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Modellers of chemical processes with knowledge about plausible parameter values use Bayesian parameter estimation methods to account for their prior beliefs. Some modellers specify prior distributions with finite parameter ranges, such as uniform distributions and truncated normal distributions, because they better account for knowledge about realistic parameter ranges than normal prior distributions with parameter values ranging between −∞ and +∞. We derive closed‐form objective functions for Bayesian parameter estimation with truncated normal priors and uniform priors, for the first time, so that parameter estimation can be performed by solving simple optimization problems rather than using complex sampling‐based techniques. A parametric bootstrapping method that considers truncated normal priors and model nonlinearity is proposed to determine 95% confidence intervals and joint confidence regions. A pharmaceutical case study is used to show the effectiveness of the proposed objective functions and bootstrapping methodology. Confidence regions from bootstrapping are similar to linearization‐based confidence regions that do not account for truncation when truncated areas in normal prior distributions are relatively small. More truncation, which corresponds to more‐precise prior knowledge about the parameters, results in smaller joint confidence regions. The proposed methods will be attractive for parameter estimation in complex process models because they can be less computationally intensive than Markov chain Monte Carlo methods that provide similar results. [ABSTRACT FROM AUTHOR]

Gamma distribution is widely used in applied fields due to its flexibility of accommodating right-skewed data. This article explores statistical methods for constructing confidence intervals for both the difference and ratio of two gamma means. We propose several methods based on the concepts of generalized inference, variance estimates recovery (MOVER) and parametric bootstrapping. The performances of proposed methods are evaluated and compared via a comprehensive simulation study. Simulation results show that the hybrid method that combines MOVER with parametric bootstrapping can provide confidence intervals with reasonable coverage probabilities and interval lengths even for parameter settings with small shape parameters. Finally, two real data examples from environmental and engineering studies are analyzed using the proposed methods. [ABSTRACT FROM AUTHOR]

In this work, a methodology to monitor a shift in the quantile of a distribution that is a member of the log‐symmetric family is proposed. Because the sampling distribution of a quantile estimator is often not available, the parametric bootstrap method is used to determine this sampling distribution and to establish the control limits when the process measurements follow a log‐symmetric distribution. The mentioned family is helpful for describing the behavior of data following a distribution with positive support and that is skewed to the right. Monte Carlo simulations are carried out to investigate the performance of the proposed bootstrap control charts for quantiles. An application regarding failure data due to stress on carbon fibers is presented for illustration when monitoring reliability data. This illustration shows that non‐conventional models, other than the Birnbaum‐Saunders, log‐normal and Weibull distributions, have potential to be used in practice. Two model selection procedures are considered to assess adequacy to the data. To facilitate the public use of the proposed methodology, we have created an R package named chartslogsym whose main functions are detailed in this paper. [ABSTRACT FROM AUTHOR]

The railroad industry has historically used the 2-Parameter Weibull equation to determine the rate of rail fatigue defect occurrences and to forecast the fatigue life of railroad rail. However, the 2-Parameter Weibull equation has significant limitations to include inability to analyze segments of track with limited number of rail defects. These limitations are addressed through modification of the traditional 2-Parameter Weibull equation with a novel approach developed from Parametric Bootstrapping. The result is a Parametric Bootstrapping modified Weibull (PBW) forecasting approach. This methodology is applied to rail segments with insufficient numbers of defects to allow for appropriate defect forecasting analysis. Thus, the PBW method provides reasonable estimates of the rate of defects for track segments that have little or no prior defect history. This approach allows for more track to be analyzed and forecasts the probability of rail defect occurrence as a function of key parameters such as cumulative traffic over the rail. A validation of the proposed methodology was performed. Comparison of the output results of over 300,000 track segments with over 200,000 rail defects showed a major improvement in percentage of segments with reasonable Weibull parameters (alpha and beta). This percentage increased from 11% of segments using traditional Weibull analysis to 77% of segments using Parametric Bootstrap modified Weibull approach. These results show that the PBW Analysis approach introduced here offers a more accurate and effective approach to determining the probability of developing future rail defects. This provides a benefit to railroads in planning maintenance of their expensive rail assets. [ABSTRACT FROM AUTHOR]

