Evaluating the Predictive Power of Ordination Methods in Ecological Context

When striving for the ordination methods best predicting independently measured site factors, the following questions arise: does the optimal choice depend on the kind of biological community analysed? Are there different ordination methods needed to address different site factors? Simultaneously, I explore alternative similarity approaches of entire ordinations, as well as the role of the transformations applied to the scale used in measuring species performance. The combination of methods and data transformations results in 96 alternative solutions for any one data set. These are compared by a graphical display, that is, an ordination of ordinations. The goodness-of-fit of independently measured site factors is assessed by two alternative methods. The resulting 96 performance values serve as independent variables in trend surfaces overlaid to the ordination of ordinations. The results from two real-world data sets indicate that some ordination methods greatly vary with data transformation. Scores close to a binary scale perform best in almost all ordination methods. Methods that intrinsically constrain the range of species scores, such as principal components analysis based on correlation, correspondence analysis (including its detrended version), nonmetric multidimensional scaling, as well as principal coordinates analysis based on the Bray-Curtis distance, always figure among the most successful methods, irrespective of data used.

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Genotyping of Two Mediterranean Trout Populations in Central-Southern Italy for Conservation Purposes Using a Rainbow-Trout-Derived SNP Array

Animals ◽

10.3390/ani11061803 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1803

Author(s):

Valentino Palombo ◽

Elena De Zio ◽

Giovanna Salvatore ◽

Stefano Esposito ◽

Nicolaia Iaffaldano ◽

...

Keyword(s):

Rainbow Trout ◽

Genetic Structure ◽

Fishery Management ◽

Snp Array ◽

Fine Scale ◽

Principal Coordinates Analysis ◽

Data Set ◽

Management Actions ◽

Principal Coordinates ◽

Mediterranean Trout

Mediterranean trout is a freshwater fish of particular interest with economic significance for fishery management, aquaculture and conservation biology. Unfortunately, native trout populations’ abundance is significantly threatened by anthropogenic disturbance. The introduction of commercial hatchery strains for recreation activities has compromised the genetic integrity status of native populations. This work assessed the fine-scale genetic structure of Mediterranean trout in the two main rivers of Molise region (Italy) to support conservation actions. In total, 288 specimens were caught in 28 different sites (14 per basins) and genotyped using the Affymetrix 57 K rainbow-trout-derived SNP array. Population differentiation was analyzed using pairwise weighted FST and overall F-statistic estimated by locus-by-locus analysis of molecular variance. Furthermore, an SNP data set was processed through principal coordinates analysis, discriminant analysis of principal components and admixture Bayesian clustering analysis. Firstly, our results demonstrated that rainbow trout SNP array can be successfully used for Mediterranean trout genotyping. In fact, despite an overwhelming number of loci that resulted as monomorphic in our populations, it must be emphasized that the resulted number of polymorphic loci (i.e., ~900 SNPs) has been sufficient to reveal a fine-scale genetic structure in the investigated populations, which is useful in supporting conservation and management actions. In particular, our findings allowed us to select candidate sites for the collection of adults, needed for the production of genetically pure juvenile trout, and sites to carry out the eradication of alien trout and successive re-introduction of native trout.

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Genetic relationships and variation among ecotypes of seashore paspalum (Paspalum vaginatum) determined by random amplified polymorphic DNA markers

Genome ◽

10.1139/g94-143 ◽

1994 ◽

Vol 37 (6) ◽

pp. 1011-1017 ◽

Cited By ~ 37

Author(s):

Zhao-Wei Liu ◽

Robert L. Jarret ◽

Ronny R. Duncan ◽

Stephen Kresovich

Keyword(s):

