Capture–recapture analysis with a latent class model allowing for local dependence and observed heterogeneity

In epidemiology, capture–recapture models are commonly used to estimate the size of an unknown population based on several incomplete lists of individuals. The method operates under two main assumptions: independence between the lists (local independence) and homogeneity of capture probabilities of individuals. In practice, these assumptions are rarely satisfied. We introduce a multinomial latent class model that can account for both list dependence and heterogeneity. Parameter estimation is performed by maximizing the conditional likelihood function with the use of the EM algorithm. In addition, a new approach for evaluating the standard errors of the parameter estimates is discussed, which considerably reduces the computational burden associated with the evaluation of the variance of the population size estimate.     

Capture-recapture, epidemiology, and list mismatches: two lists

In recent years, capture-recapture methods for closed populations have been extensively applied to epidemiology. For example, suppose we have several incomplete lists of diabetics and we wish to estimate the total number of diabetics by estimating the number missing from all the lists. A major problem is that the information about individuals on the lists may have been given incorrectly or the information may have been typed incorrectly so that some list matches are missed. Using the concept of tag loss borrowed from animal population studies, we consider methods for estimating both the probabilities of making list errors and the population size for just two independent lists. The effect of heterogeneity on the errors is examined. The methods are applied to a large data set of diabetic persons consisting of a list obtained from a survey and a list obtained from doctors' records. It was found that the error rates were high and that ignoring the errors led to a gross overestimate of the total number of diabetic persons.     

Capture-recapture, epidemiology, and list mismatches: several lists

In applying capture-recapture methods for closed populations to epidemiology, e.g., in the estimation of the size of a diabetes population, one comes up against the problem of list errors due to mistyping or misinformation. This problem has been studied for just two lists by Seber, Huakau, and Simmons (2000, Biometrics 56, 1227 1232) using the concept of tag loss borrowed from animal population studies. In this article, we discuss a similar method that can be extended to an arbitrary number of lists. The methods are applied to an example.     

Using simple species lists to monitor trends in animal populations: new methods and a comparison with independent data

There is an urgent need to develop simple and inexpensive methods for monitoring wildlife populations in resource-poor countries. List-based methods have been advocated as simple yet potentially useful biodiversity monitoring tools, and systems have recently been launched in a number of countries to collect species lists. We attempt to advance the use of systematic list-based monitoring by (1) suggesting improvements to the way in which list reporting rates are calculated; (2) assessing the extent to which degrading effort-corrected measures of abundance into simple species lists results in loss of information on population trends; (3) comparing long-term trends in list reporting rates with population trends from a wholly independent monitoring scheme. Daily species lists of birds were derived from regular trapping at a nature reserve in southern England. Most species showed a strong correlation across years between the proportion of lists on which they occurred, adjusted for list length (adjusted list reporting rate; ALRR), and an effort-corrected measure of abundance (captures per unit effort; CPUE). ALRR revealed almost as much about annual variation in abundance as CPUE for all but the most frequently captured species. Long-term (>20 years) trends in ALRRs at the nature reserve were positively correlated with UK national population trends recorded over the same period by an independent, labour-intensive monitoring scheme that counted birds at a large number of widely spread sites. Our results support previous claims that simple species lists could generate data useful for monitoring long-term population trends, particularly where such lists are collected systematically. However, further research on the efficiency of list reporting rates relative to more sophisticated methods is necessary, before list-based methods can be advocated for dedicated monitoring schemes in resource-poor regions.     

Analyzing protein lists with large networks: edge-count probabilities in random graphs with given expected degrees.

We present an analytical framework to analyze lists of proteins with large undirected graphs representing their known functional relationships. We consider edge-count variables such as the number of interactions between a protein and a list, the size of a subgraph induced by a list, and the number of interactions bridging two lists. We derive approximate analytical expressions for the probability distributions of these variables in a model of a random graph with given expected degrees. Probabilities obtained with the analytical expressions are used to mine a protein interaction network for functional modules, characterize the connectedness of protein functional categories, and measure the strength of relations between modules.     

Multi-list methods using incomplete lists in closed populations

Multi-list methods have become a common application of capture-recapture methodology to estimate the size of human populations, and have been successfully applied to estimating prevalence of diabetes, human immunodeficiency virus (HIV), and drug abuse. A key assumption in multi-list methods is that individuals have a unique "tag" that allows them to be matched across all lists. This article develops multi-list methodology that relaxes the assumption of a single tag common to all lists. Estimates are found using estimating functions. An example illustrates its application for estimating the prevalence of diabetes, and a simulation study investigates conditions under which the methodology is robust to different list and population sizes.     

