Non-metric Multidimensional Scaling Method Research Articles

Environmental Variables Influence the Composition of Frog Communities in Riparian and Semi-Deciduous Forests of the Brazilian Cerrado

Abstract. A central issue for ecologists is to understand the variables that determine species distribution. Many studies have suggested that environmental variables are the main factors that shape anuran communities. Nevertheless, several natural environments have not been assessed with ecological studies of this nature, including specific ecosystems of the South American Cerrado morphoclimatic domain. In the present study, we investigated the relationship between environmental variables and the spatial distribution of anurans in ecosystems of semi-deciduous and riparian forests in a transitional region between the Cerrado and Caatinga environments in the western Bahia state, Brazil. We collected fauna and environmental variables data in 10 sampling units during two rainy periods. The original information on species composition and abundance in the community was reduced to a single axis using a non-metric multidimensional scaling method (NMDS) and environmental data were reduced to two main factors using...

Biogeography of Anurans from the Poorly Known and Threatened Coastal Sandplains of Eastern Brazil.

The east coast of Brazil comprises an extensive area inserted in the Tropical Atlantic Domain and is represented by sandy plains of beach ridges commonly known as Restingas. The coastal environments are unique and house a rich amphibian fauna, the geographical distribution patterns of which are incipient. Biogeographical studies can explain the current distributional patterns and provide the identification of natural biogeographical units. These areas are important in elucidating the evolutionary history of the taxa and the areas where they occur. The aim of this study was to seek natural biogeographical units in the Brazilian sandy plains of beach ridges by means of distribution data of amphibians and to test the main predictions of the vicariance model to explain the patterns found. We revised and georeferenced data on the geographical distribution of 63 anuran species. We performed a search for latitudinal distribution patterns along the sandy coastal plains of Brazil using the non-metric multidimensional scaling method (NMDS) and the biotic element analysis to identify natural biogeographical units. The results showed a monotonic variation in anuran species composition along the latitudinal gradient with a break in the clinal pattern from 23°S to 25°S latitude (states of Rio de Janeiro to São Paulo). The major predictions of the vicariance model were corroborated by the detection of four biotic elements with significantly clustered distribution and by the presence of congeneric species distributed in distinct biotic elements. The results support the hypothesis that vicariance could be one of the factors responsible for the distribution patterns of the anuran communities along the sandy coastal plains of eastern Brazil. The results of the clusters are also congruent with the predictions of paleoclimatic models made for the Last Glacial Maximum of the Pleistocene, such as the presence of historical forest refugia and biogeographical patterns already detected for amphibians in the Atlantic Rainforest.

Endemic shrubs in temperate arid and semiarid regions of northern China and their potentials for rangeland restoration

Some endemic shrubs in arid and semiarid ecosystems are in danger of extinction, and yet they can play useful roles in maintaining or restoring these ecosystems, thus practical efforts are needed to conserve them. The shrubs Amygdalus pedunculata Pall., Amygdalus mongolica (Maxim.) Ricker and Ammopiptanthus mongolicus (Maxim. ex Kom.) Cheng f. are endemic species in arid and semiarid regions of northern China, where rangeland desertification is pronounced due to chronic overgrazing. In this study, we tested the hypothesis that these endemic shrubs have developed adaptations to arid and semiarid environments and could play critical roles as nurse species to initiate the process of rangeland recovery. Based on careful vegetation surveys, we analysed the niches of these species in relation to precipitation, temperature and habitats. All sampling plots were categorized by these endemics and sorted by the non-metric multidimensional scaling method. Species ratios of each life form and species co-occurrence rates with the endemics were also evaluated. Annual average temperature and annual precipitation were found to be the key factors determining vegetation diversity and distributions. Amygdalus pedunculata prefers low hills and sandy land in temperate semiarid regions. Amygdalus mongolica prefers gravel deserts of temperate semiarid regions. Ammopiptanthus mongolicus prefers sandy land of temperate arid regions. Communities of A. pedunculata have the highest diversity and the largest ratios of long-lived grass species, whereas those of A. mongolicus have the lowest diversity but the largest ratios of shrub species. Communities of A. mongolica are a transition between the first two community types. These findings demonstrate that our focal endemic shrubs have evolved adaptations to arid and semiarid conditions, thus they can be nurse plants to stabilize sand ground for vegetation restoration. We suggest that land managers begin using these shrub species to restore degraded rangelands as part of a general conservation effort.