Rapd Markers ◽

Genetic Relationships ◽

Random Amplified Polymorphic Dna ◽

The United States ◽

Principal Coordinates Analysis ◽

Data Set ◽

Seashore Paspalum ◽

Principal Coordinates ◽

Paspalum Vaginatum ◽

High Level

Random amplified polymorphic DNA (RAPD) markers were used to assess genetic relationships and variation among ecotypes of the turfgrass seashore paspalum (Paspalum vaginatum Swartz). Vegetative tissues or seeds of 46 seashore paspalum ecotypes were obtained from various locations in the United States, Argentina, and South Africa. Leaf DNA extracts were screened for RAPD markers using 34 10-mer random primers. A total of 195 reproducible RAPD fragments were observed, with an average of six fragments per primer. One hundred and sixty-nine fragments (87% of the total observed) were polymorphic, among which 27 fragments (16%) were present in three or less ecotypes, indicating the occurrence of a high level of genetic variation among the examined accessions of this species. Cluster analysis (UPGMA) and principal coordinates analysis were performed on the RAPD data set. The results illustrate genetic relationships among the 46 ecotypes, and between ecotypes and their geographical origins. Ecotypes from southern Africa could be differentiated from the U.S. and most of the Argentinean ecotypes. With a few exceptions, ecotypes collected from Argentina, Hawaii, Florida, and Texas were separated into distinct clusters.Key words: RAPDs, polymerase chain reaction, genetic diversity, phenetic analysis.

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A field study and analysis of passengers arriving at lift lobbies in social groups in multi-storey office, hotel and residential buildings

Building Services Engineering Research and Technology ◽

10.1177/0143624420983974 ◽

2020 ◽

pp. 014362442098397

Author(s):

Janne Sorsa ◽

Marja-Liisa Siikonen ◽

Juha-Matti Kuusinen ◽

Henri Hakonen

Keyword(s):

Statistical Model ◽

Field Study ◽

Group Size ◽

Goodness Of Fit ◽

Geometric Distribution ◽

Residential Buildings ◽

Social Groups ◽

Real World Data ◽

Data Set ◽

Traffic Simulations

This field study was made to study the size of social groups among passengers travelling with lifts. The group size was observed in three types of buildings located in four countries, totally in nine multi-storey buildings. The observations were carried out manually for 12 hours on a normal weekday. Analysis results show that the daily mean group size was generally low: 1.2–1.3 persons in the offices, 1.3–2.0 persons in the hotels and 1.1–1.4 persons in the residential buildings. Hourly means differed significantly from the daily means for each building. This suggests that group size should be considered on hourly or shorter basis. In addition, goodness-of-fit tests were conducted to determine a statistical model for the group size. A geometric distribution was found to fit the data the best across all building types which simplifies modeling of passenger group arrivals in buildings. Practical application: This article contributes to the understanding of lift passenger tendency to travel in socially connected groups in multi-storey buildings. A unique data set of group size observations around the world is reported on an hourly basis for office, hotel and residential buildings. A geometric distribution is proposed to model the group size statistically, which simplifies the use of socially connected groups when generating passengers in lift traffic simulations. The real-world data and the statistical model of the group size enable more realistic lift traffic simulations that are routinely conducted during the design phase of multi-storey buildings.

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Natural hybridization between Elymus repens and E. hispidus assessed by AFLP analysis

Acta Societatis Botanicorum Poloniae ◽

10.5586/asbp.2007.025 ◽

2011 ◽

Vol 76 (3) ◽

pp. 225-234 ◽

Cited By ~ 11

Author(s):

Magdalena Szczepaniak ◽

Elżbieta Cieślak ◽

Piotr Bednarek

Keyword(s):

Pollen Viability ◽

Vegetative Reproduction ◽

Natural Hybridization ◽

Hybrid Origin ◽

F1 Hybrids ◽

Aflp Analysis ◽

Principal Coordinates Analysis ◽

Data Set ◽

Principal Coordinates ◽

Elymus Repens

Hybrid speciation within the genus Elymus is analyzed and discussed. The supposed hybrid origin of Elymus x mucronatus between Elymus repens and Elymus hispidus was tested. In this aim, pollen viability and AFLP variation of putative progenitors and progenies from two natural populations were studied. Pollen fertility of hybrids ranging from 0 to 91.95% (average 59.50%) indicates the occurrence of F1 hybrids and backcross hybrids. Seven EcoRI/MseI AFLP primer combinations generated a total of 477 AFLP fragments (300 polymorphic) for 48 parental and hybrid individuals. UPGMA and principal coordinates analysis of the AFLP data set show that hybrids are closer to E. hispidus than to E. repens and suggest unidirectional introgression towards E. hispidus. AFLP fragments of the hybrid taxon were additively inherited from the parental species and no hybrid-specific bands were identified. AFLP analysis validates the hybrid status of E. ×mucronatus. Populations in which E. repens, E. hispidus and their hybrids co-occur still progress from early hybridization stages of F1 hybrids to an advanced hybridization stage of a well-established hybrid swarm. E. ×mucronatus is potentially self-sustaining by predominant vegetative reproduction by rhizomes and partial fertility leading to the development of mature seeds. We propose to consider Elymus ×mucronatus as a new species of hybrid origin in the Polish flora.

