCM levels of injured and emaciated animals within Stage 1 were considerably higher than those of birds with any other admission reason across all stages. This is the first study examining the causes and effect of rehabilitation on the physiological stress response in African penguins. Enhanced care and attention should be given to rescued individuals, especially during the first stage of rehabilitation, to reduce perception of additional stressors and thus increase the chance of full recovery.

Teachers' well-being in times of Covid-19 pandemic: factors that explain professional well-being

This cross-sectional study was aimed to describe and analyse factors related to the professional well-being of Portuguese teachers during the COVID-19 pandemic. A previously validated, anonymous and online questionnaire was applied to a non-probabilistic sample of Portuguese teachers. In addition to the socio-demographic and professional variables, the questionnaire measured subjective and professional well-being, satisfaction with the education system and future perspectives regarding the professional circumstances. The psychometric characteristics of the questionnaire were analysed, the t-test and ANOVA were used to analyse the differences between the main variables and the socio-demographic and professional characteristics, along with the generalized linear model in order to more precisely determine the predicting factors of teachers' professional well-being. The results showed that the majority of respondents have a moderately positive perception of well-being. Teachers were satisfied with the education system before the pandemic. The pandemic has reduced the perception of well-being in the face of the profession, creating some concern among teachers about their professional future. Sex, length of service, well-being, perceptions of teaching difficulties and future perspectives proved to be predictors of professional well-being in times of pandemic. This study provided scientific bases for the development of interventions that improve the professional well-being of teachers, which focus on emotional management and the development of digital skills in teachers with long careers. This piece of research concluded with thorough evidence that there is a need for further and stronger investment in public policies for the reform and, consequently, for the renewal of the teaching class.

Tilted beam scanning electron microscopy, 3-D metrology for microelectronics industry

In the microelectronics industry, most of the dimensional metrology relies on critical dimension (CD) estimation. These measurements are mainly performed by critical dimension scanning electron microscopy, because it is a very fast, mainly nondestructive method and enables direct measurements on wafers. To measure CDs, the distance is estimated between the edges of the observed pattern on an SEM image. As the CD becomes smaller and smaller, the needs for more reliable metrology techniques emerge. In order to obtain more meaningful and reproducible CD measurements regardless of the pattern type (line, space, contact, hole, etc.), one needs to perform a CD measurement at a known and constant height due to a methodology that determines the topographic shape of the pattern from SEM images. An SEM capable of bending the electron beam (up to 12 deg in our case) allows images to be caught at different angles, giving access to more information. From the analysis of such images, pattern height and sidewall angles can be determined using geometric considerations. Understanding interaction between three-dimensional (3-D) shapes, pattern materials, and the electron beam becomes essential to correlate topography information. A preliminary work based on Monte–Carlo simulations was conducted using JMONSEL, a software developed by the National Institute of Standards and Technology. With this analysis, it is possible to determine theoretical trends for different topographies and beam tilt conditions. Due to the effects highlighted by simulations, the processing of the tilted beam SEM images will be presented, as well as the method used to create a mathematical model allowing topographic reconstruction from these images. Finally some reconstruction using this model will be shown and compared to reference measurements. The overall flow used to process images is presented. First, images are transformed into grayscale profiles. After a smoothing procedure, positional descriptors are computed for specific profile derivatives values. Then, from these descriptors coming from two images of the same pattern taken at different tilt angles, we use a low-complexity linear model in order to obtain the geometrical parameters of the structure. This model is created and initially calibrated using JMONSEL simulations and then recalibrated on real silicon patterns. We demonstrate that the use of real SEM images coming from real silicon patterns with our model leads to results that are coherent with conventional 3-D measurements techniques taken as reference. Moreover, we are able to make reliable reconstructions on patterns of various heights with a single calibrated model. Our batch of experiment shows a three-sigma standard deviation of 10 nm on the estimated height for heights ranging from 50 nm to more than 200 nm. Based on simulations, we are able to reconstruct the corner rounding (CR) from SEM images. However, because our wafer has no CR variability, measurements still need to be assessed on real wafer.

The Innovation - Corporate Performance Relationship in Emergent Countries

Abstract Innovation has become a highly actual topic. The main goal of this study is to identify the existence of any possible impact of innovation on companies’ performances in the case of companies from 115 emergent countries, based on data provided by the World Bank. Thus, using the methodology of principal components analysis, we proposed two composite indexes: a multidimensional composite index measuring the corporate sector's innovation and another composite index measuring the companies’ performances for selected countries. In the case of innovation, four different dimensions were taken into consideration oriented towards the company's endeavor in obtaining the recognition of its products’ quality and accounting practices and also in enhancing its visibility on market and improving communication with business partners using information and communication technologies. Also, corporate performances were described by four measures regarding the dynamics of the annual sales and labor productivity and the company's propensity towards investing in fixed assets or in employee's formal training. The main output of this paper is that, using the generalized linear model in order to identify the relationship between the proposed two composite indexes, the innovation described by the selected dimensions has a significant influence on companies’ performances measures in selected countries.