Background: Ensemble modeling aims to boost the forecasting performance by systematically integrating the predictive accuracy across individual models. Here we introduce a simple-yet-powerful ensemble methodology for forecasting the trajectory of dynamic growth processes that are defined by a system of non-linear differential equations with applications to infectious disease spread.Methods: We propose and assess the performance of two ensemble modeling schemes with different parametric bootstrapping procedures for trajectory forecasting and uncertainty quantification. Specifically, we conduct sequential probabilistic forecasts to evaluate their forecasting performance using simple dynamical growth models with good track records including the Richards model, the generalized-logistic growth model, and the Gompertz model. We first test and verify the functionality of the method using simulated data from phenomenological models and a mechanistic transmission model. Next, the performance of the method is demonstrated using a diversity of epidemic datasets including scenario outbreak data of the Ebola Forecasting Challenge and real-world epidemic data outbreaks of including influenza, plague, Zika, and COVID-19.Results: We found that the ensemble method that randomly selects a model from the set of individual models for each time point of the trajectory of the epidemic frequently outcompeted the individual models as well as an alternative ensemble method based on the weighted combination of the individual models and yields broader and more realistic uncertainty bounds for the trajectory envelope, achieving not only better coverage rate of the 95% prediction interval but also improved mean interval scores across a diversity of epidemic datasets.Conclusion: Our new methodology for ensemble forecasting outcompete component models and an alternative ensemble model that differ in how the variance is evaluated for the generation of the prediction intervals of the forecasts. [ABSTRACT FROM AUTHOR]

The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R2 of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R2 and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR2, an R package that quantifies part R2 for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called ‘inclusive’ R2, as the square of the structure coefficients times total R2. Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.

The coefficient of determination R² quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R² of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R² and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR², an R package that quantifies part R² for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR² also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called 'inclusive' R², as the square of the structure coefficients times total R². Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR² implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR² with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors. [ABSTRACT FROM AUTHOR]

A recent study by Fok et al. (Oxf Bull Econ Stat 77:872–896, 2015) concludes that there is a low-pay no-pay cycle for males and females in the Australian labour market. This note re-estimates the model of that study using the same data. It is found that Fok et al. (2015) conclusion is based on a model specification that assumes zero correlation of unobserved heterogeneity between the different labour force states modelled. The results of this note show that when the zero correlation restriction is relaxed, there is no evidence of a low-pay no-pay cycle for either males or females. It is also found that the marginal effect estimates used in Fok et al. (2015) to draw the low-pay no-pay cycle conclusion for males and females have been estimated imprecisely. Furthermore, contrary to what Fok et al. (2015) have concluded, the results of this note show that there is no evidence on heterogeneity in the low-pay no-pay cycle across the demographic subgroups examined by Fok et al. (2015). [ABSTRACT FROM AUTHOR]

Post-stratified model-assisted (MA) and hybrid (HY) estimators are used with repeated airborne laser scanning (ALS) strip sampling and national forest inventory field data for stratum-wise and overall estimation of aboveground biomass (AGB) stock and change. The study area covered the southern portion of the Hedmark County in Norway. Both MA and HY estimation substantially reduced the uncertainty in AGB change when compared with estimation using the field survey only. Relative efficiencies (relative variance) of 4.15 (MA) and 3.36 (HY) for overall estimates were found. The results suggest the MA estimator for single-time estimation and the HY as more appropriate for change estimation by cover class. With the HY estimator, a nested post-stratification scheme is demonstrated, combining cover classes with change classes, which enables detailed reporting for change according to cause within each cover class, and has the potential to improve the estimation precision. Finally, parametric bootstrapping is demonstrated as an empirical alternative to estimate the model-error component in the HY estimator. The model error estimated with parametric bootstrapping converged to the analytically determined value of the HY estimator within 1000 bootstrap samples. [ABSTRACT FROM AUTHOR]

