Habitat type impacts small mammal diversity in the Ukaguru Mountains, Tanzania

Mammalia ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Olaoluwa John Ademola ◽  
Apia W. Massawe ◽  
Loth S. Mulungu ◽  
Proches Hieronimo ◽  
Fortunatus B. S. Makonda ◽  
...  
Keyword(s):  
Small Mammals ◽  
Habitat Type ◽  
Principal Component ◽  
Habitat Disturbances ◽  
Live Trapping ◽  
Two Factors ◽  
The Mean ◽  
Vegetation Parameters ◽  
Disturbed Forest ◽  
Mammal Diversity

Abstract We assessed the habitat association of the diversity and abundance of small mammals in the Ukaguru Mountains within the Eastern Arc Mountains, Tanzania. An estimation of the vegetation parameters and live-trapping of small mammals for three consecutive nights per month for 21 months were carried out in farmland, disturbed and intact forests. A total of 1196 individual small mammals comprising 13 species were captured. Species diversity and evenness in intact forest were much higher compared to disturbed forest. Principal component analysis (PCA) explained 87.7% of the variance with two factors. The study suggests vegetation and habitat disturbances are factors responsible for the observed diversity of small mammals in the Ukaguru Mountains. Mastomys natalensis, Mus triton and Praomys delectorum accounted for 90.0% of total captures. M. triton and M. natalensis were the most abundant in farmland with 46.8 and 42.8% of total captures respectively and associated with the herbaceous vegetation. P. delectorum correlated with litter depth and trees and accounted for 90.0 and 80.0% total captures in disturbed and intact forests respectively but the mean abundances in both habitats were not significantly different (p = 0.72).

Two types of squalor: findings from a factor analysis of the Environmental Cleanliness and Clutter Scale (ECCS)

2013 ◽  
Vol 25 (7) ◽  
pp. 1191-1198 ◽  
Author(s):  
John Snowdon ◽  
Graeme Halliday ◽  
Glenn E. Hunt
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Latent Variables ◽  
Principal Component ◽  
Living Conditions ◽  
Project Team ◽  
Factor Solution ◽  
Hoarding Disorder ◽  
Two Factors ◽  
The Mean

ABSTRACTBackground: Most people who collect and hoard, and then have difficulty discarding items, do not live in squalor, even though accumulation of hoarded items can make cleaning very difficult. Commonly, people living in squalor accumulate garbage, but relatively few fulfill proposed criteria for “hoarding disorder.” We examined the overlap between hoarding and squalor among people referred because of unacceptable living conditions.Methods: Ongoing collection of data by a Squalor Project team, including ratings on the Environmental Cleanliness and Clutter Scale (ECCS), allowed (1) description of characteristics of cases and (2) examination of ratings of uncleanliness, and of the effect of accumulation of items or material on access within dwellings. Principal component analysis was used to examine latent variables underlying the ECCS.Results: The mean age of the referred occupants (108 male, 95 female) was 61.9 years. The mean ECCS score in 186 rated cases was 18.5. Factor analysis of ECCS data showed a two-factor solution as the most plausible. Factor 1, comprising seven squalor items, accounted for 33.7% of the variance. Factor 2 comprised reduced accessibility and accumulation of items of little value (variance 17.6%). Accumulation of garbage loaded equally on the two factors. High levels of squalor and/or accumulation were recorded in 105 (56%) of the 186 dwellings. One-third scored high on accumulation/hoarding, while 38% scored high on squalor; 15% scored high on both squalor and accumulation. A quarter of those scoring high on squalor scored low on hoarding/accumulation.Conclusions: The ECCS is useful when describing whether referred cases show high levels of squalor, hoarding, or both.

Multivariate analysis of the hybrid Phoxinus eos × Phoxinus neogaeus (Pisces: Cyprinidae): reevaluation of univariate analysis

1989 ◽  
Vol 67 (7) ◽  
pp. 1637-1641
Author(s):  
Gerard R. Joswiak ◽  
John G. New
Keyword(s):  
Univariate Analysis ◽  
Principal Component ◽  
The Other ◽  
Morphological Data ◽  
Multivariate Methods ◽  
Morphometric Measurements ◽  
Data Set ◽  
Principal Component Analyses ◽  
Two Factors ◽  
The Mean

Morphological data from two species of minnows, Phoxinus eos and Phoxinus neogaeus, and their hybrids (Pisces: Cyprinidae), previously analyzed by univariate methods, were reanalyzed by multivariate methods. Stepwise discriminant and principal component analyses were conducted on a data set of 14 morphometric measurements from 172 specimens. All of the Phoxinus neogaeus and 87% of both hybrids and Phoxinus eos were correctly classified on functions derived from six discriminating variables. Principal component analysis resulted in the extraction of two factors that explained 90% of the variation in the data set. Mean scores on the first factor were significantly different for P. eos, while on the second factor the mean score for P. neogaeus differed from those of the other two groups. Possible causes of variation in the morphology in the Phoxinus hybrids, from diploidy, tripoloidy, and mosaicism, are discussed.