Microbiology- and Biodiversity-Based Modeling of Suppression of Cottony Leak of Scarlet Runner Bean in Soils with Diverse and Uniform Ecology

Soil-borne plant disease suppression is a primary solution to the pressing global need for stable food supply. However, no indicators of Soil-borne plant disease suppression have been established as yet. Hence, disease prevention through proper soil control has not been possible. In this paper, we propose a new biological indicator for Soil-borne plant disease suppression: the ability of microorganisms to consume carbon resources in the soil, which can be automatically observed with an OmniLog PM system over a period of one or two day duration. This indicator could be used to distinguish disease suppression potential soils for cottony leak of scarlet runner bean from other soils. We modelled the characteristic time developments of carbon resource consumption using a simple ecological model in which microorganisms compete with each other for carbon resources. The measured ecological structures of soil microorganism match well with the theoretical prediction. In order to determine the characteristic features for each type of soil, the observed time developments are embedded into a two-dimensional space by a non-metric multidimensional scaling method. This results in an almost one-dimensional arrangement of embedded points. An analysis of the spatial distributions of each carbon resource in the embedded space shows that healthy soils have a mostly uniform distribution along this one-dimensional arrangement. Since sick soil and non-soil samples have rather localized distributions, the ecological systems in a more disease- suppressive soil are both more diverse and more uniform. Since this indicator can easily and quickly be obtained automatically, it is expected to be useful for validating the various soil improvement methods that are used before a type of soil is employed for cultivation.

Relationships between aquatic biotic communities and water quality in a tropical river–wetland system (Ecuador)

Abstract Many tropical wetlands threatened by land use changes, or modifications in hydrological regime require effective management policies and implementation to protect them. The Abras de Mantequilla wetland, located in the Guayas River Basin in Ecuador, is subject to two major environmental disturbances, i.e., short-term agriculture (rice, maize) on the land around the wetland and the effects of planned infrastructure works of the Baba dam in the upper catchment. Both activities are expected to be the main constraints for the future wetland health. The objective of this study was to provide an initial characterization of the biotic communities of the river and wetland habitats before the dam starts operating. Plankton, macroinvertebrates, fishes and associated physical and chemical variables were sampled at 12 sites during the wet season (February 2011). Biotic metrics (abundance, taxa richness, diversity and evenness) were computed for the aquatic communities in the wetland and the river. A biotic index (Biological Monitoring Working Party-Colombia/adaptation) was applied to the macroinvertebrate community. Relationships between biotic and abiotic variables indicated nutrients, velocity and sediment type as main drivers. Cluster analysis grouped physico-chemical variables according to river or wetland sites. Similarities regarding the taxa composition among sites were explored with non-metric multidimensional scaling method (NMDS), showing clusters for ichthyoplankton and macroinvertebrates. Higher densities of organisms were recorded in the wetland compared with the river. The wetland is an important area of breeding and reproduction for fish communities, with its lentic habitats promoting the development of high densities of ichthyoplankton. In order to achieve sustainable solutions for integrated river–wetland systems, management options should focus on maintaining natural variation in hydrodynamic conditions throughout the entire catchment, as well as implement good practices in agriculture and reforestation using native species. Local and national authorities should support continuous monitoring programmes, taking account of seasonal variation and of future impacts from flow reduction and nutrient enrichment.

Contribuição de variáveis ambientais à estrutura de comunidade de anuros no Domínio da Caatinga of Brazil

To ascertain the influence of environmental variables on the structure of anuran communities, we conducted tests to determine which variables best explain the spatial distribution patterns of different communities of anurans in a rocky, montane environment characterized mainly by open habitats and gallery forests. The study area is the Sete Passagens State Park in the State of Bahia, Brazil, an area included in the Chapada Diamantina Region and in the Caatinga Morphoclimatic Domain. We sampled 20 plots (60 × 25 m) during two rainy seasons, and measured 13 environmental variables. The environmental matrix was reduced to four synthetic axes through the principal component analysi, and the anuran community matrix was reduced to a synthetic axis through the non-metric multidimensional scaling method. We tested the association between environmental axes (independent variables) and community composition (dependent variable) using a multiple regression analysis. The single axis significantly associated with the species of anurans found in any given plot is Principal Component 1, in which the major contributors to variation are factors involving vegetation structure, listed in descending order, as follow: percentage of leaf litter, density of trees, canopy height, soil moisture, and percentage of understory, shrub, and herb layers. The ordination of the anuran community showed an environmental gradient from the Campo Rupestre to Semideciduous Forest patches, characterized mainly by the gradual increase of layering in the vegetation.