Purpose ? Quality function deployment (QFD) is a planning methodology to improve products, services and their associated processes by ensuring that the voice of the customer has been effectively deployed through specified and prioritised technical attributes (TAs). The purpose of this paper is two ways: to enhance the prioritisation of TAs: computer simulation significance test; and computer simulation confidence interval. Both are based on permutation sampling, bootstrap sampling and parametric bootstrap sampling of given empirical data. Design/methodology/approach ? The authors present a theoretical case for the use permutation sampling, bootstrap sampling and parametric bootstrap sampling. Using a published case study the authors demonstrate how these can be applied on given empirical data to generate a theoretical population. From this the authors describe a procedure to decide upon which TAs have significantly different priority, and also estimate confidence intervals from the theoretical simulated populations. Findings ? First, the authors demonstrate not only parametric bootstrap is useful to simulate theoretical populations. The authors can also employ permutation sampling and bootstrap sampling to generate theoretical populations. Then the authors obtain the results from these three approaches. qThe authors describe why there is a difference in results of permutation sampling, bootstrap and parametric bootstrap sampling. Practitioners can employ any approach, it depends how much variation in FWs is required by quality assurance division. Originality/value ? Using these methods provides QFD practitioners with a robust and reliable method for determining which TAs should be selected for attention in product and service design. The explicit selection of TAs will help to achieve maximum customer satisfaction, and save time and money, which are the ultimate objectives of QFD. [ABSTRACT FROM AUTHOR]

ABSTRACT Natural resource managers are developing assessments of selenium (Se) contamination in freshwater ecosystems based on fish tissue concentrations. We evaluated the effects of sample size (i.e., number of fish per site) on the probability of correctly detecting mean whole-body Se values above a range of potential management thresholds. We modeled Se concentrations as gamma distributions with shape and scale parameters fitting an empirical mean-to-variance relationship in data from southwestern West Virginia, USA (63 collections, 382 individuals). We used parametric bootstrapping techniques to calculate statistical power as the probability of detecting true mean concentrations up to 3 mg Se/kg above management thresholds ranging from 4 to 8 mg Se/kg. Sample sizes required to achieve 80% power varied as a function of management thresholds and Type I error tolerance (α). Higher thresholds required more samples than lower thresholds because populations were more heterogeneous at higher mean Se levels. For instance, to assess a management threshold of 4 mg Se/kg, a sample of eight fish could detect an increase of approximately 1 mg Se/kg with 80% power (given α = 0.05), but this sample size would be unable to detect such an increase from a management threshold of 8 mg Se/kg with more than a coin-flip probability. Increasing α decreased sample size requirements to detect above-threshold mean Se concentrations with 80% power. For instance, at an α-level of 0.05, an 8-fish sample could detect an increase of approximately 2 units above a threshold of 8 mg Se/kg with 80% power, but when α was relaxed to 0.2, this sample size was more sensitive to increasing mean Se concentrations, allowing detection of an increase of approximately 1.2 units with equivalent power. Combining individuals into 2- and 4-fish composite samples for laboratory analysis did not decrease power because the reduced number of laboratory samples was compensated for by increased precision of composites for estimating mean conditions. However, low sample sizes (<5 fish) did not achieve 80% power to detect near-threshold values (i.e., <1 mg Se/kg) under any scenario we evaluated. This analysis can assist the sampling design and interpretation of Se assessments from fish tissue by accounting for natural variation in stream fish populations. Integr Environ Assess Manag 2015;11:143-149. Published 2014 SETAC [ABSTRACT FROM AUTHOR]