A Multilevel Model for Continuous Time Population Estimation

Summary .  Statistical methods have been developed and applied to estimating populations that are difficult or too costly to enumerate. Known as multilist methods in epidemiological settings, individuals are matched across lists and estimation of population size proceeds by modeling counts in incomplete multidimensional contingency tables (based on patterns of presence/absence on lists). As multilist methods typically assume that lists are compiled instantaneously, there are few options available for estimating the unknown size of a closed population based on continuously (longitudinally) compiled lists. However, in epidemiological settings, continuous time lists are a routine byproduct of administrative functions. Existing methods are based on time-to-event analyses with a second step of estimating population size. We propose an alternative approach to address the twofold epidemiological problem of estimating population size and of identifying patient factors related to duration (in days) between visits to a health care facility. A Bayesian framework is proposed to model interval lengths because, for many patients, the data are sparse; many patients were observed only once or twice. The proposed method is applied to the motivating data to illustrate the methods' applicability. Then, a small simulation study explores the performance of the estimator under a variety of conditions. Finally, a small discussion section suggests opportunities for continued methodological development for continuous time population estimation.     

Algebraic comparison of partial lists in bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or to a meta-analysis comparison, it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained, instead of just one list. Here we introduce a method, based on permutations, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated by finding and comparing gene profiles on a large prostate cancer dataset, consisting of two cohorts of patients from different countries, for a total of 455 samples.     

A comparative analysis of global, national, and state red lists for threatened amphibians in Brazil

The knowledge about the conservation status of species is an important data for conservation biology. Therefore, threatened species lists are a powerful tool for conservation planning and prioritization. Our objective is to compare the global, the national and state red lists of amphibians in Brazil. Threatened species were categorized according to their listing in one or several of these lists. We analyzed for true inconsistencies across lists in order to evaluate practical consequences of such incongruences on amphibian conservation in Brazil. We recorded a total of 61 threatened amphibian species in Brazil (across all red lists). Only one species, Phrynomedusa fimbriata, was listed as Extinct (both in IUCN, Brazil and S?o Paulo lists). A total of eleven endemic species are listed as threatened by the global red list, but do not appear in Brazil’s national red list, which represent an inconsistence among these lists. Besides that, the threat category of Thoropa lutzi and Thoropa petropolitana, two endemic species, differ among both lists, which also represents a problem between both lists. These mismatches may be due to several reasons such as different interpretation of the criteria; different methodologies used; different data availability on species; differences in the dates of assessments processes; the assessors’ attitudes to uncertainty; outdated red lists. Harmonization among red lists permits a better picture of threatened amphibian diversity across scales and to develop global, national and state plans to complement conservation actions in order to maximize the chance of success of these initiatives.     

Detecting and accounting for multiple sources of positional variance in peak list registration analysis and spin system grouping

Peak lists derived from nuclear magnetic resonance (NMR) spectra are commonly used as input data for a variety of computer assisted and automated analyses. These include automated protein resonance assignment and protein structure calculation software tools. Prior to these analyses, peak lists must be aligned to each other and sets of related peaks must be grouped based on common chemical shift dimensions. Even when programs can perform peak grouping, they require the user to provide uniform match tolerances or use default values. However, peak grouping is further complicated by multiple sources of variance in peak position limiting the effectiveness of grouping methods that utilize uniform match tolerances. In addition, no method currently exists for deriving peak positional variances from single peak lists for grouping peaks into spin systems, i.e. spin system grouping within a single peak list. Therefore, we developed a complementary pair of peak list registration analysis and spin system grouping algorithms designed to overcome these limitations. We have implemented these algorithms into an approach that can identify multiple dimension-specific positional variances that exist in a single peak list and group peaks from a single peak list into spin systems. The resulting software tools generate a variety of useful statistics on both a single peak list and pairwise peak list alignment, especially for quality assessment of peak list datasets. We used a range of low and high quality experimental solution NMR and solid-state NMR peak lists to assess performance of our registration analysis and grouping algorithms. Analyses show that an algorithm using a single iteration and uniform match tolerances approach is only able to recover from 50 to 80% of the spin systems due to the presence of multiple sources of variance. Our algorithm recovers additional spin systems by reevaluating match tolerances in multiple iterations. To facilitate evaluation of the algorithms, we developed a peak list simulator within our nmrstarlib package that generates user-defined assigned peak lists from a given BMRB entry or database of entries. In addition, over 100,000 simulated peak lists with one or two sources of variance were generated to evaluate the performance and robustness of these new registration analysis and peak grouping algorithms.     