Ammonite paleobiogeography during the Pliensbachian–Toarcian crisis (Early Jurassic) reflecting paleoclimate, eustasy, and extinctions

The Pliensbachian–Toarcian crisis (Early Jurassic) is one of the major Mesozoic paleoecological disturbances when ca. 20% of marine and continental families went extinct. Contemporaneously, profound paleobiogeographical changes occurred in most oceanic domains including a disruption of ammonite provincialism during the Early Toarcian. Here, we quantitatively reappraise the structure and evolution of paleobiogeographical patterns displayed by ammonite faunas before, during, and after the biological crisis, over a time-interval including 13 biochronozones. The high-resolution study presented here involves the use of hierarchical Cluster Analyses, non-metric Multi-Dimensional Scaling methods, and Bootstrap Spanning Network approaches that we apply to a large database including 772 ammonite species from 16 northwestern Tethyan and Arctic basins. Our results confirm a robust faunal dichotomy between Euro-Boreal and Mediterranean areas throughout the Pliensbachian, with the first emergence of an Arctic biome during the cooling regressive event of the Spinatum Zone. Whatever its complexity, Pliensbachian provincialism could be directly linked to paleogeographical barriers and to latitudinal paleoclimatic and paleoecological contrasts. During the Early Toarcian, this pattern was progressively lost, with northward expansions of Mediterranean ammonites during the Tenuicostatum Zone, followed by a strong interprovincial mixing during the Falciferum Zone. This faunal homogenization results from the combination of several parameters including a major sea-level rise facilitating basinal connections, a global warming event stretching the spatial range limits of southern taxa, and a mass extinction preferentially removing endemic species. Ammonite provincialism, although slightly different, was progressively re-established during the cooling regressive trend of the Middle Toarcian. These results therefore suggest a paramount influence of paleoclimatic, eustatic, and extinction constraints on the paleobiogeography of Early Jurassic ammonites, even if some threshold effects or independent biological factors may sporadically complicate the patterns.

Bayesian Nonmetric Successive Categories Multidimensional Scaling

A Bayesian nonmetric successive categories multidimensional scaling (MDS) method is proposed. The proposed method can be seen as a Bayesian alternative to the maximum likelihood multidimensional successive scaling method proposed by Takane (1981), or as a nonmetric extension of Bayesian metric MDS by Oh and Raftery (2001). The model has a graded-response type measurement model part and a latent metric MDS part. All the parameters are jointly estimated using a Markov chain Monte Carlo (MCMC) estimation technique. Moreover, WinBUGS/OpenBUGS code for the proposed methodology is also given to aid applied researchers. The proposed method is illustrated through the analysis of empirical two-mode three-way similarity data.

On the leafy nature of lodicules in the genus Triticum (Poaceae)

Lodicules are important organs creating the mating system in grasses, in a range between autogamy in clestogamous flowers and allogamy in chasmogamous flowers. This balance is the main factor determining variation patterns in plant populations. Recognition of variation in lodicule morphology is especially important in wheat, the main crop of the world. For this reason, a broad range of species of wheat was evaluated by means of characters showing the leafy nature of lodicules. The characters were studied under polarizing and epifluorescence microscopes. Development of lodicule stomata is different than that in leaves. Development of stomata and xylem, is distinctly correlated. Other short cells created by epidermal meristemoids are developmentally rather independent of both stomata and xylem. Interrelations between stomata, xylem and morphogenetic traits of the abaxial epidermis of the lodicule are illustrated by pictures and diagrams of numerical analyses, including a non-metric multidimensional scaling method. Some unthreshable wheats, such as Triticum diccocum, T. ispahanicum and T. spelta, have more leafy lodicules, but intraspecific variation appears in the form of +leafy vs. −leafy lodicules. Formation of leafy lodicule morphs does not depend on the ploidy level of wheat. An intraspecific mosaic of + or −leafy lodicules proves a mutational origin of their leafy characters.