Data sets from ecological experiments can be difficult to analyze, due to lack of independence of experimental units and complex variance structures. In addition, information of interest may lie in complicated contrasts among treatments, rather than direct output from statistical tests. Here, we present a statistical framework for analyzing data sets containing non-independent experimental units and differences in variance among treatments (hetero-scedasticity) and apply this framework to experimental data on interspecific competition among three tadpole species. Our framework involves three steps: (1) use a multilevel regression model to calculate coefficients of treatment effects on response variables; (2) combine coefficients to quantify the strength of competition (the target information of our experiment); and (3) use parametric bootstrapping to calculate significance of competition strengths. We repeated this framework using three multilevel regression models to analyze data at the level of individual tadpoles, at the replicate level, and at the replicate level accounting for heteroscedasticity. Comparing results shows the need to correctly specify the statistical model, with the model that accurately accounts for heteroscedasticity leading to different conclusions from the other two models. This approach gives a single, comprehensive analysis of experimental data that can be used to extract informative biological parameters in a statistically rigorous way. [ABSTRACT FROM AUTHOR]

Abstract: In the megadiverse conservation hotspot of the Philippines, biodiversity is not uniformly distributed throughout the archipelago, but hierarchically partitioned into islands and island groups that were conjoined during the mid- to late-Pleistocene. Few species groups are widely distributed throughout the archipelago, but some exceptions exist, such as the common scincid lizards of the Sphenomorphus jagori complex (including S. jagori, S. coxi, and S. abdictus). Using mtDNA haplotype data we test biogeographic and taxonomic predictions in these abundant, large-bodied, forest floor lizards and arrive at conclusions that differ significantly from both past, and current, appraisals of species diversity. In contrast to expectations based on existing taxonomy (three species, each with two subspecies), we find evidence of at least eleven highly divergent species lineages diagnosed by haplotypic variation. Each lineage corresponds to a biogeographically circumscribed distribution (i.e., isolated islands or geological components of islands), suggesting lineage cohesion and allopatric differentiation. Parametric bootstrapping tests reject taxonomic and biogeographic hypotheses and suggest a complex pattern of unpredicted relationships. Only one of the former species (S. jagori) appears as a monophyletic entity (including four allopatric, highly divergent lineages that we suspect may represent evolutionary species), and the remaining species are paraphyletic, necessitating a comprehensive future taxonomic revision. The pattern of biogeographic provincialism and hidden cryptic species diversity detected here leads us to suspect that even the most common, presumably well-studied, and widespread species complexes in the Philippines are in need of thorough analysis with modern genetic and phylogenetic techniques. Such studies of speciation genetics in these common, widely distributed groups may lead to a better understanding of the genetic underpinnings of biodiversity, allow for an enhanced appreciation of the evolutionary history of this model island archipelago, and enable more informed conservation planning in a global biodiversity hotspot. [Copyright &y& Elsevier]

The article proposes a solution to determine the number of clumps through model selection of a multi-clump finite mixture model using the bootstrap likelihood ratio test compared with likelihood ratio test and score test. Based on the analysis, the likelihood ratio tests have shown the shortcomings of the traditional large sample procedures. In these situations, the score test does not exist, while the bootstrap ratio test produces an approximate correct values.

Investigating the fit of a parametric model is an important part of the measurement process when implementing item response theory (IRT), but research examining it is limited. A general nonparametric approach for detecting model misfit, introduced by J. Douglas and A. S. Cohen (2001), has exhibited promising results for the twoparameter logistic model and Samejima s graded response model. This study extends this approach to test the fit of generalized partial credit model (GPCM). The empirical Type I error rate and power of the proposed method are assessed for various test lengths, sample sizes, and type of assessment. Overall, the proposed fit statistic performed well under the studied conditions in that the Type I error rate was not inflated and the power was acceptable, especially for moderate to large sample sizes. A further advantage of the nonparametric approach is that it provides a convenient graphical display of possible misfit. [ABSTRACT FROM AUTHOR]