scholarly journals Small Mammals in Forests of Romania: Habitat Type Use and Additive Diversity Partitioning

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1107
Author(s):  
Anamaria Lazăr ◽  
Ana Maria Benedek ◽  
Ioan Sîrbu
Keyword(s):  
Species Richness ◽  
Small Mammals ◽  
Small Mammal ◽  
Habitat Type ◽  
Tree Canopy ◽  
Natural Forests ◽  
Time Dimension ◽  
Small Mammal Communities ◽  
Mammal Communities ◽  
Mammal Diversity

Small mammals are key components of forest ecosystems, playing vital roles for numerous groups of forest organisms: they exert bottom-up and top-down regulatory effects on vertebrate and invertebrate populations, respectively; they are fungus- and seed-dispersers and bioturbators. Therefore, preserving or restoring the diversity of small mammal communities may help maintain the functions of these ecosystems. In Romania, a country with low-intensity forest management and a high percentage of natural forests compared to other European countries, an overview of forest small mammal diversity and habitat type use is lacking, and our study aimed to fill this gap. We also aimed to partition the total small mammal diversity of Romanian forests into the alpha (plot-level), beta, and delta (among forest types) diversities, as well as further partition beta diversity into its spatial (among plots) and temporal (among years) components. We surveyed small mammals by live trapping in eight types of forest across Romania. We found that small mammal abundance was significantly higher in lowland than in mountain forests, but species richness was similar, being associated with the diversity of tree canopy, with the highest values in mixed forests. In contrast, small mammal heterogeneity was related to overall habitat heterogeneity. As predicted, community composition was most distinct in poplar plantations, where forest specialists coexist with open habitat species. Most of the diversity was represented by alpha diversity. Because of strong fluctuations in population density of dominant rodents, the temporal component of beta heterogeneity was larger than the spatial component, but species richness also presented an important temporal turnover. Our results show the importance of the time dimension in the design of the surveys aiming at estimating the diversity of small mammal communities, both at the local and regional scales.

Terrestrial Small Mammal Communities from Hârtibaciu Plateau (Romania)/Comunităţi de mamifere mici terestre din Podişul Hârtibaciu (România)

2015 ◽  
Vol 57 (1) ◽  
pp. 57-67
Author(s):  
Ana Maria Benedek ◽  
Ioan Sîrbu ◽  
Anca Bucur ◽  
Victoria Cociş ◽  
Adrian Răulea ◽  
...  
Keyword(s):  
Small Mammals ◽  
Small Mammal ◽  
Habitat Type ◽  
Index Number ◽  
Habitat Types ◽  
Live Trapping ◽  
Small Mammal Communities ◽  
Mammal Communities ◽  
Number Of Individuals ◽  
Active Trap

Abstract Small mammal communities were studied by live trapping during August-September 2010 and June-September 2011 in three localities from Hârtibaciu Plateau, in southern Transylvania. The area is situated between 420 and 550 m a.s.l., and represents a mosaic of small patches of different land use. 200 traps were set in lines for three consecutive nights, in 80 different habitats representing 12 habitat types, both cultivated and semi-natural. 1235 small mammals belonging to 15 species (four soricomorphs and 11 rodents) were captured. Abundance of small mammals was expressed by means of capture index (number of individuals caught per 100 active trap-nights). The community structure was strongly shaped by habitat type, even in case of small land patches. Microtus arvalis prevailed in the investigated area, being the dominant species in open fields with high grassy vegetation. The density of this species increased strongly from the beginning of summer to autumn, when the traps were occasionally saturated with field voles.

Effects of an Anxiety-Specific Psychometric Factor on Fear Conditioning and Fear Generalization

2017 ◽  
Vol 225 (3) ◽  
pp. 200-213 ◽  
Author(s):  
Christian Baumann ◽  
Miriam A. Schiele ◽  
Martin J. Herrmann ◽  
Tina B. Lonsdorf ◽  
Peter Zwanzger ◽  
...  
Keyword(s):  
Risk Factor ◽  
Anxiety Disorders ◽  
Fear Conditioning ◽  
Longitudinal Design ◽  
Principal Component ◽  
Anxiety And Depression ◽  
Specific Factor ◽  
High Anxious ◽  
Two Factors ◽  
Fear Generalization

Abstract. Conditioning and generalization of fear are assumed to play central roles in the pathogenesis of anxiety disorders. Here we investigate the influence of a psychometric anxiety-specific factor on these two processes, thus try to identify a potential risk factor for the development of anxiety disorders. To this end, 126 healthy participants were examined with questionnaires assessing symptoms of anxiety and depression and with a fear conditioning and generalization paradigm. A principal component analysis of the questionnaire data identified two factors representing the constructs anxiety and depression. Variations in fear conditioning and fear generalization were solely associated with the anxiety factor characterized by anxiety sensitivity and agoraphobic cognitions; high-anxious individuals exhibited stronger fear responses (arousal) during conditioning and stronger generalization effects for valence and UCS-expectancy ratings. Thus, the revealed psychometric factor “anxiety” was associated with enhanced fear generalization, an assumed risk factor for anxiety disorders. These results ask for replication with a longitudinal design allowing to examine their predictive validity.