Revealed access to haemodialysis facilities in northeastern Iran: Factors that matter in rural and urban areas.

Poor access to haemodialysis facilities is associated with high mortality and morbidity rates. This study investigated factors affecting revealed access to the haemodialysis facilities considering patients living in rural and urban areas without any haemodialysis facility (Group A) and those living urban areas with haemodialysis facilities (Group B). This study is based on selfreported Actual Access Time (AAT) to referred haemodialysis facilities and other information regarding travel to haemodialysis facilities from patients. All significant variables on univariate analysis were entered into a univariate general linear model in order to identify factors associated with AAT. Both spatial (driving time and distance) and non-spatial factors (sex, income level, caregivers, transportation mode, education level, ethnicity and personal vehicle ownership) influenced the revealed access identified in Group A. The non-spatial factors for Group B patients were the same as for Group A, but no spatial factor was identified in Group B. It was found that accessibility is strongly underestimated when driving time is chosen as accessibility measure to haemodialysis facilities. Analysis of revealed access determinants provides policymakers with an appropriate decision base for making appropriate decisions and finding solutions to decrease the access time for patients under haemodialysis therapy. Driving time alone is not a good proxy for measuring access to haemodialysis facilities as there are many other potential obstacles, such as women's special travel problems, poor other transportation possibilities, ethnicity disparities, low education levels, low caregiver status and low-income.

Prediction of functional recovery six months following traumatic spinal cord injury during acute care hospitalization

Objectives: To determine factors associated with functional status six months following a traumatic cervical and thoracic spinal cord injury (SCI), with a particular interest in factors related to the acute care hospitalization stay. Design: This is a prospective cohort study. Sixteen potential predictive variables were studied. Univariate regression analyses were first performed to determine the strength of association of each variable independently with the total Spinal Cord Independence Measure (SCIM) score. Significant ones were then included in a General linear model in order to determine the most relevant predictive factors among them. Analyses were carried out separately for tetraplegia and paraplegia. Setting: A single specialized Level I trauma center. Participants: One hundred fifty-nine patients hospitalized for an acute traumatic SCI between January 2010 and February 2015. Interventions: Not applicable. Main outcome measure: The SCIM (version 3) functional score. Results: Motor-complete SCI (AIS-A,B) was the main predictive factor associated with decreased total SCIM score in tetraplegia and paraplegia. Longer acute care length of stay and the occurrence of acute medical complications (either pneumonia, urinary tract infections or pressure ulcers) were predictors of decreased functional outcome following tetraplegia, while increased body mass index and higher trauma severity were predictive of decreased functional outcome following paraplegia. Conclusions: This study supports previous work while adding information regarding the importance of optimizing acute care hospitalization as it may influence chronic functional status following traumatic SCI.

Bifacial Silicon Solar Cell Steady Photoconductivity under Constant Magnetic Field and Junction Recombination Velocity Effects

The paper reported a theoretical study on the photoconductivity of a bifacial silicon solar cell under polychromatic illumination and a constant magnetic field effect. By use of the continuity equation in the base of the solar cell maintained in a constant temperature at 300 K, an expression of the excess minority carriers’ density was determined according to the applied magnetic field, the base depth and the junction recombination velocity. From the expression of the minority carriers’ density, the photoconductivity of the solar cell was deduced and which allowed us to predict some recombination phenomena, the use of such solar cell in optoelectronics. The profile of the photoconductivity also permitted us to utilize a linear model in order to determine an electrical capacitance that varied with magnetic field.

SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity

Functional hubs are defined as the specific brain regions with dense connections to other regions in a functional brain network. Among them, connector hubs are of great interests, as they are assumed to promote global and hierarchical communications between functionally specialized networks. Damage to connector hubs may have a more crucial effect on the system than does damage to other hubs. Hubs in graph theory are often identified from a correlation matrix, and classified as connector hubs when the hubs are more connected to regions in other networks than within the networks to which they belong. However, the identification of hubs from functional data is more complex than that from structural data, notably because of the inherent problem of multicollinearity between temporal dynamics within a functional network. In this context, we developed and validated a method to reliably identify connectors and corresponding overlapping network structure from resting-state fMRI. This new method is actually handling the multicollinearity issue, since it does not rely on counting the number of connections from a thresholded correlation matrix. The novelty of the proposed method is that besides counting the number of networks involved in each voxel, it allows us to identify which networks are actually involved in each voxel, using a data-driven sparse general linear model in order to identify brain regions involved in more than one network. Moreover, we added a bootstrap resampling strategy to assess statistically the reproducibility of our results at the single subject level. The unified framework is called SPARK, i.e. SParsity-based Analysis of Reliable k-hubness, where k-hubness denotes the number of networks overlapping in each voxel. The accuracy and robustness of SPARK were evaluated using two dimensional box simulations and realistic simulations that examined detection of artificial hubs generated on real data. Then, test/retest reliability of the method was assessed using the 1000 Functional Connectome Project database, which includes data obtained from 25 healthy subjects at three different occasions with long and short intervals between sessions. We demonstrated that SPARK provides an accurate and reliable estimation of k-hubness, suggesting a promising tool for understanding hub organization in resting-state fMRI.

Fold-change threshold screening: a robust algorithm to unmask hidden gene expression patterns in noisy aggregated transcriptome data.

Transcriptomics is often used to investigate changes in an organism's genetic response to environmental contamination. Data noise can mask the effects of contaminants making it difficult to detect responding genes. Because the number of genes which are found differentially expressed in transcriptome data is often very large, algorithms are needed to reduce the number down to a few robust discriminative genes. We present an algorithm for aggregated analysis of transcriptome data which uses multiple fold-change thresholds (threshold screening) and p values from Bayesian generalized linear model in order to assess the robustness of a gene as a potential indicator for the treatments tested. The algorithm provides a robustness indicator (ROBI) as well as a significance profile, which can be used to assess the statistical significance of a given gene for different fold-change thresholds. Using ROBI, eight discriminative genes were identified from an exemplary dataset (Danio rerio FET treated with chlorpyrifos, methylmercury, and PCB) which could be potential indicators for a given substance. Significance profiles uncovered genetic effects and revealed appropriate fold-change thresholds for single genes or gene clusters. Fold-change threshold screening is a powerful tool for dimensionality reduction and feature selection in transcriptome data, as it effectively reduces the number of detected genes suitable for environmental monitoring. In addition, it is able to unmask patterns in altered genetic expression hidden by data noise and reduces the chance of type II errors, e.g., in environmental screening.

A Bayesian approach for structure learning in oscillating regulatory networks.

Motivation: Oscillations lie at the core of many biological processes, from the cell cycle, to circadian oscillations and developmental processes. Time-keeping mechanisms are essential to enable organisms to adapt to varying conditions in environmental cycles, from day/night to seasonal. Transcriptional regulatory networks are one of the mechanisms behind these biological oscillations. However, while identifying cyclically expressed genes from time series measurements is relatively easy, determining the structure of the interaction network underpinning the oscillation is a far more challenging problem.Results: Here, we explicitly leverage the oscillatory nature of the transcriptional signals and present a method for reconstructing network interactions tailored to this special but important class of genetic circuits. Our method is based on projecting the signal onto a set of oscillatory basis functions using a Discrete Fourier Transform. We build a Bayesian Hierarchical model within a frequency domain linear model in order to enforce sparsity and incorporate prior knowledge about the network structure. Experiments on real and simulated data show that the method can lead to substantial improvements over competing approaches if the oscillatory assumption is met, and remains competitive also in cases it is not.Availability: DSS, experiment scripts and data are available at http://homepages.inf.ed.ac.uk/gsanguin/DSS.zip.Contact: d.trejo-banos@sms.ed.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Trends in the average temperature in Finland, 1847–2013

The change in the mean temperature in Finland is investigated with a dynamic linear model in order to define the sign and the magnitude of the trend in the temperature time series within the last 166 years. The data consists of gridded monthly mean temperatures. The grid has a 10 km spatial resolution, and it was created by interpolating a homogenized temperature series measured at Finnish weather stations. Seasonal variation in the temperature and the autocorrelation structure of the time series were taken account in the model. Finnish temperature time series exhibits a statistically significant trend, which is consistent with human-induced global warming. The mean temperature has risen very likely over 2 °C in the years 1847–2013, which amounts to 0.14 °C/decade. The warming after the late 1960s has been more rapid than ever before. The increase in the temperature has been highest in November, December and January. Also spring months (March, April, May) have warmed more than the annual average, but the change in summer months has been less evident. The detected warming exceeds the global trend clearly, which matches the postulation that the warming is stronger at higher latitudes.