Evaluating the risk of disorders in long-term insurance often relies on rates of onset estimated from quite small epidemiological studies. These estimates can carry considerable uncertainty, hence so may functions of them, such as a premium rate. In the case of genetic disorders, where it may be required to demonstrate the reliability of genetic information as a risk factor, such uncertainty may be material. Epidemiological studies publish their results in a variety of forms and it is rarely easy to estimate the sampling distribution of a premium rate without access to the original data. We found a large study of breast and ovarian cancer that cited relative risks of breast and ovarian cancer onset, with confidence intervals, in 10-year age groups. We obtained critical illness premium rates and their sampling distributions by parametric bootstrapping, and investigated the effect of possible patterns of sampling correlations. We found that this study provides ample statistical evidence that known BRCA 1 or BRCA2 mutations, or a typical family history of breast or ovarian cancer, are reliable risk factors, but the sampling covariances of the relative risks could be important at some ages and terms. Studies that cite only standard errors of parameter estimates erect a small but awkward barrier between the models they describe, and some important actuarial questions. [ABSTRACT FROM AUTHOR]

Abstract: Overdispersion or extra variation is a common phenomenon that occurs when binomial (multinomial) data exhibit larger variances than that permitted by the binomial (multinomial) model. This arises when the data are clustered or when the assumption of independence is violated. Goodness-of-fit (GOF) tests available in the overdispersion literature have focused on testing for the presence of overdispersion in the data and hence they are not applicable for choosing between the several competing overdispersion models. In this paper, we consider a GOF test proposed by Neerchal and Morel [1998. Large cluster results for two parametric multinomial extra variation models. J. Amer. Statist. Assoc. 93(443), 1078–1087], and study its distributional properties and performance characteristics. This statistic is a direct analogue of the usual Pearson chi-squared statistic, but is also applicable when the clusters are not necessarily of the same size. As this test statistic is for testing model adequacy against the alternative that the model is not adequate, it is applicable in testing two competing overdispersion models. [Copyright &y& Elsevier]

Abstract: This is an expanded study of the relationships among the deuterostome animals based on combined, nearly complete 28S and 18S rRNA genes (>3925 nt.). It adds sequences from 20 more taxa to the ∼45 sequences used in past studies. Seven of the new taxa were sequenced here (brittle star Ophiomyxa, lizard Anolis, turtle Chrysemys, sixgill shark Hexanchus, electric ray Narcine, Southern Hemisphere lamprey Geotria, and Atlantic hagfish Myxine for 28S), and the other 13 were from GenBank and the literature (from a chicken, dog, rat, human, three lungfishes, and several ray-finned fishes, or Actinopterygii). As before, our alignments were based on secondary structure but did not account for base pairing in the stems of rRNA. The new findings, derived from likelihood-based tree-reconstruction methods and by testing hypotheses with parametric bootstrapping, include: (1) brittle star joins with sea star in the echinoderm clade, Asterozoa; (2) with two hagfishes and two lampreys now available, the cyclostome (jawless) fishes remain monophyletic; (3) Hexanchiform sharks are monophyletic, as Hexanchus groups with the frilled shark, Chlamydoselachus; (4) turtle is the sister taxon of all other amniotes; (5) bird is closer to the lizard than to the mammals; (6) the bichir Polypterus is in a monophyletic Actinopterygii; (7) Zebrafish Danio is the sister taxon of the other two teleosts we examined (trout and perch); (8) the South American and African lungfishes group together to the exclusion of the Australian lungfish. Other findings either upheld those of the previous rRNA-based studies (e.g., echinoderms and hemichordates group as Ambulacraria; orbitostylic sharks; batoids are not derived from any living lineage of sharks) or were obvious (monophyly of mammals, gnathostomes, vertebrates, echinoderms, etc.). Despite all these findings, the rRNA data still fail to resolve the relations among the major groups of deuterostomes (tunicates, Ambulacraria, cephalochordates and vertebrates) and of gnathostomes (chondrichthyans, lungfishes, coelacanth, actinopterygians, amphibians, and amniotes), partly because tunicates and lungfishes are rogue taxa that disrupt the tree. Nonetheless, parametric bootstrapping showed our RNA-gene data are only consistent with these dominant hypotheses: (1) deuterostomes consist of Ambulacraria plus Chordata, with Chordata consisting of tunicates and ‘vertebrates plus cephalochordates’; and (2) lungfishes are the closest living relatives of tetrapods. [Copyright &y& Elsevier]

