Showing papers by "Anne Chao published in 2015"

Estimating diversity and entropy profiles via discovery rates of new species

Abstract: Summary The compositional complexity of an assemblage is not expressible as a single number; standard measures such as diversities (Hill numbers) and entropies (Renyi entropies and Tsallis entropies) vary in their order q which determines the measures' emphasis on rare or common species. Ranking and comparing assemblages depend on the choice of q. Rather than selecting one or a few measures to describe an assemblage, it is preferable to convey the complete story by presenting a continuous profile, a plot of diversity or entropy as a function of q ≥ 0. This makes it easy to visually compare the compositional complexities of multiple assemblages and to judge the evenness of the relative abundance distributions of the assemblages. In practice, the profile is plotted for all values of q from 0 to q = 3 or 4 (beyond which it generally changes little). These profiles are usually generated by substituting species sample proportions into the complexity measures. However, this empirical approach typically underestimates the true profile for low values of q, because samples usually miss some of the assemblage's species due to under-sampling. Although bias-reduction methods exist for individual measures of order q = 0, 1 and 2, there has been no analytic method that unifies these bias-corrected estimates into a continuous profile. For incomplete sampling data, this work proposes a novel analytic method to obtain accurate, continuous, low-bias diversity and entropy profiles with focus on low orders of q (0 ≤ q ≤ 3). Our approach is based on reformulating the diversity and entropy of any order q in terms of the successive discovery rates of new species with respect to sample size, that is the successive slopes of the species accumulation curve. A bootstrap method is applied to obtain approximate variances of our proposed profiles and to construct the associated confidence intervals. Extensive simulations from theoretical models and real surveys show that the proposed profiles greatly reduce under-sampling bias and have substantially lower bias and mean-squared error than the empirical profile, especially for q ≤ 1. Our method is also extended to deal with incidence data.

Rarefaction and extrapolation of phylogenetic diversity

Abstract: Summary 1 Traditional species diversity measures do not make distinctions among species Faith’s phylogenetic diversity (PD), which is defined as the sum of the branch lengths of a phylogenetic tree connecting all species, takes into account phylogenetic differences among species and has found many applications in various research fields In this paper, we extend Faith’s PD to represent the total length of a phylogenetic tree from any fixed point on its main trunk 2 Like species richness, Faith’s PD tends to be an increasing function o f sampling effort and thus tends to increase with sample completeness We develop in this paper the ‘PD accumulation curve’ (an extension of the species accumulation curve) to depict how PD increases with sampling size and sample completeness 3 To make fair comparisons of Faith’s PD among several assemblages based on sampling data from each assemblage, we derive both theoretical formulae and analytic estimators for seamless rarefaction (interpolation) and extrapolation (prediction) We develop a lower bound of the undetected PD for an incomplete sample to guide the extrapolation; the PD estimator for an extrapolated sample is generally reliable up to twice the size of the empirical sample 4 We propose an integrated curve that smoothly links rarefaction and extrapolation to standardize samples on the basis of sample size or sample completeness A bootstrap method is used to obtain the unconditional variances ofPD estimators and to construct the confidence interval of the expected PD for a fixed sample size or fixed degree of sample completeness This facilitates comparison of multiple assemblages of both rarefied and extrapolated samples 5 We illustrate our formulae and estimators using empirical data sets from Australian birds in two sites We discuss the extension of our approach to the case of multiple incidence data and to incorporate species abundances

Unveiling the species-rank abundance distribution by generalizing the Good-Turing sample coverage theory

Abstract: Based on a sample of individuals, we focus on inferring the vector of species relative abundance of an entire assemblage and propose a novel estimator of the complete species-rank abundance distribution (RAD). Nearly all previous estimators of the RAD use the conventional "plug-in" estimator Pi (sample relative abundance) of the true relative abundance pi of species i. Because most biodiversity samples are incomplete, the plug-in estimators are applied only to the subset of species that are detected in the sample. Using the concept of sample coverage and its generalization, we propose a new statistical framework to estimate the complete RAD by separately adjusting the sample relative abundances for the set of species detected in the sample and estimating the relative abundances for the set of species undetected in the sample but inferred to be present in the assemblage. We first show that P, is a positively biased estimator of pi for species detected in the sample, and that the degree of bias increases with increasing relative rarity of each species. We next derive a method to adjust the sample relative abundance to reduce the positive bias inherent in j. The adjustment method provides a nonparametric resolution to the longstanding challenge of characterizing the relationship between the true relative abundance in the entire assemblage and the observed relative abundance in a sample. Finally, we propose a method to estimate the true relative abundances of the undetected species based on a lower bound of the number of undetected species. We then combine the adjusted RAD for the detected species and the estimated RAD for the undetected species to obtain the complete RAD estimator. Simulation results show that the proposed RAD curve can unveil the true RAD and is more accurate than the empirical RAD. We also extend our method to incidence data. Our formulas and estimators are illustrated using empirical data sets from surveys of forest spiders (for abundance data) and soil ciliates (for incidence data). The proposed RAD estimator is also applicable to estimating various diversity measures and should be widely useful to analyses of biodiversity and community structure.

