An integrated framework to map animal distributions in large and remote regions

In this paper we show how new technologies can be incorporated from the gathering of field data on wildlife distribution to the final stage of producing distribution maps. We describe an integrated framework for conducting wildlife censuses to obtain data to build predictive models of species distribution that when integrated in a GIS will produce a distribution map. Field data can be obtained with greater accuracy and at lower costs using a combination of Global Positioning System, Personal Digital Assistant, and specific wildlife recording software. Sampling design benefits from previous knowledge of environmental variability that can be obtained from free remote sensing data. Environmental predictors derived from this remote sensing information alone, combined with automatic procedures for predictor selection and model fitting, can render cost-effective predictive distribution models for wildlife. We show an example with guanaco distribution in the Patagonian steppes of Santa Cruz province, Argentina.     

A novel generalized normal distribution for human longevity and other negatively skewed data

Negatively skewed data arise occasionally in statistical practice; perhaps the most familiar example is the distribution of human longevity. Although other generalizations of the normal distribution exist, we demonstrate a new alternative that apparently fits human longevity data better. We propose an alternative approach of a normal distribution whose scale parameter is conditioned on attained age. This approach is consistent with previous findings that longevity conditioned on survival to the modal age behaves like a normal distribution. We derive such a distribution and demonstrate its accuracy in modeling human longevity data from life tables. The new distribution is characterized by 1. An intuitively straightforward genesis; 2. Closed forms for the pdf, cdf, mode, quantile, and hazard functions; and 3. Accessibility to non-statisticians, based on its close relationship to the normal distribution.     

Parameter-free testing of the shape of a probability distribution

The Kolmogorov-Smirnov test determines the consistency of empirical data with a particular probability distribution. Often, parameters in the distribution are unknown, and have to be estimated from the data. In this case, the Kolmogorov-Smirnov test depends on the form of the particular probability distribution under consideration, even when the estimated parameter-values are used within the distribution. In the present work, we address a less specific problem: to determine the consistency of data with a given functional form of a probability distribution (for example the normal distribution), without enquiring into values of unknown parameters in the distribution. For a wide class of distributions, we present a direct method for determining whether empirical data are consistent with a given functional form of the probability distribution. This utilizes a transformation of the data. If the data are from the class of distributions considered here, the transformation leads to an empirical distribution with no unknown parameters, and hence is susceptible to a standard Kolmogorov-Smirnov test. We give some general analytical results for some of the distributions from the class of distributions considered here. The significance level and power of the tests introduced in this work are estimated from simulations. Some biological applications of the method are given.     

The Distribution of Ocular Chlamydia Prevalence across Tanzanian Communities Where Trachoma Is Declining

BackgroundMathematical models predict an exponential distribution of infection prevalence across communities where a disease is disappearing. Trachoma control programs offer an opportunity to test this hypothesis, as the World Health Organization has targeted trachoma for elimination as a public health concern by the year 2020. Local programs may benefit if a single survey could reveal whether infection was headed towards elimination. Using data from a previously-published 2009 survey, we test the hypothesis that Chlamydia trachomatis prevalence across 75 Tanzanian communities where trachoma had been documented to be disappearing is exponentially distributed.Methods/FindingsWe fit multiple continuous distributions to the Tanzanian data and found the exponential gave the best approximation. Model selection by Akaike Information Criteria (AIC_c) suggested the exponential distribution had the most parsimonious fit to the data. Those distributions which do not include the exponential as a special or limiting case had much lower likelihoods of fitting the observed data. 95% confidence intervals for shape parameter estimates of those distributions which do include the exponential as a special or limiting case were consistent with the exponential. Lastly, goodness-of-fit testing was unable to reject the hypothesis that the prevalence data came from an exponential distribution.ConclusionsModels correctly predict that infection prevalence across communities where a disease is disappearing is best described by an exponential distribution. In Tanzanian communities where local control efforts had reduced the clinical signs of trachoma by 80% over 10 years, an exponential distribution gave the best fit to prevalence data. An exponential distribution has a relatively heavy tail, thus occasional high-prevalence communities are to be expected even when infection is disappearing. A single cross-sectional survey may be able to reveal whether elimination efforts are on-track.     

Predicting the species abundance distribution using a model food web

A large number of models of the species abundance distribution (SAD) have been proposed, many of which are generically similar to the log-normal distribution, from which they are often indistinguishable when describing a given data set. Ecological data sets are necessarily incomplete samples of an ecosystem, subject to statistical noise, and cannot readily be combined to yield a closer approximation to the underlying distribution. In this paper, we adopt the Webworld ecosystem model to study the predicted SAD in detail. The Webworld model is complex, and does not allow analytic examination of such features; rather, we use simulation data and an approach similar to that of ecologists analysing empirical data. By examining large sets of fully described data we are able to resolve features which can distinguish between models but which have not been investigated in detail in field data. We find that the power-law normal distribution is superior to both the log-normal and logit-normal distributions, and that the data can improve on even this at the high-population cut-off.     