Indicating Soil Acidity Using Vegetation Relevés in Spatially Limited Areas – Case Study from the Považský Inovec, Slovakia

A fine-scaled approach for predicting soil acidity using plant species in a spatially limited area (Cepu�ky Nature Reserve, Slovakia) is presented here. This approach copes with some specific limitations: i) a limited pool of vegetation data may make the predictions too sensitive to the lack of species information, and ii) the predictions may be sensitive to the narrow pH gradient. Vegetation releves and soil reaction (pH-H2O and pH-CaCl2) were systematically recorded. A set of species indicator values and amplitudes was calibrated with physical pH data using the Weighted Averaging (WA), HOF modelling and Non-Metric Multidimensional Scaling (NMDS) methods, along with Ellenberg indicator values. Two prediction methods were tested: i) WA and ii) Amplitude Overlap (AO). WA prediction with Ellenberg�s and WA-calibrated species indicator values were the most powerful technique (R 2?=?68.4�68.7% and 53.4�59.1% for pH-CaCl2 and pH-H2O, respectively). WA-prediction with HOF-based indicator values was less effective (R 2?=?61.7% and 50.7%) due to the decrease in species� information because with HOF modelling many species are assumed indifferent or too rare. The NMDS method does not bring any significant gain to the calibration, though it avoids the lack of species information. The AO method was proven to be less powerful under studied circumstances, because it is sensitive both to the lack of species� information and to the truncation of species responses. The results prove that a spatially explicit approach can provide significant indices to estimate changes in soil acidity � pH-CaCl2 better than pH-H2O.

Going beyond Clustering in MD Trajectory Analysis: An Application to Villin Headpiece Folding

Recent advances in computing technology have enabled microsecond long all-atom molecular dynamics (MD) simulations of biological systems. Methods that can distill the salient features of such large trajectories are now urgently needed. Conventional clustering methods used to analyze MD trajectories suffer from various setbacks, namely (i) they are not data driven, (ii) they are unstable to noise and changes in cut-off parameters such as cluster radius and cluster number, and (iii) they do not reduce the dimensionality of the trajectories, and hence are unsuitable for finding collective coordinates. We advocate the application of principal component analysis (PCA) and a non-metric multidimensional scaling (nMDS) method to reduce MD trajectories and overcome the drawbacks of clustering. To illustrate the superiority of nMDS over other methods in reducing data and reproducing salient features, we analyze three complete villin headpiece folding trajectories. Our analysis suggests that the folding process of the villin headpiece is structurally heterogeneous.

Comparison of molecular fingerprinting methods for analysis of soil microbial community structure

AbstractDenaturing gradient gel electrophoresis (DGGE), terminal‐restriction fragment length polymorphism (T‐RFLP) analysis, and automated ribosomal intergenic spacer analysis (ARISA) have been widely used as molecular fingerprinting methods for analysis of microbial communities. To find suitable methods, we compared the three fingerprinting methods by analyzing soil fungal communities in four differing land‐use types: bare ground, crop fields, grasslands, and forests. We also examined optimal primer pairs for DGGE analysis by comparing single and mixed DNA samples of cultured fungal populations. Principal coordinate analysis (PCO), nonmetric multidimensional scaling method (NMDS), and analysis of similarities (ANOSIM), which are major multivariate statistical analyses for quantifying fingerprint patterns, were compared. All three fingerprinting methods yielded clear discrimination of soil fungal communities among the four land‐use types, irrespective of statistical methods. The advantages and disadvantages of the three fingerprinting methods were discussed.

Sequence-related amplified polymorphism (SRAP) markers for assessing genetic relationships and diversity in sugarcane germplasm collections