Reliable quantification of BOLD fMRI cerebrovascular reactivity despite poor breath-hold performance

Cerebrovascular reactivity (CVR) can be mapped using BOLD fMRI to provide a clinical insight into vascular health that can be used to diagnose cerebrovascular disease. Breath-holds are a readily accessible method for producing the required arterial CO2 increases but their implementation into clinical studies is limited by concerns that patients will demonstrate highly variable performance of breath-hold challenges. This study assesses the repeatability of CVR measurements despite poor task performance, to determine if and how robust results could be achieved with breath-holds in patients. Twelve healthy volunteers were scanned at 3T. Six functional scans were acquired, each consisting of 6 breath-hold challenges (10, 15, or 20s duration) interleaved with periods of paced breathing. These scans simulated the varying breath-hold consistency and ability levels that may occur in patient data. Uniform ramps, time-scaled ramps, and end-tidal CO2 data were used as regressors in a general linear model in order to measure CVR at the grey matter, regional, and voxelwise level. The intraclass correlation coefficient (ICC) quantified the repeatability of the CVR measurement for each breath-hold regressor type and scale of interest across the variable task performances. The ramp regressors did not fully account for variability in breath-hold performance and did not achieve acceptable repeatability (ICC<0.4) in several regions analysed. In contrast, the end-tidal CO2 regressors resulted in “excellent” repeatability (ICC=0.82) in the average grey matter data, and resulted in acceptable repeatability in all smaller regions tested (ICC>0.4). Further analysis of intra-subject CVR variability across the brain (ICCspatial and voxelwise correlation) supported the use of end-tidal CO2 data to extract robust whole-brain CVR maps, despite variability in breath-hold performance. We conclude that the incorporation of end-tidal CO2 monitoring into scanning enables robust, repeatable measurement of CVR that makes breath-hold challenges suitable for routine clinical practice.

The opposition effect in Saturn’s main rings as seen by Cassini ISS: 1. Morphology of phase functions and dependence on the local optical depth

During the Prime mission (2004–2008), the Cassini cameras captured the opposition effect in Saturn’s main rings with a fine radial resolution at extremely small phase angles. Phase functions were extracted for 211 different radial locations in the main ring system (135 in the broadband clear filter of the Wide Angle Camera at 635nm, 76 in four narrowband color filters (at the respective central wavelengths 440, 568, 650, and 752nm for the Narrow Angle Camera and 460, 567, 649, and 742nm for the Wide Angle Camera). The morphology of the observed opposition surges is fitted with a simple linear model in order to derive three useful parameters – the amplitude A, the width of the surge HWHM, and the slope S at larger phase angles – throughout the whole main ring system. In agreement with previous studies, the opposition surge is found to vary substantially across the ring system: whereas values of A and HWHM are nearly constant across the A and B Rings, they show strong local variations for the C Ring and the Cassini Division. Thanks to the high spatial resolution of Cassini images, the opposition surges are obtained for very narrow regions, allowing us to study the effect of the local normal optical depth (τ) on the surge. For τ>0.7, A(τ) and HWHM(τ) are nearly constant (1.3° and 0.25°, respectively) across the whole ring system. For τ<0.7, A(τ) and HWHM(τ) exhibit clear decreasing trends for the A, B and C Rings only. Conversely, points from the Cassini Division show a very large dispersion in both A and HWHM, with no visible trend. The slope S is found to be an increasing function of the optical depth across the full ring system. A trend S∝τ0.5 is observed, in qualitative agreement with a geometrical description of the shadow hiding effect. No strong trend of A, HWHM and S with the wavelength of observation was found. We also tabulate the fit parameters for the opposition surge of 139 regions in the clear filter for future comparison with numerical simulations and photometric models.

A single nucleotide polymorphism in the promoter region of river buffalo stearoyl CoA desaturase gene (SCD) is associated with milk yield

An association study between the milk yield trait and the stearoyl-CoA desaturase (SCD) polymorphism (g.133A > C) in Italian Mediterranean river buffalo was carried out. A full characterization of the river buffalo SCD promoter region was presented. Genotyping information was provided and a quick method for allelic discrimination was developed. The frequency of the C allele was 0·16. Test-day (TD) records (43 510) of milk production belonging to 226 lactations of 169 buffalo cows were analysed with a mixed linear model in order to estimate the effect of g.133A > C genotype, as well as the effect of parity and calving season. The SCD genotype was significantly associated with milk yield (P = 0·02). The genotype AC showed an over-dominance effect with an average daily milk yield approximately 2 kg/d higher than CC buffaloes. Such a difference represents about 28% more milk/d. The effect of the genotype was constant across lactation stages. The contribution of SCD genotype (r(2)SCD) to the total phenotypic variance in milk yield was equal to 0·12. This report is among the first indications of genetic association between a trait of economic importance in river buffalo. Although such results need to be confirmed with large-scale studies in the same and other buffalo populations, they might offer useful indications for the application of MAS programmes in river buffalo and in the future they might be of great economic interest for the river buffalo dairy industry.