Using temporally spaced sequences to simultaneously estimate migration rates, mutation rate and population sizes in measurably evolving populations

We present a Bayesian statistical inference approach for simultaneously estimating mutation rate, population sizes, and migration rates in an island-structured population, using temporal and spatial sequence data. Markov chain Monte Carlo is used to collect samples from the posterior probability distribution. We demonstrate that this chain implementation successfully reaches equilibrium and recovers truth for simulated data. A real HIV DNA sequence data set with two demes, semen and blood, is used as an example to demonstrate the method by fitting asymmetric migration rates and different population sizes. This data set exhibits a bimodal joint posterior distribution, with modes favoring different preferred migration directions. This full data set was subsequently split temporally for further analysis. Qualitative behavior of one subset was similar to the bimodal distribution observed with the full data set. The temporally split data showed significant differences in the posterior distributions and estimates of parameter values over time.     

Assessing ecological habitat structure from local to landscape scales using synthetic aperture radar

Ecological studies need accurate environmental data such as vegetation characterization, landscape structure and organization, to predict and explain the spatial distribution of biodiversity. Few ecological studies use remote sensing data to assess the biophysical or structural properties of vegetation to understand species distribution. To date, synthetic aperture radar (SAR) data have seldom been used for ecological applications. However, these sensors provide data allowing access to the inner structure of vegetation which is a key information in ecology. The objective of this article is to compare the predictive power of ecological habitat structure variables derived from a TerraSAR-X image, an aerial photograph and a SPOT-5 image for species distribution. The test was run with a hedgerow network in Brittany and assessed the spatial distribution of the forest ground carabid beetles which inhabit these hedgerows. The results confirmed that radar and optical images can be indifferently used to extract hedgerow network and derived landscape metrics (hedgerow density, network grain) useful to explain the spatial distribution of forest carabid beetles. In comparison with passive optical remotely sensed data, VHSR SAR images provide new data to characterize vegetation structure and more particularly hedgerow canopy cover, a variable known to explain the spatial distribution of carabid beetles in an agricultural landscape, but not yet quantified at a fine scale. The hedgerow canopy cover derived from the SAR image is a strong predictor of the abundance of forest carabid beetles at two scales i.e., a local scale and a landscape scale.     

Reconstruction of the antibody affinity distribution from experimental binding data by a minimum cross-entropy procedure.

A new solution is presented for the reconstruction of the distribution of association constants of antigen-antibody binding from a finite number of noisy experimental binding data. This ill-posed problem is solved by utilizing an information-theoretic method based on the principle of minimum cross-entropy (MCE) to select, as the solution, that unique antibody binding distribution which minimizes the cross entropy relative to some prior distribution subject to the constraints imposed by the given measurements. The prior distribution is selected to properly encode all the a priori information on the affinity distribution before the measurement of the experimental binding data. The utility of the method is demonstrated by application to synthetic binding data.     

A new approach to the Namib Region

For a new approach to the phytogeography of the Namib region three sets of data are analyzed, (a) distribution data of ca. 1700 taxa, (b) habitat informations of a large number of taxa, collected in course of an extensive phytosociological survey, (c) distribution data of characteristic life form spectra and plant formations.In this paper, as a first step of a comprehensive phytochorological analysis, phytochoria and their limits are proposed as derived from frequently observed areas of distribution, while a numerical analysis of the complete flora of these phytochoria is in preparation.     

Host plant distributions and climate interact to affect the predicted geographic distribution of a Neotropical termite

Species distribution models (SDMs) have been widely used in the scientific literature. The majority of SDMs use climate data or other abiotic variables to forecast the potential distribution of a species in geographic space. Biotic interactions can affect the predicted spatial distribution of a species in many ways across multiple spatial scales, and incorporating these predictors in an SDM is a current topic in the scientific literature. Constrictotermes cyphergaster is a widely distributed termite in the Neotropics. This termite species nests in plants and more frequently nests in some arboreal species. Thus, this species is an excellent model to evaluate the influence of biotic interactions in SDMs. We evaluate the influences of climate and the geographic distribution of host plants on the potential distribution of C. cyphergaster. Three correlative models (MaxEnt) were built to predict the geographic distribution of the termite: (1) climate data, (2) biotic data (i.e., the geographic distribution of host plants), and (3) climate and biotic data. The models that were generated indicate that the potential geographic distribution of C. cyphergaster is concentrated in the Cerrado and Caatinga regions. In addition, path analysis and multiple regression revealed the importance of the direct effects of biological interactions in the geographic distribution of the termite, while climate affected the distribution of the termite mainly through indirect effects by influencing the geographic distributions of host plants. The current study endorses the importance of including biological interactions in SDMs. We recommend using biotic predictors in SDM studies of insect species, mainly because insects have important environmental services and biotic interaction data can improve the macroecological studies of this group.     

