Nest-site characteristics of the avian community in the dune-ridge forest, Delta Marsh, Manitoba: a multivariate analysis

1982 ◽  
Vol 60 (9) ◽  
pp. 2212-2223 ◽  
Author(s):  
David I. MacKenzie ◽  
Spencer G. Sealy ◽  
Glenn D. Sutherland
Keyword(s):  
Discriminant Analysis ◽  
Nest Site ◽  
Bird Species ◽  
Principal Component ◽  
Multivariate Techniques ◽  
Data Set ◽  
Delta Marsh ◽  
Site Characteristics ◽  
Nest Habitat ◽  
Dune Ridge

Nest-site characteristics of nine bird species breeding in high densities in the dune-ridge forest at Delta Marsh, Manitoba, were analyzed using multivariate techniques. Varimax-rotated principal component analysis of the entire set of nest-site variables suggested partitioning of the data into nest-habitat and nest-tree subsets. Discriminant analysis of nest-habitat variables confirmed the ambiguous nature of species relationships in the factor analysis. Discriminant analysis of nest-tree variables identified three distinct groups of species, based primarily on vertical stratification. The existence of these groups and their memberships were supported by similar results derived from discriminant analysis of the entire nest-site data set. Within these groups, pairs of species showed sufficient similarity in nest sites to warrant detailed investigation.

scholarly journals Application of multivariate statistical techniques in the water quality assessment of Danube river, Serbia

2015 ◽  
Vol 41 (4) ◽  
pp. 96-103 ◽  
Author(s):  
Danijela Voza ◽  
Milovan Vukovic ◽  
Ljiljana Takic ◽  
Djordje Nikolic ◽  
Ivana Mladenovic-Ranisavljevic
Keyword(s):  
Water Quality ◽  
Water Quality Management ◽  
Surface Water Quality ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Water Quality Monitoring ◽  
Danube River ◽  
Multivariate Techniques ◽  
Data Set ◽  
Political Issue

AbstractThe aim of this article is to evaluate the quality of the Danube River in its course through Serbia as well as to demonstrate the possibilities for using three statistical methods: Principal Component Analysis (PCA), Factor Analysis (FA) and Cluster Analysis (CA) in the surface water quality management. Given that the Danube is an important trans-boundary river, thorough water quality monitoring by sampling at different distances during shorter and longer periods of time is not only ecological, but also a political issue. Monitoring was carried out at monthly intervals from January to December 2011, at 17 sampling sites. The obtained data set was treated by multivariate techniques in order, firstly, to identify the similarities and differences between sampling periods and locations, secondly, to recognize variables that affect the temporal and spatial water quality changes and thirdly, to present the anthropogenic impact on water quality parameters.

scholarly journals Nest site characteristics of cavity-nesting birds on a small island, in Haida Gwaii, British Columbia, Canada

2020 ◽  
Vol 133 (4) ◽  
pp. 352-363
Author(s):  
Neil G. Pilgrim ◽  
Joanna L. Smith ◽  
Keith Moore ◽  
Anthony J. Gaston
Keyword(s):  
British Columbia ◽  
Nest Site ◽  
Bird Species ◽  
Tree Height ◽  
Small Island ◽  
Haida Gwaii ◽  
Cavity Nesting ◽  
Site Characteristics ◽  
The Mean ◽  
Brown Creeper

Many studies of cavity-nesting birds in North America are conducted in large continental forests and much less is known about them in island ecosystems. We describe a 29-year study of tree species, nest site characteristics, and fledge dates of cavity-nesting birds on a small island in Haida Gwaii, British Columbia (BC). Seven cavity-nesting bird species were documented on East Limestone Island and 463 nests were found in 173 different trees. Nest trees were significantly taller and had a greater diameter than a random sample of snags. Tree height did not differ among bird species but diameter at breast height was larger for trees used by Brown Creeper (Certhia americana) than for other species. Cavity-nesters selected tree decay classes 2–7 (all dead/near dead [snags]), with 85% in decay class 4 (35%) or 5 (50%), similar to the random snag sample (class 4, 32%; class 5, 42%). Cavity height ranged from 2.6 to 44.9 m and for all species, except Brown Creeper, the mean nest height was >60% of the mean tree height. Nest heights were generally greater than observed elsewhere in BC. Nest cavity orientation was random except for Red-breasted Sapsuckers (Sphyrapicus ruber), for which only 13% of the cavity entrances faced southeast. Median fledging dates ranged from 7 June (Chestnut-backed Chickadee [Poecile rufescens]) to 28 June (Northern Flicker [Colaptes auratus]). Estimated median dates of clutch completion were similar for all species. Our results show that large snags provide habitat for a high diversity of cavity-nesting birds on Haida Gwaii.