Extinction risk assessments at the population and species level: implications for amphibian conservation

Amphibian populations are declining worldwide and this is causing growing concern. High levels of population declines followed by the expansion of red lists are creating demands for effective strategies to maximize conservation efforts for amphibians. Ideally, integrated and comprehensive strategies should be based on complementary information of population and species extinction risk. Here we evaluate the congruence between amphibian extinction risk assessments at the population level (Declining Amphibian Database––DAPTF) and at species level (GAA––IUCN Red List). We used the Declining Amphibian Database––DAPTF that covers 967 time-series records of amphibian population declines assigned into four levels of declines. We assigned each of its corresponding species into GAA––IUCN red list status, discriminated each species developmental mode, and obtained their geographic range size as well. Extinction risk assessments at the population and species level do not fully coincide across geographic realms or countries. In Paleartic, Neartic and Indo-Malayan realms less than 25% of species with reported population declines are formally classified as threatened. In contrast, more than 60% of all species with reported population declines that occur in Australasia and the Neotropics are indeed threatened according to the GAA––IUCN Red List. Species with aquatic development presented proportionally higher extinction risks at both population and species level than those with terrestrial development, being this pattern more prominent at Australasia, Paleartic, and Neartic realms. Central American countries, Venezuela, Mexico and Australia presented the highest congruence between both population and species risk. We address that amphibian conservation strategies could be improved by using complementary information on time-series population trends and species threat. Whenever feasible, conservation assessments should also include life-history traits in order to improve its effectiveness.     

Integrating batch marks and radio tags to estimate the size of a closed population with a movement model

Movement models require individually identifiable marks to estimate the movement rates among strata. But they are relatively expensive to apply and monitor. Batch marks can be readily applied, but individual animal movements cannot be identified. We describe a method to estimate population size in a stratified population when movement takes place among strata and animals are marked with a combination of batch and individually identifiable tags. A hierarchical model with Bayesian inference is developed that pools information across segments on the detection efficiency based on radio‐tagged fish and also uses the movement of the radio‐tagged fish to impute the movement of the batch‐marked fish to provide estimates of the population size on a segment and river level. The batch marks provide important information to help estimate the movement rates, but contribute little to the overall estimate of the population size. In this case, the approximate equal catchability among strata in either sample obviates the need for stratification.     

Variance of Stratified Survey Estimators With Probability of Detection Adjustments

Abstract: Estimates of wildlife population sizes are frequently constructed by combining counts of observed animals from a stratified survey of aerial sampling units with an estimated probability of detecting animals. Unlike traditional stratified survey designs, stratum-specific estimates of population size will be correlated if a common detection model is used to adjust counts for undetected animals in all strata. We illustrate this concept in the context of aerial surveys, considering 2 cases: 1) a single-detection parameter is estimated under the assumption of constant detection probabilities, and 2) a logistic-regression model is used to estimate heterogeneous detection probabilities. Naïve estimates of variance formed by summing stratum-specific estimates of variance may result in significant bias, particularly if there are a large number of strata, if detection probabilities are small, or if estimates of detection probabilities are imprecise. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):837–844; 2008)     

Peakmatch: a simple and robust method for peak list matching

Peak lists are commonly used in NMR as input data for various software tools such as automatic assignment and structure calculation programs. Inconsistencies of chemical shift referencing among different peak lists or between peak and chemical shift lists can cause severe problems during peak assignment. Here we present a simple and robust tool to achieve self-consistency of the chemical shift referencing among a set of peak lists. The Peakmatch algorithm matches a set of peak lists to a specified reference peak list, neither of which have to be assigned. The chemical shift referencing offset between two peak lists is determined by optimizing an assignment-free match score function using either a complete grid search or downhill simplex optimization. It is shown that peak lists from many different types of spectra can be matched reliably as long as they contain at least two corresponding dimensions. Using a simulated peak list, the Peakmatch algorithm can also be used to obtain the optimal agreement between a chemical shift list and experimental peak lists. Combining these features makes Peakmatch a useful tool that can be applied routinely before automatic assignment or structure calculation in order to obtain an optimized input data set.     

Monitoring bald eagles using lists of nests: Response to Watts and Duerr

The post-delisting monitoring plan for bald eagles (Haliaeetus leucocephalus) roposed use of a dual-frame sample design, in which sampling of known nest sites in combination with additional area-based sampling is used to estimate total number of nesting bald eagle pairs. Watts and Duerr (2010) used data from repeated observations of bald eagle nests in Virginia, USA to estimate a nest turnover rate and used this rate to simulate decline in number of occupied nests in list nests over time. Results of Watts and Duerr suggest that, given the rates of loss of nests from the list of known nest sites in Virginia, the list information will be of little value to sampling unless lists are constantly updated. Those authors criticize the plan for not placing sufficient emphasis on updating and maintaining lists of bald eagle nests. Watts and Duerr's metric of turnover rate does not distinguish detectability or temporary nonuse of nests from permanent loss of nests and likely overestimates turnover rate. We describe a multi-state capture–recapture model that allows appropriate estimation of rates of loss of nests, and we use the model to estimate rates of loss from a sample of nests from Maine, USA. The post-delisting monitoring plan addresses the need to maintain and update the lists of nests, and we show that dual frame sampling is an effective approach for sampling nesting bald eagle populations. © 2011 The Wildlife Society.     

Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information

MOTIVATION: The analysis of genome-scale data from different high throughput techniques can be used to obtain lists of genes ordered according to their different behaviours under distinct experimental conditions corresponding to different phenotypes (e.g. differential gene expression between diseased samples and controls, different response to a drug, etc.). The order in which the genes appear in the list is a consequence of the biological roles that the genes play within the cell, which account, at molecular scale, for the macroscopic differences observed between the phenotypes studied. Typically, two steps are followed for understanding the biological processes that differentiate phenotypes at molecular level: first, genes with significant differential expression are selected on the basis of their experimental values and subsequently, the functional properties of these genes are analysed. Instead, we present a simple procedure which combines experimental measurements with available biological information in a way that genes are simultaneously tested in groups related by common functional properties. The method proposed constitutes a very sensitive tool for selecting genes with significant differential behaviour in the experimental conditions tested. RESULTS: We propose the use of a method to scan ordered lists of genes. The method allows the understanding of the biological processes operating at molecular level behind the macroscopic experiment from which the list was generated. This procedure can be useful in situations where it is not possible to obtain statistically significant differences based on the experimental measurements (e.g. low prevalence diseases, etc.). Two examples demonstrate its application in two microarray experiments and the type of information that can be extracted.     

Quantifying trait selection driving community assembly: a test in herbaceous plant communities under contrasted land use regimes

Plant traits are particularly important in determining plant community structure. However, how can one identify which traits are the most important in driving community assembly? Here we propose a method 1) to quantify the direction and strength of trait selection during community assembly and 2) to obtain parsimonious lists of traits that can predict species relative abundances in plant communities. We tested our method using floristic data from 32 plots experiencing different treatments (fertilisation and grazing) in southern France. Twelve functional traits were measured on 68 species. We determined the direction and strength of selection on these 12 traits using a metric derived from a maximum entropy model (i.e. lambda). We then determined our parsimonious list of traits using a backward selection of traits based on these lambda values (for all treatments and in each treatment separately). We finally compared our method to two other methods: one based on iterative RLQ and the other based on an entropy‐based forward selection of traits. We found major differences in the direction and strength of selection across the 12 traits and treatments. From the 12 traits, plant vegetative and reproductive heights, leaf dry matter content leaf nitrogen content, specific leaf area, and leaf phosphorus content were particularly important for predicting species relative abundances when considering all treatments together. Our method yielded results similar to those produced by the entropy‐based approach but differed from those produced by the iterative RLQ, whose selected traits could not significantly predict species relative abundances. Together these results suggest that the assembly of these communities is primarily driven by a small number of key functional traits. We argue that our method provides an objective way of determining a parsimonious list of traits that together accurately predict community structure and which, despite its complementarities with entropy‐based method, offers significant advantages.     

Molecular Pathway Reconstruction and Analysis of Disturbed Gene Expression in Depressed Individuals Who Died by Suicide

Molecular mechanisms behind the etiology and pathophysiology of major depressive disorder and suicide remain largely unknown. Recent molecular studies of expression of serotonin, GABA and CRH receptors in various brain regions have demonstrated that molecular factors may contribute to the development of depressive disorder and suicide behaviour. Here, we used microarray analysis to examine the expression of genes in brain tissue (frontopolar cortex) of individuals who had been diagnosed with major depressive disorder and died by suicide, and those who had died suddenly without a history of depression. We analyzed the list of differentially expressed genes using pathway analysis, which is an assumption-free approach to analyze microarray data. Our analysis revealed that the differentially expressed genes formed functional networks that were implicated in cell to cell signaling related to synapse maturation, neuronal growth and neuronal complexity. We further validated these data by randomly choosing (100 times) similarly sized gene lists and subjecting these lists to the same analyses. Random gene lists did not provide highly connected gene networks like those generated by the differentially expressed list derived from our samples. We also found through correlational analysis that the gene expression of control participants was more highly coordinated than in the MDD/suicide group. These data suggest that among depressed individuals who died by suicide, wide ranging perturbations of gene expression exist that are critical for normal synaptic connectively, morphology and cell to cell communication.