Lifespan of Commodities,Part II

Lifespan of commodities is essential information for material flow analysis and material stock accounting. Lifespan data is available in the literature; however, it varies in definition and in methodology employed. This article reviews and categorizes different types of lifespan distribution and distribution estimation methodologies, and investigates the relationship and differences between lifespan definitions and estimation methodologies. Lifespan distribution of commodities can be classified into five types from two perspectives: base year for which the distribution is drawn, and vertical axis of the distribution. The methodologies for estimating lifespan distribution were classified into four types and the details of each methodology and the relationship to the definition of lifespan were also clarified. This article also examines differences in actual lifespan data—between the types of distribution, the definitions, and the employed methodologies—by comparing reported data in literature. Any of the four methodologies are theoretically applicable and provide the same value of a lifespan; however unless accurate data such as census statistics are available, lifespan data can vary, and therefore we must be very cautious about the representativeness of sample data.     

High-performance size-exclusion chromatography (HPSEC) and fluorophore-assisted carbohydrate electrophoresis (FACE) to describe the chain-length distribution of debranched starch

Chain-length (CL) distribution is an important feature of the "fine structure" of starch molecules, which are comprised of amylose and amylopectin. The objective of the present work was to combine data for two methods to achieve a more comprehensive data set that would allow a fuller comparison of the CL distribution for different starches. Both high-performance size-exclusion chromatography (HPSEC) and fluorophore-assisted carbohydrate electrophoresis (FACE) were carried out on endosperm starch isolated from five maize genotypes. For the CL distribution in the range DP50, data in the HPSEC chromatogram were transformed to the form of a FACE electrophoregram, in which the x-axis is DP and the y-axis is the number of chains. The two sets of data in this region were shown to be similar. We conclude that the data sets from HPSEC and FACE may be considered together to describe the CL distribution more completely than for either method alone. We further note that for DP 6-50, data from HPSEC may be transformed to allow a similar presentation as for that obtained by FACE, such that FACE analysis might not be required for comparison of CL distribution of different starches.     

数据来源对蛋白质相互作用网络度分布的影响

蛋白质-蛋白质相互作用(Protein-protein interaction,PPI)是生命体结构和生命活动的基础和特征,控制着生命活动的各个过程.PPI网络是研究蛋白质相互作用的有效手段.随着高通量实验技术的发展,越来越多的PPI数据得以使用,收录蛋白质相互作用的数据库数据每年都有变化.本文对DIP数据库从2003年到2008年的PPI网络数据分别计算度分布.为提高可信度,对注释蛋白质数据库交集进行抽样,分别探讨对不同年份的数据和注释数据库抽样对PPI网络度分布的影响.结果表明,从2003年到2008年的数据增长对PPI网络度分布没有明显影响,而且拟合度分布最优的函数并不是以往所认为的幂率分布(power-law),而是广延指数(stretched exponential)函数,数据库交集抽样同样得到广延指数(stretched exponential)函数分布最优且可信度的高低并不影响PPI网络的度分布.     

Present-day G-6-PD deficit in Sardinia with respect to malarial morbidity and mortality in the past

Current Gd- gene distribution in Sardinia is analyzed using data on a sample of 4300 Sardinian males examined at the time of their pre-military checkup from 1983 to 1986, as well as data in the literature. Also examined is the relationship of current G-6-PD deficit distribution to probable malarial morbidity and mortality during the first half of this century. From data on deficit distribution by altitude analyzed for 100 villages of the island, the authors suggest the possibility of using altitude above sea level to replace incidence of malaria, which was used in the past only as an indicative, rather than substitutive, parameter. The authors also corroborate the hypothesis that G-6-PD deficit distribution is basically a consequence of selection caused by malarial endemicity, although several other factors may have interacted to influence Gd- gene incidence and distribution.     