Nest-site selection and nest design of Iberian bullfinches Pyrrhula pyrrhula iberiae in northwestern Spain

2021 ◽  
pp. 175815592110375
Author(s):  
Ángel Hernández ◽  
Pilar Zaldívar
Keyword(s):  
Site Selection ◽  
Nest Site ◽  
Temporal Variations ◽  
Nest Site Selection ◽  
Tree Cover ◽  
Site Characteristics ◽  
Nest Habitat ◽  
Northwestern Spain ◽  
Nest Design ◽  
First Time

Nest-habitat selection and nest design in a Eurasian bullfinch population in the Iberian Peninsula are thoroughly addressed in this study for the first time. Hedgerows and meadows were found around all of the nests and most of them were supported by hedgerows, so bullfinches consistently used the general woody vegetation available as reproduction habitat and site. Also, poplar plantations appeared preferentially in the immediate surroundings of the nests. Partly reflecting these results, bullfinches chose zones with greater shrub and tree cover than that available. Bullfinches placed their nests on a wide variety of plant species, but showed predilection for thorny species. Overall mean height of nests above the ground was 1.43 m and large-sized shrubs/trees were preferred. The most predominant bullfinch nest orientations were S, E and centered, which arguably provided thermal benefits and protected from severe weather. In general, there were no significant temporal variations in nest-site selection. With the exception of thorny support and favourable orientation, acting jointly, there was no significant association between nest-site characteristics and nesting success, presumably because many nests were already located in the most advantageous places at each time, and because despite this, predation pressure was high. Nest external dimensions were relatively variable, whereas internal width was the least variable nest dimension. No significant monthly or interannual variations in nest weight were observed. Larger nests did not hold larger clutches. Successful nests were larger than unsuccessful ones. The bullfinch nests were of simple construction, with two clearly different regions, the outer nest and the internal cup, with no significant temporal variations in the weight of either. The outer, structural nest consisted mainly of twigs, whereas roots and herbaceous shoots were the highest fractions lining the cup. Hair was the only animal-derived material used by bullfinches.

scholarly journals Multivariate techniques in the analysis of carcass traits of Morada Nova breed sheep

2018 ◽  
Vol 48 (9) ◽  
Author(s):  
Déborah Galvão Peixôto Guedes ◽  
Maria Norma Ribeiro ◽  
Francisco Fernando Ramos de Carvalho
Keyword(s):  
Total Variation ◽  
Principal Components ◽  
Strong Association ◽  
Principal Component ◽  
Original Data ◽  
Discriminatory Power ◽  
Multivariate Techniques ◽  
Data Set ◽  
Canonical Variables ◽  
And Performance

ABSTRACT: This study aimed to use multivariate techniques of principal component analysis and canonical discriminant analysis in a data set from Morada Nova sheep carcass to reduce the dimensions of the original data set, identify variables with the best discriminatory power among the treatments, and quantify the association between biometric and performance traits. The principal components obtained were efficient in reducing the total variation accumulated in 19 original variables correlated to five linear combinations, which explained 80% of the total variation present in the original variables. The first two principal components together accounted for 56.12% of the total variation of the evaluated variables. Eight variables were selected using the stepwise method. The first three canonical variables were significant, explaining 92.25% of the total variation. The first canonical variable showed a canonical correlation coefficient of 0.94, indicating a strong association between biometric traits and animal performance. Slaughter weight and hind width were selected because these variables presented the highest discriminatory power among all treatments, based on standard canonical coefficients.