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A Monte Carlo Simulation Study on the Power of Autocorrelation Tests for ARMA Models

American Journal of Undergraduate Research ◽

10.33697/ajur.2019.030 ◽

2019 ◽

Vol 16 (3) ◽

pp. 59-67

Author(s):

Zachary Wenning ◽

Emily Valenci

Keyword(s):

Time Series ◽

Monte Carlo ◽

Goodness Of Fit ◽

Arma Model ◽

Small Sample ◽

Arma Models ◽

Real World Data ◽

Data Set ◽

Monte Carlo Simulation Study ◽

Portmanteau Tests

It is often the case when assessing the goodness of fit for an ARMA time series model that a portmanteau test of the residuals is conducted to assess residual serial correlation of the fitted ARMA model. Of the many portmanteau tests available for this purpose, one of the most famous and widely used is a variant of the original Box-Pierce test, the Ljung-Box test. Despite the popularity of this test, however, there are several other more modern portmanteau tests available to assess residual serial autocorrelation of the fitted ARMA model. These include two portmanteau tests proposed by Monti and Peña and Rodríguez. This paper focuses on the results of a power analysis comparing these three different portmanteau tests against different fits of ARMA - derived time series, as well as the behavior of the three different test statistics examined when applied to a real-world data set. We confirm that for situations in which the moving average component of a fitted ARMA model is underestimated or when the sample size is small, the portmanteau test proposed by Monti is a viable alternative to the Ljung-Box test. We show new evidence that the Peña and Rodríguez may also be a viable option for testing for residual autocorrelation for data with small sample sizes. KEYWORDS: Time Series; Monte Carlo; ARMA Models; Power; Simulation; Autocorrelation Tests; Portmanteau Tests; Monti; Ljung-Box; Peña and Rodríguez

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A comparative analysis of mathematical methods for homogeneity estimation of the Lithuanian population

Acta medica Lituanica ◽

10.6001/actamedica.v26i4.4206 ◽

2020 ◽

Vol 26 (4) ◽

pp. 211-216

Author(s):

Alma Molytė ◽

Alina Urnikytė ◽

Vaidutis Kučinskas

Keyword(s):

Comparative Analysis ◽

Genetic Structure ◽

Multidimensional Scaling ◽

Population Genetic ◽

Venous Blood ◽

Principal Component ◽

Principal Coordinates Analysis ◽

Data Set ◽

Background Population ◽

Principal Coordinates

Background. Population genetic structure is one of the most important population genetic parameters revealing its demographic features. The aim of this study was to evaluate the homogeneity of the Lithuanian population on the basis of the genome-wide genotyping data. The comparative analysis of three methods – multidimensional scaling, principal components, and principal coordinates analysis – to visualize multidimensional genetics data was performed. The results of visualization (mapping images) are also presented. Materials and methods. The data set consisted of 425 samples from six ethnolinguistic groups of the Lithuanian population. Genomic DNA was extracted from whole venous blood using either the phenol-chloroform extraction method or the automated DNA extraction platform TECAN Freedom EVO. Genotyping was performed at the Department of Human and Medical Genetics, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Lithuania, with the Illumina HumanOmniExpress-12 v1.1 and the Infinium OmniExpress-24. For the estimation of homogeneity of the Lithuanian population, PLINK data file was obtained using PLINK v1.07 program. The Past3 software was used to visualize the genotype data with multidimensional scaling and principal coordinates methods. The SmartPCA from EIGENSOFT 7.2.1 program was used in the principal component analysis to determine the population structure. Conclusions. Methods of multidimensional scaling, principal coordinate, and principal component for the genetic structure of the Lithuanian population were investigated and compared. The principal coordinate and principal component methods can be used for genotyping data visualization, since any essential differences in the results obtained were not observed and compared to multidimensional scaling. The Lithuanian population is homogenous whereas the points are strongly close when we use the principal coordinates or principal component methods.