Information Generation and Lagrangian Coherent Structures in Multiscroll Attractors

The present work aims to apply a recently alternative approach to estimate the Lyapunov exponents that is suitable for such piecewise linear model in order of characterizing - with the aid of the metric entropy - the degree of information generated by several kinds of multiscroll attractors. Another analytical path is followed with the extraction of the corresponding Lagrangian coherent structures, which allows the determination of the dynamical system separatrices and also of the folding/stretching behavior that results in chaos. Overall, the reported study can be understood as an investigation, from distinct theoretical standpoints, of key aspects regarding the dynamic behavior of systems capable of engendering multiscroll structures.

일반화가법부분선형모형을 이용한 자동차보험 충성도 요인분석

우리나라 자동차보험 시장은 이미 성숙기에 접어들어 성장률이 감소하고 있는 추세이다. 이러한 시기에는 새로운 고객을 유치하는 것보다도 기존 고객을 유지하는, 즉 가입한 보험회사에 대한 충성도를 높이는 것이 새로운 마케팅 경쟁전략의 목표가 될 수 있을 것이다. 이에 본 연구에서는 개인용 자동차보험시장의 현 경쟁 환경 및 향후 변화될 환경에 맞추어 손해보험회사들이 새로운 자동차보험 마케팅 전략을 수립하는데 도움이 되는 마케팅 전략을 도출 제시하고자 한다. 이를 위하여 자동차보험 가입자를 대상으로 설문조사를 진행하였고, 일반화가법부분선형모형을 활용하여 분석을 시행하여 충성도에 영향을 주는 유의한 고객만족도 요인을 찾아내었다. The car insurance market in Korea has already entered (or is in the process of entry) a mature market that is characterized by increased competition by market participants. Participants are expected to compete more intensively in order to survive. Together with a slowdown in market growth the goal of non-life insurers' marketing strategies is to enhance existing customer loyalty because it is easier to raise their loyalty via customer satisfaction than to attract new customers in a stagnant market. In this article, we investigate what factors affect customer loyalty, and suggest some specific ways to establish and implement marketing strategies. We use a generalized additive partial linear model in order to find some significant factors.

Spatial Agglomeration and Wages in the U.S. Biotechnology Sector

Recent studies show that firms and workers benefit from the geographic concentration or spatial agglomeration of employment in a local labor market. While economic geographers have devoted considerable attention to the benefits firms receive from spatial agglomeration, the reasons workers may benefit in the form of higher wages are less clear. I draw from several theoretical perspectives in order to explain the relationship between spatial agglomeration and wages for U.S. biotech workers, including the new economic geography, new structuralism, organizational ecology, and the new economic sociology. Using the 1990 PUMS-L, I employ a hierarchical linear model in order to test hypotheses from these perspectives. The results indicate that biotech workers benefit more from industrial than urban agglomeration, and these benefits are primarily due to the greater degree of organizational diversity in large industry clusters and urban labor markets.

Niche partitioning based on soil type and climate at the landscape scale in a community of plant-feeding nematodes

Understanding how environmental factors structure communities is important in conservation biology and ecosystem management. The aim of this study was to test the hypothesis that a plant-feeding nematode community composed of six species is structured by soil type and climate at the landscape scale, and that niche partitioning via these factors is consistent with the coexistence of the species. Martinique has an impressive diversity of abiotic factors (climate and soil type) over a relatively small land area, which facilitates the study of how soil type and climate affect the nematode community.We conducted this study by building an extensive data set containing the abundance of each nematode species on banana (3708 samples and 5,673,705 nematodes) in a wide range of sites in Martinique. The data set also contained environmental data (soil, climate) and plantation age. We analyzed the response of each nematode species to climate and soil type with a generalized linear model in order to understand whether niche partitioning of factors could contribute to the coexistence of the nematode species.Temperature, rainfall, soil type, and plantation age significantly affected the abundance of the six nematode species. While some pairs of species shared the same environmental niches, other showed clear niche partitioning along climatic axes. The two dominant species, Radopholus similis and Helicotylenchus multicinctus, have similar convergent ecological niches regarding climate, soil type, plantation age, and host range. These two species, which often co-occur, probably have different resources at the root scale. Soil type and climate structure plant-feeding nematode species community at the island scale. Further studies need to evaluate coexistence at the root scale among dominant species.