Expected Shannon Entropy and Shannon Differentiation between Subpopulations for Neutral Genes under the Finite Island Model

Abstract: Shannon entropy H and related measures are increasingly used in molecular ecology and population genetics because (1) unlike measures based on heterozygosity or allele number, these measures weigh alleles in proportion to their population fraction, thus capturing a previously-ignored aspect of allele frequency distributions that may be important in many applications; (2) these measures connect directly to the rich predictive mathematics of information theory; (3) Shannon entropy is completely additive and has an explicitly hierarchical nature; and (4) Shannon entropy-based differentiation measures obey strong monotonicity properties that heterozygosity-based measures lack. We derive simple new expressions for the expected values of the Shannon entropy of the equilibrium allele distribution at a neutral locus in a single isolated population under two models of mutation: the infinite allele model and the stepwise mutation model. Surprisingly, this complex stochastic system for each model has an entropy expressable as a simple combination of well-known mathematical functions. Moreover, entropy- and heterozygosity-based measures for each model are linked by simple relationships that are shown by simulations to be approximately valid even far from equilibrium. We also identify a bridge between the two models of mutation. We apply our approach to subdivided populations which follow the finite island model, obtaining the Shannon entropy of the equilibrium allele distributions of the subpopulations and of the total population. We also derive the expected mutual information and normalized mutual information (“Shannon differentiation”) between subpopulations at equilibrium, and identify the model parameters that determine them. We apply our measures to data from the common starling (Sturnus vulgaris) in Australia. Our measures provide a test for neutrality that is robust to violations of equilibrium assumptions, as verified on real world data from starlings.

Highlighting Indication of extracorporeal membrane oxygenation in endocrine emergencies

Abstract: Extracorporeal membrane oxygenation (ECMO) has been repeatedly used to rescue patients with cardiopulmonary arrest. However, its clinical utility in endocrine emergencies remains unclear. Herein, we describe a case series of 12 patients presenting with refractory shock secondary to endocrine emergencies who were rescued by ECMO support. Patients were identified between 2005 and 2012 from our ECMO registry. The diagnostic distribution was as follows: pheochromocytoma crisis (n = 4), thyroid storm (n = 5), and diabetic ketoacidosis (n = 3). The initial presentation of pheochromocytoma crisis was indistinguishable from acute myocardial infarction (AMI) and frequently accompanied by paroxysmal hypertension and limb ischemia. Thyroid storm was characterized by hyperbilirubinemia and severe gastrointestinal bleeding, whereas neurological symptoms were common in diabetic ketoacidosis. The clinical outcomes of patients with endocrine emergencies were compared with those of 80 cases with AMI who received ECMO because of cardiogenic shock. The cardiac function and the general conditions showed a significantly faster recovery in patients with endocrine emergencies than in those with AMI. We conclude that ECMO support can be clinically useful in endocrine emergencies. The screening of endocrine diseases should be considered during the resuscitation of patients with refractory circulatory shock.

Capture-Recapture for Human Populations

Abstract: Capture–recapture methods, originally developed for estimating demographic parameters of animal populations, have been applied to human populations. In epidemiology and health sciences, most surveillance studies and prevalence surveys based on multiple records of incomplete lists are likely to miss some cases, and thus the number of ascertained cases in the final merged list tends to undercount the true size of the target population. Capture–recapture methods can be applied for these types of studies/surveys to provide useful estimators for the size of the target population and adjust for underascertainment. This article describes the history of the method and focuses on population size estimation based on the sample-coverage approach, which also provides measures to quantify the extent of overlapping information and assess the dependences among samples. The R package CARE1 (CApture–REcapture, part 1) is applied to two real examples for illustration. Other models are briefly discussed. Keywords: ascertainment data; dual-record system; epidemiology; heterogeneity; local dependence; multiple-record systems; overlapping information; population size; sample coverage

The effect of endotoxin adsorber haemoperfusion on microcirculation in septic pigs

Abstract: Microcirculatory dysfunction plays an important role in sepsis-related multiple organ dysfunction.(1) Several studies has shown polymyxin B hemoperfusion has favorable effects on mean arterial pressure, vasopressor use, and mortality.(2) One rat sepsis study had found that microcirculation was better maintained in the polymyxin B hemoperfusion group.(3) However, the effects of polymyxin B hemoperfusion on the microcirculation of the intestinal mucosa, intestinal muscular-serosal layer, kidney, and liver were unknown. We used a fecal peritonitis-induced septic pig model to investigate the effect of polymyxin B perfusion on the microcirculation.