A statistical model of the VA/Q distribution

A "blocks model" is proposed to model the distribution of the ventilation-perfusion ratio (VA/Q distribution). This model is developed from statistical principles and enables the estimated VA/Q distribution to be interpreted in a straightforward and intuitive manner. Estimation of parameters of the blocks model uses a constrained weighted least-squares procedure. Although developed initially to estimate VA/Q distributions from data generated by the multiple inert gas elimination technique (P. D. Wagner, H. A. Saltzman, and J. B. West, J. Appl. Physiol. 36: 588-599, 1974), the blocks method is applicable to any problem in which the unknown distribution is related to the data through an ill-posed integral equation and is particularly suited for problems in which the data are scarce. The method is illustrated with several examples--hypothetical data representing a wide range of VA/Q distributions as well as some real data.     

A mixture model for occupational exposure mean testing with a limit of detection

Information from detectable exposure measurements randomly sampled from a left-truncated log-normal distribution may be used to evaluate the distribution of nondetectable values that fall below an analytic limit of detection. If the proportion of nondetects is larger than expected under log normality, alternative models to account for these unobserved data should be considered. We discuss one such model that incorporates a mixture of true zero exposures and a log-normal distribution with possible left censoring, previously considered in a different context by Moulton and Halsey (1995, Biometrics 51, 1570-1578). A particular relationship is demonstrated between maximum likelihood parameter estimates based on this mixture model and those assuming either left-truncated or left-censored data. These results emphasize the need for caution when choosing a model to fit data involving nondetectable values. A one-sided likelihood ratio test for comparing mean exposure under the mixture model to an occupational exposure limit is then developed and evaluated via simulations. An example demonstrates the potential impact of specifying an incorrect model for the nondetectable values.     

Reconstructing the orientation distribution of actin filaments in the lamellipodium of migrating keratocytes from electron microscopy tomography data

Migration of motile cells on flat substrates is usually driven by the polymerization of a flat actin filament network. Theoretical models have made different predictions regarding the distribution of the filament orientation in the lamellipodium with respect to the direction of motion. Here we show how one can automatically reconstruct the orientation distribution of actin filaments in the lamellipodium of migrating keratocytes from electron microscopy tomography data. We use two different image analysis methods, an algorithm which explicitly extracts an abstract network representation and an analysis of the gray scale information based on the structure tensor. We show that the two approaches give similar results, both for simulated data and for electron microscopy tomography data from migrating keratocytes. For the lamellipodium at the leading edge of fast moving cells, we find an orientation distribution that is peaked at +35/-35 degrees. For the lamellipodium at the leading edge of slow moving cells as well as for the lamellipodium at the flanks of fast moving cells, one broad peak around 0 degree dominates the distribution.     

Exact inference for matched case-control studies

In an epidemiological study with a small sample size or a sparse data structure, the use of an asymptotic method of analysis may not be appropriate. In this paper we present an alternative method of analyzing data for case-control studies with a matched design that does not rely on large-sample assumptions. A recursive algorithm to compute the exact distribution of the conditional sufficient statistics of the parameters of the logistic model for such a design is given. This distribution can be used to perform exact inference on model parameters, the methodology of which is outlined. To illustrate the exact method, and compare it with the conventional asymptotic method, analyses of data from two case-control studies are also presented.     

Semiparametric models for missing covariate and response data in regression models

We consider a class of semiparametric models for the covariate distribution and missing data mechanism for missing covariate and/or response data for general classes of regression models including generalized linear models and generalized linear mixed models. Ignorable and nonignorable missing covariate and/or response data are considered. The proposed semiparametric model can be viewed as a sensitivity analysis for model misspecification of the missing covariate distribution and/or missing data mechanism. The semiparametric model consists of a generalized additive model (GAM) for the covariate distribution and/or missing data mechanism. Penalized regression splines are used to express the GAMs as a generalized linear mixed effects model, in which the variance of the corresponding random effects provides an intuitive index for choosing between the semiparametric and parametric model. Maximum likelihood estimates are then obtained via the EM algorithm. Simulations are given to demonstrate the methodology, and a real data set from a melanoma cancer clinical trial is analyzed using the proposed methods.     

Analysis of vesicular-arbuscular mycorrhizal colonization data with a logistic regression model

 Vesicular-arbuscular mycorrhizal (VAM) infection is usually expressed as percentage of root length colonized. The frequency distributions of the data are often non-normal and may follow a negative binomial distribution. Data transformation, such as an arcsin of percentage colonization, may be used to help colonization data satisfy the normal distribution assumption, but is not always successful. In this paper, we compare ANOVA and logistic regression model (LRM) analysis of data on the effect of phosphorus fertilization and corn cultivar on VAM colonization. Transformed data did not fit a normal distribution, and we propose the LRM as a better model for statistical analysis of VAM colonization. The LRM is more accurate because (1) this model assumes a binomial distribution, (2) it incorporates the original sample size into the probability estimation, and (3) the model uses non-transformed data, which are easier to interpret. Accepted: 3 November ▪▪▪