Differential measurement errors in zero-truncated regression models for count data

Measurement errors in covariates may result in biased estimates in regression analysis. Most methods to correct this bias assume nondifferential measurement errors-i.e., that measurement errors are independent of the response variable. However, in regression models for zero-truncated count data, the number of error-prone covariate measurements for a given observational unit can equal its response count, implying a situation of differential measurement errors. To address this challenge, we develop a modified conditional score approach to achieve consistent estimation. The proposed method represents a novel technique, with efficiency gains achieved by augmenting random errors, and performs well in a simulation study. The method is demonstrated in an ecology application.     

A class of markov models for longitudinal ordinal data

Generalized linear models with serial dependence are often used for short longitudinal series. Heagerty (2002, Biometrics58, 342-351) has proposed marginalized transition models for the analysis of longitudinal binary data. In this article, we extend this work to accommodate longitudinal ordinal data. Fisher-scoring algorithms are developed for estimation. Methods are illustrated on quality-of-life data from a recent colorectal cancer clinical trial.     

Acoustic estimation of wildlife abundance: methodology for vocal mammals in forested habitats

Habitat loss and hunting pressure threaten mammal populations worldwide, generating critical time constraints on trend assessment. This study introduces a new survey method that samples continuously and non‐invasively over long time periods, obtaining estimates of abundance from vocalization rates. We present feasibility assessment methods for acoustic surveys and develop equations for estimating population size. As an illustration, we demonstrate the feasibility of acoustic surveys for African forest elephants (Loxodonta africana cyclotis). Visual surveys and vocalizations from a forest clearing in the Central African Republic were used to establish that low‐frequency elephant calling rate is a useful index of elephant numbers (linear regression P < 0.001, r_adj.² = 0.58). The effective sampling area was 3.22 km² per acoustic sensor, a dramatic increase in coverage over dung survey transects. These results support the use of acoustic surveys for estimating elephant abundance over large remote areas and in diverse habitats, using a distributed network of acoustic sensors. The abundance estimation methods presented can be applied in surveys of any species for which an acoustic abundance index and detection function have been established. This acoustic survey technique provides an opportunity to improve management and conservation of many acoustically‐active taxa whose populations are currently under‐monitored.     

Stochastic models for an enzyme reaction in an open linear system

A stochastic model is developed for an enzyme reaction in an open linear system. The proposed model assumes that the open system maintains the concentration of substrate and inhibitor at constant levels and that the product molecules are removed from the system by a first order reaction. Stochastic models for several enzyme reactions occurring in this open system are shown to correspond to special cases of theGI/M/∞ queue. Takács’ (1958) results for this queueing system are used to obtain the stochastic properties of the enzyme systems. A specific model we studied assumed completely competitive inhibition in an open system. The stationary distribution for the number of product molecules in the system is obtained. The enzyme reaction which incorporated the “intermediate chain hypothesis” can also be investigated by the queueing theory approach. It is shown that for this open system, if the model which incorporated the intermediate chain hypothesis has the same deterministic properties as the Michaelis-Menten model, then the latter has greater stochastic variation than the former. Research supported by the NIH Training Grant No. GM 1237-05 awarded to the Department of Statistics, The Johns Hopkins University. This paper in whole or in part may be reproduced for any purpose of the United States Government.     

Detecting and cleaning outliers for robust estimation of variogram models in insect count data