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Simple Index to Assess the Calibration Quality of Safety Performance Functions Based on Multiple Goodness-of-Fit Metrics

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211008896 ◽

2021 ◽

pp. 036119812110088

Author(s):

Raul E. Avelar ◽

Karen Dixon ◽

Boniphace Kutela ◽

Sam Klump ◽

Beth Wemple ◽

...

Keyword(s):

Goodness Of Fit ◽

Synthetic Data ◽

Calibration Procedure ◽

Safety Performance ◽

Absolute Deviation ◽

Data Set ◽

Safety Database ◽

Simple Index ◽

Safety Performance Functions

The calibration of safety performance functions (SPFs) is a mechanism included in the Highway Safety Manual (HSM) to adjust SPFs in the HSM for use in intended jurisdictions. Critically, the quality of the calibration procedure must be assessed before using the calibrated SPFs. Multiple resources to aid practitioners in calibrating SPFs have been developed in the years following the publication of the HSM 1st edition. Similarly, the literature suggests multiple ways to assess the goodness-of-fit (GOF) of a calibrated SPF to a data set from a given jurisdiction. This paper uses the calibration results of multiple intersection SPFs to a large Mississippi safety database to examine the relations between multiple GOF metrics. The goal is to develop a sensible single index that leverages the joint information from multiple GOF metrics to assess overall quality of calibration. A factor analysis applied to the calibration results revealed three underlying factors explaining 76% of the variability in the data. From these results, the authors developed an index and performed a sensitivity analysis. The key metrics were found to be, in descending order: the deviation of the cumulative residual (CURE) plot from the 95% confidence area, the mean absolute deviation, the modified R-squared, and the value of the calibration factor. This paper also presents comparisons between the index and alternative scoring strategies, as well as an effort to verify the results using synthetic data. The developed index is recommended to comprehensively assess the quality of the calibrated intersection SPFs.

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Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab526 ◽

2021 ◽

Vol 503 (2) ◽

pp. 2688-2705

Author(s):

C Doux ◽

E Baxter ◽

P Lemos ◽

C Chang ◽

A Alarcon ◽

...

Keyword(s):

Dark Energy ◽

Internal Consistency ◽

Goodness Of Fit ◽

Small Scale ◽

P Value ◽

Posterior Predictive Distribution ◽

Data Set ◽

Dark Energy Survey ◽

Data Vector ◽

Energy Survey

ABSTRACT Beyond ΛCDM, physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analysed assuming ΛCDM. We present an application of internal consistency tests to measurements from the Dark Energy Survey Year 1 (DES Y1) joint probes analysis. Our analysis relies on computing the posterior predictive distribution (PPD) for these data under the assumption of ΛCDM. We find that the DES Y1 data have an acceptable goodness of fit to ΛCDM, with a probability of finding a worse fit by random chance of p = 0.046. Using numerical PPD tests, supplemented by graphical checks, we show that most of the data vector appears completely consistent with expectations, although we observe a small tension between large- and small-scale measurements. A small part (roughly 1.5 per cent) of the data vector shows an unusually large departure from expectations; excluding this part of the data has negligible impact on cosmological constraints, but does significantly improve the p-value to 0.10. The methodology developed here will be applied to test the consistency of DES Year 3 joint probes data sets.

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Auto-sharing parameters for transfer learning based on multi-objective optimization

Integrated Computer-Aided Engineering ◽

10.3233/ica-210655 ◽

2021 ◽

pp. 1-13

Author(s):

Hailin Liu ◽

Fangqing Gu ◽

Zixian Lin

Keyword(s):

Transfer Learning ◽

Optimization Problem ◽

Data Sets ◽

Multi Objective Optimization ◽

Particle Swarm Optimizer ◽

Real World Data ◽

Data Set ◽

Target Task ◽

Main Research ◽

Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

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