Jaxk Reeves

On the meaning and measurement of nestedness of species assemblages

Nestedness of species assemblages occurs when thebiotas of sites with lower numbers of species tend to be subsets of the biotas at richer sites. We develop new quantitative and statistical techniques for measuring, testing, and comparing nestedness, and apply these methods to data from the literature. Significantly nonrandom nestedness was present in all 27 assemblages examined, and tended to be stronger in systems dominated by extinction, such as landbridge islands. Sets of assemblages that were very strongly nested were more likely to have greater species richness on one or a few large sites than on several smaller sites of equivalent total area — that is, to fall toward the “single large” side of the “Single Large Or Several Small” (SLOSS) continuum. Our analysis indicates that nestedness, when quantified as a single number for a presence-absence matrix, measures community-wide differences in incidence (the frequency of occurrence or “distribution” of species). Factors that lead to consistent differences among species in immigration or extinction rates cause strong patterns of nestedness of species assemblages. Nestedness is negatively related to beta diversity: nestedness is low when beta diversity is high, and vice versa. Conservation managers will thus seek to minimize nestedness and the development of nested structure in systems of nature reserves.

A Review and Comparison of Changepoint Detection Techniques for Climate Data

Abstract This review article enumerates, categorizes, and compares many of the methods that have been proposed to detect undocumented changepoints in climate data series. The methods examined include the standard normal homogeneity (SNH) test, Wilcoxon’s nonparametric test, two-phase regression (TPR) procedures, inhomogeneity tests, information criteria procedures, and various variants thereof. All of these methods have been proposed in the climate literature to detect undocumented changepoints, but heretofore there has been little formal comparison of the techniques on either real or simulated climate series. This study seeks to unify the topic, showing clearly the fundamental differences among the assumptions made by each procedure and providing guidelines for which procedures work best in different situations. It is shown that the common trend TPR and Sawa’s Bayes criteria procedures seem optimal for most climate time series, whereas the SNH procedure and its nonparametric variant are probably best whe...

Detection of Undocumented Changepoints: A Revision of the Two-Phase Regression Model

Abstract Changepoints (inhomogeneities) are present in many climatic time series. Changepoints are physically plausible whenever a station location is moved, a recording instrument is changed, a new method of data collection is employed, an observer changes, etc. If the time of the changepoint is known, it is usually a straightforward task to adjust the series for the inhomogeneity. However, an undocumented changepoint time greatly complicates the analysis. This paper examines detection and adjustment of climatic series for undocumented changepoint times, primarily from single site data. The two-phase regression model techniques currently used are demonstrated to be biased toward the conclusion of an excessive number of unobserved changepoint times. A simple and easily applicable revision of this statistical method is introduced.

Heterogeneity in the substitution process of amino acid sites of proteins coded for by mitochondrial DNA.

SummarySeveral forms of maximum likelihood models are applied to aligned amino acid sequence data coded for in the mitochondrial DNA of six species (chicken, frog, human, bovine, mouse, and rat). These models range in form from relatively simple models of the type currently used for inferring phylogenetic tree structure to models more complex than those that have been used previously. No major discrepancies between the optimal trees inferred by any of these methods are found, but there are huge differences in adequacy of fit. A very significant finding is that the fit of any of these models is vastly improved by allowing a certain proportion of the amino acid sites to be invariant. An even more important, although disquieting, finding is that none of these models fits well, as judged by standard statistical criteria. The primary reason for this is that amino acid sites undergo substitution according to a process that is very heterogeneous. Because most phylogenetic inference is accomplished by choosing the optimal tree under the assumption that a homogeneous process is acting on the sites, the potential invalidity of some such conclusions is raised by this articles results. The seriousness of this problem depends upon the robustness of the phylogenetic inferential procedure to departures from the underlying model.

A regression-based approach to scalability prediction

Many applied scientific domains are increasingly relying on large-scale parallel computation. Consequently, many large clusters now have thousands of processors. However, the ideal number of processors to use for these scientific applications varies with both the input variables and the machine under consideration, and predicting this processor count is rarely straightforward. Accurate prediction mechanisms would provide many benefits, including improving cluster efficiency and identifying system configuration or hardware issues that impede performance. We explore novel regression-based approaches to predict parallel program scalability. We use several program executions on a small subset of the processors to predict execution time on larger numbers of processors. We compare three different regression-based techniques: one based on execution time only; another that uses per-processor information only; and a third one based on the global critical path. These techniques provide accurate scaling predictions, with median prediction errors between 6.2% and 17.3% for seven applications.

Potentially inappropriate medication use and healthcare expenditures in the US community-dwelling elderly.

Background:Potentially inappropriate medication (PIM) use is a major source of drug-related problems in the elderly. Few studies have quantified the effect of PIM use on total healthcare expenditures in the United States. Objectives:We sought to determine the relationship between PIM use and healthcare expenditure and to estimate the annual incremental healthcare expenditures related to PIM use in the community-dwelling elderly population in the United States in 2001. Methods:This was a retrospective cohort study. Participants were age 65 years or older who had no PIM use in rounds 1 and 2 of the 2000–2001 Medical Expenditure Panel Survey, a nationally representative survey of the US noninstitutionalized population. On the basis of the 2002 Beers criteria, PIM users were identified as those who had been prescribed at least one PIM during specified time periods in the study. Propensity scores were used to match PIM users and nonusers in the analysis examining differences in total healthcare expenditures. Results:PIM utilization is a significant predictor for higher healthcare expenditures (P < 0.05). A conservative estimate of the incremental healthcare expenditures related to PIM use in the community-dwelling elderly population would be

Changepoint Detection in Periodic and Autocorrelated Time Series

7.2 billion (95% confidence interval,

Bootstrap test of significance and sequential bootstrap estimation for unstable first order autoregressive processes

3.4 billion–

CRISP: Catheterization RISk score for pediatrics: A Report from the Congenital Cardiac Interventional Study Consortium (CCISC)

15.7 billion) in the United States in 2001. Conclusions:PIM use is a major patient safety concern that results in increased healthcare expenditures. This study emphasizes the need for continued provider education to inform prescribers of the potential risks of using certain medications in the elderly and to improve prescribing practices.

An Algorithm to Detect Adverse Drug Reactions in the Neonatal Intensive Care Unit

Abstract Undocumented changepoints (inhomogeneities) are ubiquitous features of climatic time series. Level shifts in time series caused by changepoints confound many inference problems and are very important data features. Tests for undocumented changepoints from models that have independent and identically distributed errors are by now well understood. However, most climate series exhibit serial autocorrelation. Monthly, daily, or hourly series may also have periodic mean structures. This article develops a test for undocumented changepoints for periodic and autocorrelated time series. Classical changepoint tests based on sums of squared errors are modified to take into account series autocorrelations and periodicities. The methods are applied in the analyses of two climate series.