Characterization of wild germplasm provides essential information on genetic diversity that breeders utilize for crop improvement. The potential of the sequence-related amplified polymorphism (SRAP) technique, which preferentially amplifies gene-rich regions, was evaluated to assess the genetic relationships among members of theSaccharumspecies. A panel of 31 SRAP primer combinations were used to score 30 genotypes ofSaccharum officinarum,Saccharum spontaneum,Saccharum robustum,Saccharum sinense,Saccharum barberiand sugarcane hybrids, withMiscanthusandErianthusincluded as outgroups. The amplifications produced 1364 DNA fragments for an average of 44 per primer combination, with 83% (1135) being polymorphic and 8.7% (119) being species specific. Based on the Dice index, all 30 genotypes were differentiated from each other with genetic similarity (GS) estimates ranging from 0.60 to 0.96 (mean = 0.79). Both the dendrogram [obtained by the unweighted pairgroup method with arithmetic mean or (UPGMA)] and the non-metric multidimensional scaling method grouped the genotypes according to their phylogenetic relationships.ErianthusandMiscanthuswere separated as two outgroups (at GS levels of 0.56 and 0.72, respectively) into two major clusters: cluster I separated theS. robustum,S. sinense,S. barberiand hybrids as different subgroups with each one including someS. officinarumclones, while cluster II included theS. spontaneumclones, exclusively. AS. officinarum–S. spontaneumsequence comparison of some of the monomorphic and polymorphic bands revealed 65–90% homology with rice, corn or sugarcane sequences deposited in databases. The high discriminatory power coupled with the possibility that most of the amplicons may be amplifying gene-rich regions of the genome makes SRAP a potentially robust tool for genetic mapping aimed at marker-assisted introgression in sugarcane.

Influences of habitat and land cover on fish distributions along a tributary to Lake Ontario, New York

Influences of habitat and land cover on fish distributions were determined along a lentic–lotic gradient along a tributary to Lake Ontario, New York. Nonmetric multidimensional scaling, cluster analysis, and specific characterization methods were used to classify the fish species into five groups based on their similar patterns of distribution, species-specific habitat relationship, and relative abundance observed along the gradient. A stepwise regression approach was used to select the best habitat and land cover variables to explain variations in the distribution pattern of each fish group. Distribution patterns of the five fish groups were significantly explained by either a set of the selected habitat or land cover predictor variables or a combination of both. Of the 10 habitat variables, water depth, current velocity, aquatic plants, algae, woody debris, sand, and rock-bedrock were selected to explain the variations in distribution patterns of one or more fish groups. Of the 16 land cover types, evergreen wetlands, evergreen plantations, successional shrubs, shrub swamps, roads, and urban areas were selected to explain the variations in distribution patterns of at least one fish group.

Development of an Aroma Map of Spices by Multidimensional Scaling

ABSTRACT The current work visualizes odor similarities of spices as perceived by naïve consumers. Similarities were obtained by letting assessors sort samples into groups. A non-metric multidimensional scaling (MDS) method was applied to plot similarities of spices as distances on a three-dimensional map. Such a map could be used by spice companies to communicate to consumers how rather unknown spices smell in relation to better known ones, or to indicate aroma impressions of spice mixtures.

The Impact of Dryland Salinity and Waterlogging On Mosquito-Borne Disease in Western Australia

SAB1-O-04 Introduction: Anthropogenic changes in land use and land cover are primary drivers of ecologic disruption, and have the potential to strongly influence human vulnerability to vector-borne diseases, particularly those carried by mosquitoes. One million hectares in the southwest of Western Australia (5.5% of the region) are currently affected by secondary soil salinization (dryland salinity) and waterlogging caused by extensive clearing of native vegetation, and this is predicted to increase to 5.4 million ha (29%) by 2050. This has led to a steady rise in salt concentration in inland aquatic systems, which progressively compromises ecosystem integrity and reduces aquatic biodiversity. Thus, mosquito species with a high salinity tolerance may be favored over endemic freshwater-breeding species as available freshwater habitat and predator populations decrease. Extensive waterlogging may also increase the overall area of available mosquito breeding habitat. One such salinity tolerant species is Ochlerotatus camptorhynchus, a major vector of Ross River virus (RRV) disease in temperate Australia. The relationship between salinity, mosquitoes, and disease distribution are currently being investigated in the heavily salinised Wheatbelt region of Western Australia. Methods: Mosquito collections were undertaken in regions with varying salinity levels between October 2004 and December 2005 to investigate the seasonal dynamics of the mosquito populations present. Associations were tested using correspondence analysis and non-metric multidimensional scaling (NMDS) methods. All blood-fed mosquitoes collected were tested by direct enzyme-linked immunosorbent assay (ELISA) for blood-meal source identification. Serum neutralization assay testing is currently being carried out to determine the prevalence of IgG antibodies to RRV of residents within these regions. These data will be combined using GIS to analyze the overall impact of dryland salinity on mosquito-borne disease risk. Results: Oc. camptorhynchus breeding was significantly associated with salinity affected sites. The primary bloodmeal source was sheep and marsupials, the latter of which are natural vertebrate hosts for RRV. Results of the serosurvey will also be presented. Discussion and Conclusions: Dryland salinity is significantly associated with breeding of the main mosquito vector for RRV in southwestern Australia. Changes in disease risk in rural communities are currently being evaluated by a concurrent seroprevalence survey. This study indicates the potential impact of environmental degradation on arboviral disease ecology in semi-arid environments. The risk of vector-borne disease in this region of Australia is expected to escalate given the predicted increase in dryland salinity over the next 50 years.