SHORT PROKARYOTIC DNA FRAGMENT BINNING USING A HIERARCHICAL CLASSIFIER BASED ON LINEAR DISCRIMINANT ANALYSIS AND PRINCIPAL COMPONENT ANALYSIS

2010 ◽  
Vol 08 (06) ◽  
pp. 995-1011 ◽  
Author(s):  
HAO ZHENG ◽  
HONGWEI WU
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Simulated Data ◽  
Principal Component ◽  
Component Analysis ◽  
Dna Fragments ◽  
Data Set ◽  
Linear Discriminant ◽  
Hierarchical Classifier

Metagenomics is an emerging field in which the power of genomic analysis is applied to an entire microbial community, bypassing the need to isolate and culture individual microbial species. Assembling of metagenomic DNA fragments is very much like the overlap-layout-consensus procedure for assembling isolated genomes, but is augmented by an additional binning step to differentiate scaffolds, contigs and unassembled reads into various taxonomic groups. In this paper, we employed n-mer oligonucleotide frequencies as the features and developed a hierarchical classifier (PCAHIER) for binning short (≤ 1,000 bps) metagenomic fragments. The principal component analysis was used to reduce the high dimensionality of the feature space. The hierarchical classifier consists of four layers of local classifiers that are implemented based on the linear discriminant analysis. These local classifiers are responsible for binning prokaryotic DNA fragments into superkingdoms, of the same superkingdom into phyla, of the same phylum into genera, and of the same genus into species, respectively. We evaluated the performance of the PCAHIER by using our own simulated data sets as well as the widely used simHC synthetic metagenome data set from the IMG/M system. The effectiveness of the PCAHIER was demonstrated through comparisons against a non-hierarchical classifier, and two existing binning algorithms (TETRA and Phylopythia).

A Simulation Study of Factor Score Indeterminacy

1986 ◽  
Vol 23 (2) ◽  
pp. 111-118 ◽  
Author(s):  
Frank Acito ◽  
Ronald D. Anderson
Keyword(s):  
Principal Component Analysis ◽  
Principal Axis ◽  
Simulation Experiment ◽  
Factor Score ◽  
Principal Component ◽  
Multivariate Techniques ◽  
Research Practice ◽  
Factor Scores ◽  
Data Set ◽  
Factor Analytic

Though factor analysis continues to be one of the most frequently used multivariate techniques, its value has been questioned because of the indeterminacy of factor scores. The authors review the literature on factor score indeterminacy and discuss the implications of indeterminacy for research practice. A simulation experiment is used to investigate the effects of characteristics of the factor analytic data set on the accuracy of factor score estimation. Indeterminacy is found to depend critically on the level of communality and to be detected more accurately via image factoring than by principal axis or principal component analysis.

scholarly journals An Improved Comparison of Chemometric Analyses for the Identification of Acids and Bases With Colorimetric Sensor Arrays

2018 ◽  
Vol 10 (2) ◽  
pp. 36 ◽  
Author(s):  
Michael James Kangas ◽  
Christina L Wilson ◽  
Raychelle M Burks ◽  
Jordyn Atwater ◽  
Rachel M Lukowicz ◽  
...  
Keyword(s):  
Image Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Sensor Arrays ◽  
Principal Component ◽  
Colorimetric Sensor ◽  
Support Vector ◽  
Chemometric Methods ◽  
Data Set ◽  
Linear Discriminant

Colorimetric sensor arrays incorporating red, green, and blue (RGB) image analysis use value changes from multiple sensors for the identification and quantification of various analytes. RGB data can be easily obtained using image analysis software such as ImageJ. Subsequent chemometric analysis is becoming a key component of colorimetric array RGB data analysis, though literature contains mainly principal component analysis (PCA) and hierarchical cluster analysis (HCA). Seeking to expand the chemometric methods toolkit for array analysis, we explored the performance of nine chemometric methods were compared for the task of classifying 631 solutions (0.1 to 3 M) of acetic acid, malonic acid, lysine, and ammonia using an eight sensor colorimetric array. PCA and LDA (linear discriminant analysis) were effective for visualizing the dataset. For classification, linear discriminant analysis (LDA), (k nearest neighbors) KNN, (soft independent modelling by class analogy) SIMCA, recursive partitioning and regression trees (RPART), and hit quality index (HQI) were very effective with each method classifying compounds with over 90% correct assignments. Support vector machines (SVM) and partial least squares – discriminant analysis (PLS-DA) struggled with ~85 and 39% correct assignments, respectively. Additional mathematical treatments of the data set, such as incrementally increasing the exponents, did not improve the performance of LDA and KNN. The literature precedence indicates that the most common methods for analyzing colorimetric arrays are PCA, LDA, HCA, and KNN. To our knowledge, this is the first report of comparing and contrasting several more diverse chemometric methods to analyze the same colorimetric array data.