We used parametric bootstrapping to assess the performance of maximum parsimony and maximum likelihood phylogenetic analyses of a 12-locus seed plant data set. Evidence of biases in maximum parsimony analyses of single-locus data sets may explain some of the locus-specific variation among DNA-based hypotheses of seed plant phylogeny. In particular, there is strong evidence of bias in maximum parsimony analyses, especially of plastid loci, that favors placing Gnetales sister to other seed plants. We concatenated simulated single-locus data sets to examine biases in analyses of a 12-locus data set in which each locus is simulated with different substitution parameters and branch lengths. Maximum parsimony analyses of the simulated 12-locus data set also show evidence of biases in favor of recovering trees with Gnetales sister to other seed plants and against recovering anthophyte, gnepine, and gnetifer trees. These biases are most evident in analyses that include the fastest-evolving characters. In the maximum likelihood analyses of the simulated 12-locus data sets, there is evidence of a bias against recovering the anthophyte hypothesis. Otherwise, there is little evidence that the heterogeneous branch lengths and substitution processes among loci influence the results from maximum likelihood phylogenetic analyses. [ABSTRACT FROM AUTHOR]

Abstract: This work expands on a study from 2004 by Mallatt, Garey, and Shultz [Mallatt, J.M., Garey, J.R., Shultz, J.W., 2004. Ecdysozoan phylogeny and Bayesian inference: first use of nearly complete 28S and 18S rRNA gene sequences to classify the arthropods and their kin. Mol. Phylogenet. Evol. 31, 178–191] that evaluated the phylogenetic relationships in Ecdysozoa (molting animals), especially arthropods. Here, the number of rRNA gene-sequences was effectively doubled for each major group of arthropods, and sequences from the phylum Kinorhyncha (mud dragons) were also included, bringing the number of ecdysozoan taxa to over 80. The methods emphasized maximum likelihood, Bayesian inference and statistical testing with parametric bootstrapping, but also included parsimony and minimum evolution. Prominent findings from our combined analysis of both genes are as follows. The fundamental subdivisions of Hexapoda (insects and relatives) are Insecta and Entognatha, with the latter consisting of collembolans (springtails) and a clade of proturans plus diplurans. Our rRNA-gene data provide the strongest evidence to date that the sister group of Hexapoda is Branchiopoda (fairy shrimps, tadpole shrimps, etc.), not Malacostraca. The large, Pancrustacea clade (hexapods within a paraphyletic Crustacea) divided into a few basic subclades: hexapods plus branchiopods; cirripedes (barnacles) plus malacostracans (lobsters, crabs, true shrimps, isopods, etc.); and the basally located clades of (a) ostracods (seed shrimps) and (b) branchiurans (fish lice) plus the bizarre pentastomids (tongue worms). These findings about Pancrustacea agree with a recent study by Regier, Shultz, and Kambic that used entirely different genes [Regier, J.C., Shultz, J.W., Kambic, R.E., 2005a. Pancrustacean phylogeny: hexapods are terrestrial crustaceans and maxillopods are not monophyletic. Proc. R. Soc. B 272, 395–401]. In Malacostraca, the stomatopod (mantis shrimp) was not at the base of the eumalacostracans, as is widely claimed, but grouped instead with an euphausiacean (krill). Within centipedes, Craterostigmus was the sister to all other pleurostigmophorans, contrary to the consensus view. Our trees also united myriapods (millipedes and centipedes) with chelicerates (horseshoe crabs, spiders, scorpions, and relatives) and united pycnogonids (sea spiders) with chelicerates, but with much less support than in the previous rRNA-gene study. Finally, kinorhynchs joined priapulans (penis worms) at the base of Ecdysozoa. [Copyright &y& Elsevier]