Outlier detection and cleaning procedures were evaluated to estimate mathematical restricted variogram models with discrete insect population count data. Because variogram modeling is significantly affected by outliers, methods to detect and clean outliers from data sets are critical for proper variogram modeling. In this study, we examined spatial data in the form of discrete measurements of insect counts on a rectangular grid. Two well-known insect pest population data were analyzed; one data set was the western flower thrips, Frankliniella occidentalis (Pergande) on greenhouse cucumbers and the other was the greenhouse whitefly, Trialeurodes vaporariorum (Westwood) on greenhouse cherry tomatoes. A spatial additive outlier model was constructed to detect outliers in both the isolated and patchy spatial distributions of outliers, and the outliers were cleaned with the neighboring median cleaner. To analyze the effect of outliers, we compared the relative nugget effects of data cleaned of outliers and data still containing outliers after transformation. In addition, the correlation coefficients between the actual and predicted values were compared using the leave-one-out cross-validation method with data cleaned of outliers and non-cleaned data after unbiased back transformation. The outlier detection and cleaning procedure improved geostatistical analysis, particularly by reducing the nugget effect, which greatly impacts the prediction variance of kriging. Consequently, the outlier detection and cleaning procedures used here improved the results of geostatistical analysis with highly skewed and extremely fluctuating data, such as insect counts.     

Indices of environmental temperatures for primates in open habitats

Studies of thermoregulation in primates are under-represented in the literature, although there is sufficient evidence to suggest that temperature represents an important ecological constraint. One of the problems in examining thermoregulation in primates, however, is the difficulty in quantifying the thermal environment, since shade temperatures, solar radiation, humidity and wind speed all serve to alter an animals perceived temperature. Since animals respond to their perceived temperature, we need methods to account for each of these factors, both individually and collectively, if we are to understand the integrated impact of the thermal environment on primates. Here, we present a review of some thermal indices currently available. Black bulb temperatures can account for the effect of solar radiation, with wind chill equivalent temperatures and the heat index providing quantifiable estimates of the relative impact of wind speed and humidity, respectively. We present three potential indices of the perceived environmental temperature (PET) that account for the combined impact of solar radiation, humidity and wind speed on temperature, and perform a preliminary test of all of the climatic indices against behavioural data from a field study of chacma baboons (Papio cynocephalus ursinus) at De Hoop Nature Reserve, South Africa. One measure of the perceived environmental temperature, PET2, is an effective thermal index, since it enters the models for feeding and resting behaviour, and also accounts for levels of allogrooming. Solar radiation intensity is an important factor underlying these relationships, although the wind chill equivalent temperature and humidity enter the models for other behaviours. Future studies should thus be mindful of the impact of each of these elements of the thermal environment. A detailed understanding of primate thermoregulation will only come with the development of biophysical models of the thermal characteristics of the species and its environment. Until such developments, however, the indices presented here should permit a more detailed examination of the thermal environment, allowing thermoregulation to be given greater precedence in future studies of primate behaviour.     

Towards a standardized index of European rabbit abundance in Iberian Mediterranean habitats

European rabbits Oryctolagus cuniculus are a keystone species in Iberian Mediterranean ecosystems. However, the reliability of methods for estimating rabbit abundance, particularly when at low numbers, is not well understood. Further, better standardization of these methodologies would allow abundance estimates to be more reliably compared between areas and periods. Consequently, we compared several frequently used methods of estimating rabbit abundance and assessed their advantages and disadvantages. During the summers of 2008 and 2009, in 11 localities of central-southern Spain we undertook (a) driving transect counts of rabbits, either at dusk or at night, (b) linear transects on foot recording rabbit signs, (c) cleared-plot pellet counts at permanent plots, and (d) standing crop counts, both with and without habitat stratification. Density estimated at night from driving transects using the Distance Sampling method (the reference method against which all other indices were compared) varied from 0 to 2.69 rabbits ha⁻¹. Most pellet-count indices were significantly related to the reference method. In particular, cleared-plot pellet counts in permanent plots corrected for pellet persistence showed the best correlation with the reference method. In contrast, latrine counts were not related to the reference method index, and we recommend against their use. A standard methodology based on cleared-plot pellets counts could be used to monitor rabbit abundance on a large scale.     