scholarly journals Customer Segment Prognostic System by Machine Learning using Principal Component and Linear Discriminant Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 6198-6203
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Customer Behavior ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Linear Discriminant ◽  
Customer Group

Recently, manufacturing industry faces lots of problem in predicting the customer behavior and group for matching their outcome with the profit. The organizations are finding difficult in identifying the customer behavior for the purpose of predicting the product design so as to increase the profit. The prediction of customer group is a challenging task for all the organization due to the current growing entrepreneurs. This results in using the machine learning algorithms to cluster the customer group for predicting the demand of the customers. This helps in decision making process of manufacturing the products. This paper attempts to predict the customer group for the wine data set extracted from UCI Machine Learning repository. The wine data set is subjected to dimensionality reduction with principal component analysis and linear discriminant analysis. A Performance analysis is done with various classification algorithms and comparative study is done with the performance metric such as accuracy, precision, recall, and f-score. Experimental results shows that after applying dimensionality reduction, the 2 component LDA reduced wine data set with the kernel SVM, Random Forest classifier is found to be effective with the accuracy of 100% compared to other classifiers.

scholarly journals A Bayesian Classification Intrusion Detection Method Based on the Fusion of PCA and LDA

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Zhidong Shen ◽  
Yuhao Zhang ◽  
Weiying Chen
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Principal Component ◽  
Bayesian Classification ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set ◽  
Linear Discriminant

The rapid development of network technology is facing severe security threats while bringing convenience to people. How to build a secure network environment has become an important guarantee for social development. Intrusion detection plays an important role in the field of network security. With the complexity and diversification of networks, intrusion detection systems also need to be constantly improved and developed to match external environmental changes. The innovative work of this paper is as follows: principal component analysis and linear discriminant analysis are used to reduce the dimensionality of the data set, which avoids unnecessary detection content and improves detection efficiency and accuracy. The principal component analysis method, linear discriminant analysis algorithm, and Bayesian classification are combined to construct the PCA-LDA-BC classification algorithm, and the intrusion detection model is established based on this algorithm. The simulation experiment was carried out on the algorithm CICIDS2017 data set proposed in this paper. From the experimental results, it can be analysed that in the intrusion detection of missing data, the improved algorithm is compared with the traditional naive Bayesian classification algorithm, the detection rate is improved, and the false detection rate and the missed alarm rate are reduced. In terms of intrusion detection for various types of attacks, the detection rate, false detection rate, and missed alarm rate have been improved accordingly. It is proved that the algorithm has certain validity and feasibility.

scholarly journals Surface Water Quality Assessment of Wular Lake, A Ramsar Site in Kashmir Himalaya, Using Discriminant Analysis and WQI

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Salim Aijaz Bhat ◽  
Ashok K. Pandit
Keyword(s):  
Water Quality ◽  
Discriminant Analysis ◽  
Quality Data ◽  
Stepwise Discriminant Analysis ◽  
Multivariate Techniques ◽  
Kashmir Himalaya ◽  
Water Quality Data ◽  
Data Set ◽  
Total N ◽  
The Mean

Multivariate techniques, discriminant analysis, and WQI were applied to analyze a water quality data set including 27 parameters at 5 sites of the Lake Wular in Kashmir Himalaya from 2011 to 2013 to investigate spatiotemporal variations and identify potential pollution sources. Spatial and temporal variations in water quality parameters were evaluated through stepwise discriminant analysis (DA). The first spatial discriminant function (DF) accounted for 76.5% of the total spatial variance, and the second DF accounted for 19.1%. The mean values of water temperature, EC, total-N, K, and silicate showed a strong contribution to discriminate the five sampling sites. The mean concentration of NO2-N, total-N, and sulphate showed a strong contribution to discriminate the four sampling seasons and accounted for most of the expected seasonal variations. The order of major cations and anions was Ca2+>Mg2+> Na+>K+ and Cl->SO42->SiO22- respectively. The results of water quality index, employing thirteen core parameters vital for drinking water purposes, showed values of 49.2, 46.5, 47.3, 40.6, and 37.1 for sites I, II, III, IV, and V, respectively. These index values reflect that the water of lake is in good condition for different purposes but increased values alarm us about future repercussions.

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