Previous hypotheses of phylogenetic relationships among Nearctic toads (Bufonidae) and their congeners suggest contradictory biogeographic histories. These hypotheses argue that the Nearctic Bufo are: (1) a polyphyletic assemblage resulting from multiple colonizations from Africa; (2) a paraphyletic assemblage resulting from a single colonization event from South America with subsequent dispersal into Eurasia; or (3) a monophyletic group derived from the Neotropics. We obtained approximately 2.5 kb of mitochondrial DNA sequence data for the 12S, 16S, and intervening valine tRNA gene from 82 individuals representing 56 species and used parametric bootstrapping to test hypotheses of the biogeographic history of the Nearctic Bufo. We find that the Nearctic species of Bufo are monophyletic and nested within a large clade of New World Bufo to the exclusion of Eurasian and African taxa. This suggests that Nearctic Bufo result from a single colonization from the Neotropics. More generally, we demonstrate the utility of parametric bootstrapping for testing alternative biogeographic hypotheses. Through parametric bootstrapping, we refute several previously published biogeographic hypotheses regarding Bufo. These previous studies may have been influenced by homoplasy in osteological characters. Given the Neotropical origin for Nearctic Bufo, we examine current distributional patterns to assess whether the Nearctic-Neotropical boundary is a broad transition zone or a narrow boundary. We also survey fossil and paleogeographic evidence to examine potential Tertiary and Cretaceous dispersal routes, including the Paleocene Isthmian Link, the Antillean and Aves Ridges, and the current Central American Land Bridge, that may have allowed colonization of the Nearctic. [ABSTRACT FROM AUTHOR]

Advanced frogs (Neobatrachia) are usually divided into two taxa, Ranoidea (the firmisternal frogs) and Hyloidea (all other neobatrachians). We investigated phylogenetic relationships among several groups of Hyloidea using 12S and 16S rRNA mitochondrial gene sequences and tested explicit relationships of certain problematic hyloid taxa using a sample of 93 neobatrachians. Parsimony, maximum likelihood, and Bayesian inference methods suggest that both the Ranoidea and Hyloidea are well-supported monophyletic groups. We reject three hypotheses using parametric bootstrap simulation: (1) Dendrobatidae lies within the Ranoidea; (2) The group containing Hylidae, Pseudidae, and Centrolenidae is monophyletic; and (3) Brachycephalus is part of Bufonidae. [Copyright &y& Elsevier]

The taxonomic status of and evolutionary relationship between Tasmannia and Drimys (Winteraceae) have been subjects of controversy for many years. In this paper, a molecular phylogenetic analysis of the family with sequences of previously unpublished Tasmannia and Drimys species confirms earlier conclusions that Tasmannia and Drimys do not form a monophyletic group, despite the fact that they appear to share distinctive inflorescence and floral morphological attributes. Examination of alternative hypotheses of relationships with likelihood-ratio tests and parametric bootstrapping supports the separation of Tasmannia and Drimys. A detailed analysis of floral development in Tasmannia lanceolata and T. xerophila indicates that timing and position of sepal initiation differs between them, but that the position of subsequent organ initiation predictably follows from sepal position. This is in contrast to Drimys winteri, where a prolonged delay between sepal and petal initiation leads to the production of many phyllotactic patterns. The prolonged period of calyx tube growth leading to the formation of a calyptra in Tasmannia and Drimys probably evolved in parallel in the two lineages. [ABSTRACT FROM AUTHOR]