DADOS-Prospective: an open source application for Web-based prospective data collection

Background  

Randomized, prospective trials involving multi-institutional collaboration have become a central part of clinical and translational research. However, data management and coordination of multi-center studies is a complex process that involves developing systems for data collection and quality control, tracking data queries and resolutions, as well as developing communication procedures. We describe DADOS-Prospective, an open-source Web-based application for collecting and managing prospective data on human subjects for clinical and translational trials. DADOS-Prospective not only permits users to create new clinical research forms (CRF) and supports electronic signatures, but also offers the advantage of containing, in a single environment, raw research data in downloadable spreadsheet format, source documentation and regulatory files stored in PDF format, and audit trails.     

Biodiversity in agricultural landscapes: the ground beetle communities of woody uncultivated habitats

Agricultural landscapes can be defined as mosaics of landscape elements which are affected by farming practices. Woodland habitats, even though they are managed, are amongst the most stable elements of agricultural landscapes and can play a key role in the maintenance of biodiversity. This study of the ground beetle (carabid) communities of woodlands and woody linear features in a Scottish agricultural landscape shows that these habitats contribute significantly to the overall landscape diversity of these beetles. Communities in woods and hedgerows display the same species diversity and are both characterized by the presence of forest species. The main factors constraining carabid communities in both environments are the grazing intensity and, to a lesser extent, the type of soil. Heavily grazed locations are characterized by the occurrence of grassland species while forest species are restricted to ungrazed locations. At the landscape scale, the distribution of the forest species is limited by spatial isolation, indicating that there are insufficient functional links between woodland habitats in the study area. Isolation could be compensated for either by a better control of grazing so that linear features can be used as dispersal corridors for forest carabids or by planting more linear features and woods in the area.     

MedlineR: an open source library in R for Medline literature data mining

SUMMARY: We describe an open source library written in the R programming language for Medline literature data mining. This MedlineR library includes programs to query Medline through the NCBI PubMed database; to construct the co-occurrence matrix; and to visualize the network topology of query terms. The open source nature of this library allows users to extend it freely in the statistical programming language of R. To demonstrate its utility, we have built an application to analyze term-association by using only 10 lines of code. We provide MedlineR as a library foundation for bioinformaticians and statisticians to build more sophisticated literature data mining applications. AVAILABILITY: The library is available from http://dbsr.duke.edu/pub/MedlineR.     

Estimating acridid densities in grassland habitats: a comparison between presence-absence and abundance sampling designs

Sampling methods to estimate acridid density per surface area unit in grassland habitats were compared using presence-absence data and count data. Sampling plans based on 6 yr of surveys were devised to estimate the density of Chorthippus spp., Euchorthippus spp., and Calliptamus italicus L. These acridids represented >90% of species in the study area. Sampling plans based on count data provided a reasonable tool when densities were >1/m(2) and when the level of precision was 0.20-0.30. A binomial sampling plan can be used to estimate C. italicus density with a level of precision >or=0.28. Sampling characteristics, i.e., estimated mean, actual precision, and sample size, were established on validation data sets with bootstrapping analysis. Sampling costs were also calculated according to density-dependent functions. Comparison between binomial sampling and enumerative sampling of C. italicus showed that binomial sampling required less time than enumerative sampling when densities were 0.35. Plot area had no significant effect on sample variances of counts.     

Estimating red deer abundance in a wide range of management situations in Mediterranean habitats

When human interventions interfere with the natural regulation of wildlife populations by favouring some species, overabundance can emerge. We evaluated different methods of estimating red deer abundance in a wide range of population densities from southern Spain. Distance sampling estimates were used as the reference method across 22 localities and were compared with two kilometric abundance indices (KAIs), four indices based on pellet group counts and two browsing indices (BWIs). The average red deer density estimated by distance sampling was 19.51±3.19 deer per 100 ha, showing a wide range across the study area (0.04–66.77). Distance sampling estimates correlated with the KAIs, pellet group-based index and the BWI. The agreement with distance sampling improved when groups were used instead of individuals in the KAIs, when the minimum pellet group size was fixed at 20 pellets in the dropping counts, and when only highly palatable species were used in the BWI. Thus, several direct and indirect methods can estimate red deer abundance in Mediterranean habitats from Southern Spain with appropriate modifications.     