Relational patterns of gene expression via non-metric multidimensional scaling analysis

Microarray experiments result in large-scale data sets that require extensive mining and refining to extract useful information. We demonstrate the usefulness of (non-metric) multidimensional scaling (MDS) method in analyzing a large number of genes. Applying MDS to the microarray data is certainly not new, but the existing works are all on small numbers (< 100) of points to be analyzed. We have been developing an efficient novel algorithm for non-metric MDS (nMDS) analysis for very large data sets as a maximally unsupervised data mining device. We wish to demonstrate its usefulness in the context of bioinformatics (unraveling relational patterns among genes from time series data in this paper). The Pearson correlation coefficient with its sign flipped is used to measure the dissimilarity of the gene activities in transcriptional response of cell-cycle-synchronized human fibroblasts to serum. These dissimilarity data have been analyzed with our nMDS algorithm to produce an almost circular relational pattern of the genes. The obtained pattern expresses a temporal order in the data in this example; the temporal expression pattern of the genes rotates along this circular arrangement and is related to the cell cycle. For the data we analyze in this paper we observe the following. If an appropriate preparation procedure is applied to the original data set, linear methods such as the principal component analysis (PCA) could achieve reasonable results, but without data preprocessing linear methods such as PCA cannot achieve a useful picture. Furthermore, even with an appropriate data preprocessing, the outcomes of linear procedures are not as clear-cut as those by nMDS without preprocessing.

Nonmetric multidimensional scaling with clustering of subjects

A new nonmetric multidimensional scaling method is devised to analyze three‐way data concerning inter‐stimulus similarities obtained from many subjects. It is assumed that subjects are classified into a small number of clusters and that the stimulus configuration is specific to each cluster. Under this assumption, the classification of subjects and the scaling used to derive the configurations for clusters are simultaneously performed using an alternating least‐squares algorithm. The monotone regression of ordinal similarity data, the scaling of stimuli and the K‐means clustering of subjects are iterated in the algorithm. The method is assessed using a simulation and its practical use is illustrated with the analysis of real data. Finally, some extensions are considered.

Nonmetric Linear Biplots

A methodology is developed for constructing linear biplots for a class of nonmetric multidimensional scaling methods for multivariate data. The nonlinear transformations of nonmetric scaling manifest themselves in irregularly spaced calibration markers. Two approaches are examined, one based on Procrustean embedding, the other on a modification of the popular regression method. The widespread use of an unmodified regression method in association with nonlinear transformations is questioned. An example is given. The methodology presented here could potentially be developed to give an optimal represention of a matrix in fewer geometric dimensions than its rank.

Multidimensional analysis of alaryngeal voice quality.

This study investigates and acoustically defines some of the perceptual parameters which characterize alaryngeal voice quality. Twenty voice samples of vowel /e/ were recorded from 16 alaryngeal and four normal speakers, and were paired, randomized and presented to 20 listeners with normal hearing. The listeners rated the dissimilarities of all combinations of the voices, and these dissimilarities were analyzed by a nonmetric multidimensional scaling method called SMACOF. Five acoustical measures and 21 psychoacoustical ratings were made for each vowel, and these provided the interpretation for the SMACOF solution. The results indicate that at least two dimensions are necessary to describe the alaryngeal voice quality. One is connected with the listeners' preferences for the voices and differentiates the alaryngeal voices from the normal ones. This dimension correlates with the fundamental frequency and the relative spectral level of the high frequency components. Another dimension indicates the abnormalities peculiar to the voices produced with artificial larynges and differentiates them from the normal and esophageal voices. It correlates positively with periodicity in the excitation signal waveform and negatively with aperiodicity in the pitch or in the amplitude. These results suggest some direction toward the enhancement of the alaryngeal voice quality.