We conducted a phylogenetic study of pygopodid lizards, a group of 38 species endemic to Australia and New Guinea, with two major goals: to reconstruct a taxonomically complete and robustly supported phylogeny for the group and to use this information to gain insights into the tempo, mode, and timing of the pygopodid radiation. Phylogenetic analyses of mitochondrial DNA (mtDNA), nuclear DNA (nDNA), and previously published morphological data using parsimony, maximum likelihood, and Bayesian methods on the independent and combined three data sets yielded trees with similar and largely stable ingroup topologies. However, relationships among the six most inclusive and unambiguously supported clades ( Aprasia , Delma , Lialis , Ophidiocephalus , Pletholax , and Pygopus ) varied depending on data set analyzed. We used parametric bootstrapping to help us understand which of the three-branch schemes linking these six taxa was most plausible given our data. We conclude based on our results that the arrangement (((( Delma , Lialis ) Pygopus ) Pletholax )( Aprasia , Ophidiocephalus )) represents the best hypothesis of intergeneric relationships. A second major problem to arise in our study concerned the inability of our two outgroup taxa ( Diplodactylus ) to root trees properly; three different rooting locations were suggested depending upon analysis. This long-branch attraction problem was so severe that the outgroup branch also interfered with estimation of ingroup relationships. We therefore used the molecular clock method to root the pygopodid tree. Results of two independent molecular clock analyses (mtDNA and nDNA) converged upon the same root location (branch leading to Delma ). We are confident that we have found the correct root because the possibility of our clock estimates agreeing by chance alone is remote given that there are 65 possible root locations (branches) on the pygopodid tree (∼1 in 65 odds). Our analysis also indicated that Delma fraseri is not monophyletic, a result supported by a parametric bootstrapping test. We elevated the Western Australian race, Delma f. petersoni , to species status (i.e., Delma petersoni ) because hybridization and incomplete lineage sorting could be ruled out as potential causes of this paraphyletic gene tree and because D. grayii is broadly sympatric with its sister species D. fraseri . Climate changes over the past 23 million years, which transformed Australia from a wet, green continent to one that is largely dry and brown, have been suspected as playing a major role in the diversification of Australia's temperate biotas. Our phylogenetic analyses of pygopodid speciation and biogeography revealed four important findings consistent with this climate change diversification model: (1) our fossil-calibrated phylogeny shows that although some extant pygopodid lineages predate the onset of aridification, 28 of 33 pygopodid species included in our study seem to have originated in the last 23 million years; (2) relative cladogenesis tests suggest that several major clades underwent higher than expected rates of speciation; (3) our findings support earlier studies showing that speciation of mesic-adapted biotas in the southeastern and southwestern corners of Australia largely occurred within each of these regions between 12 and 23 million years ago as opposed to repeated dispersal between these regions; and (4) we have identified for the first time the existence of several pairs of sympatric sister species of lizards living in arid and semiarid ecosystems. These sympatric sister species seem to be younger than allopatric or parapatric sister-species pairs, which is not consistent with previous beliefs. [ABSTRACT FROM AUTHOR]

The Mediterranean fruit fly ( Ceratitis capitata) is an invasive agricultural pest with a wide host range and a nearly global distribution. Efforts to forgo the medfly's spread into the United States are dependent on an understanding of population dynamics in newly established populations elsewhere. To explore the potential influence of demographic and historical parameters in six medfly populations distributed from Mexico to Peru, we created population genetic null models using Monte Carlo simulations. Null expectations for genetic differentiation ( FST) were compared with actual sequence variation from four highly polymorphic nuclear loci. Four colonization scenarios that were modeled led to unique genetic signatures that could be used to interpret empirical data. Unless current gene flow across Latin America was assumed to be very high, we could reject colonizations consisting of multiple introductions, each of low genetic diversity. Further, if simulated populations were small ( Ne = 5 × 102 individuals per population), small invasions from a single source consistently produced FST values comparable to those currently observed in Latin America. In contrast, only large invasions from diverse sources were compatible with the observed data for large populations ( Ne ≥ 5 × 103). This study demonstrates that alternative population genetic hypotheses can be tested empirically even when departures from equilibrium are extreme, and that population genetic theory can be used to explore the processes that underlie biological invasions. [ABSTRACT FROM AUTHOR]

The coefficient of determination R 2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R 2 of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R 2 . Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.R 2 for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called 'inclusive' R 2 , as the square of the structure coefficients times total R 2 . Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors., Competing Interests: The authors declare there are no competing interests., (©2021 Stoffel et al.)