Semiparametric frailty models for zero‐inflated event count data in the presence of informative dropout

Recurrent events data are commonly encountered in medical studies. In many applications, only the number of events during the follow‐up period rather than the recurrent event times is available. Two important challenges arise in such studies: (a) a substantial portion of subjects may not experience the event, and (b) we may not observe the event count for the entire study period due to informative dropout. To address the first challenge, we assume that underlying population consists of two subpopulations: a subpopulation nonsusceptible to the event of interest and a subpopulation susceptible to the event of interest. In the susceptible subpopulation, the event count is assumed to follow a Poisson distribution given the follow‐up time and the subject‐specific characteristics. We then introduce a frailty to account for informative dropout. The proposed semiparametric frailty models consist of three submodels: (a) a logistic regression model for the probability such that a subject belongs to the nonsusceptible subpopulation; (b) a nonhomogeneous Poisson process model with an unspecified baseline rate function; and (c) a Cox model for the informative dropout time. We develop likelihood‐based estimation and inference procedures. The maximum likelihood estimators are shown to be consistent. Additionally, the proposed estimators of the finite‐dimensional parameters are asymptotically normal and the covariance matrix attains the semiparametric efficiency bound. Simulation studies demonstrate that the proposed methodologies perform well in practical situations. We apply the proposed methods to a clinical trial on patients with myelodysplastic syndromes.     

Autoregressive models for capture-recapture data: a Bayesian approach

In this article, we incorporate an autoregressive time-series framework into models for animal survival using capture-recapture data. Researchers modeling animal survival probabilities as the realization of a random process have typically considered survival to be independent from one time period to the next. This may not be realistic for some populations. Using a Gibbs sampling approach, we can estimate covariate coefficients and autoregressive parameters for survival models. The procedure is illustrated with a waterfowl band recovery dataset for northern pintails (Anas acuta). The analysis shows that the second lag autoregressive coefficient is significantly less than 0, suggesting that there is a triennial relationship between survival probabilities and emphasizing that modeling survival rates as independent random variables may be unrealistic in some cases. Software to implement the methodology is available at no charge on the Internet.     

Inferences about population dynamics from count data using multistate models: a comparison to capture–recapture approaches

Wildlife populations consist of individuals that contribute disproportionately to growth and viability. Understanding a population's spatial and temporal dynamics requires estimates of abundance and demographic rates that account for this heterogeneity. Estimating these quantities can be difficult, requiring years of intensive data collection. Often, this is accomplished through the capture and recapture of individual animals, which is generally only feasible at a limited number of locations. In contrast, N‐mixture models allow for the estimation of abundance, and spatial variation in abundance, from count data alone. We extend recently developed multistate, open population N‐mixture models, which can additionally estimate demographic rates based on an organism's life history characteristics. In our extension, we develop an approach to account for the case where not all individuals can be assigned to a state during sampling. Using only state‐specific count data, we show how our model can be used to estimate local population abundance, as well as density‐dependent recruitment rates and state‐specific survival. We apply our model to a population of black‐throated blue warblers (Setophaga caerulescens) that have been surveyed for 25 years on their breeding grounds at the Hubbard Brook Experimental Forest in New Hampshire, USA. The intensive data collection efforts allow us to compare our estimates to estimates derived from capture–recapture data. Our model performed well in estimating population abundance and density‐dependent rates of annual recruitment/immigration. Estimates of local carrying capacity and per capita recruitment of yearlings were consistent with those published in other studies. However, our model moderately underestimated annual survival probability of yearling and adult females and severely underestimates survival probabilities for both of these male stages. The most accurate and precise estimates will necessarily require some amount of intensive data collection efforts (such as capture–recapture). Integrated population models that combine data from both intensive and extensive sources are likely to be the most efficient approach for estimating demographic rates at large spatial and temporal scales.