Principal component analysis of substituent constants

1990 ◽  
Vol 55 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Drahomír Hnyk
Keyword(s):  
Principal Component Analysis ◽  
Inductive Effect ◽  
Resonance Effect ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Data Matrix ◽  
Cumulative Proportion ◽  
Two Factors ◽  
Substituent Constants

The principal component analysis has been applied to a data matrix formed by 7 usual substituent constants for 38 substituents. Three factors are able to explain 99.4% cumulative proportion of total variance. Several rotations have been carried out for the first two factors in order to obtain their physical meaning. The first factor is related to the resonance effect, whereas the second one expresses the inductive effect, and both together describe 97.5% cumulative proportion of total variance. Their mutual orthogonality does not directly follow from the rotations carried out. With the help of these factors the substituents are divided into four main classes, and some of them assume a special position.

scholarly journals Towards Understanding Variability in Droughts in Response to Extreme Climate Conditions over the Different Agro-Ecological Zones of Pakistan

2021 ◽  
Vol 13 (12) ◽  
pp. 6910
Author(s):  
Adil Dilawar ◽  
Baozhang Chen ◽  
Arfan Arshad ◽  
Lifeng Guo ◽  
Muhammad Irfan Ehsan ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Principal Component ◽  
Mitigation Strategies ◽  
Precipitation Extremes ◽  
Climate Mitigation ◽  
Mean Annual Temperature ◽  
Climate Extreme ◽  
Ecological Zones ◽  
The Mean ◽  
Agro Ecological Zones

Here, we provided a comprehensive analysis of long-term drought and climate extreme patterns in the agro ecological zones (AEZs) of Pakistan during 1980–2019. Drought trends were investigated using the standardized precipitation evapotranspiration index (SPEI) at various timescales (SPEI-1, SPEI-3, SPEI-6, and SPEI-12). The results showed that droughts (seasonal and annual) were more persistent and severe in the southern, southwestern, southeastern, and central parts of the region. Drought exacerbated with slopes of −0.02, −0.07, −0.08, −0.01, and −0.02 per year. Drought prevailed in all AEZs in the spring season. The majority of AEZs in Pakistan’s southern, middle, and southwestern regions had experienced substantial warming. The mean annual temperature minimum (Tmin) increased faster than the mean annual temperature maximum (Tmax) in all zones. Precipitation decreased in the southern, northern, central, and southwestern parts of the region. Principal component analysis (PCA) revealed a robust increase in temperature extremes with a variance of 76% and a decrease in precipitation extremes with a variance of 91% in the region. Temperature and precipitation extremes indices had a strong Pearson correlation with drought events. Higher temperatures resulted in extreme drought (dry conditions), while higher precipitation levels resulted in wetting conditions (no drought) in different AEZs. In most AEZs, drought occurrences were more responsive to precipitation. The current findings are helpful for climate mitigation strategies and specific zonal efforts are needed to alleviate the environmental and societal impacts of drought.

scholarly journals Voxelwise Principal Component Analysis of Dynamic [S-Methyl-11C]Methionine PET Data in Glioma Patients

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2342
Author(s):  
Corentin Martens ◽  
Olivier Debeir ◽  
Christine Decaestecker ◽  
Thierry Metens ◽  
Laetitia Lebrun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Added Value ◽  
Time Activity ◽  
Positron Emission ◽  
Activity Curve ◽  
The Mean ◽  
Mean Time ◽  
Met Pet

Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas.

scholarly journals The Development of Athlete Performance Capabilities Index (APCI) Model for Male U12 Player Selection using Multivariate Analysis

2018 ◽  
Vol 7 (4.34) ◽  
pp. 97
Author(s):  
Mohamad Razali Abdullah ◽  
Hafizan Juahir ◽  
N. Mohamad Shukri ◽  
N. A. Fuat ◽  
N. A. Mohd Ros ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Factor Score ◽  
Principal Component ◽  
Factor Loading ◽  
Group Classification ◽  
Performance Group ◽  
Two Factors ◽  
Low Performance ◽  
High Factor Loading ◽  
Athlete Performance

This study develops an Athlete Performance Capabilities Index (APCI) model using multivariate analysis for selecting the best player of under twelve (U12).  Measurement of anthropometrics and physical fitness were evaluated among 178 male players aged 12±0.52 years. Factor score derived by Principal Component Analysis were used to obtain a model for APCI and Discriminant Analysis (DA) were conducted to validate the correctness of group classification by APCI. Result was found two factors with eigenvalues greater than 1 were extracted which accounted for 62.00% of the variations present in the original variables. The two factors were used to obtain the factor score coefficients explained by 35.72% and 26.67% of the variations in athlete performance respectively. Factor 1 revealed high factor loading on fitness compared to Factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing APCI ≥ 75%, 25% ≤ APCI < 75% and APCI < 25% as high, moderate and low performance group respectively. Three discriminated variables out of thirteen variables were obtained using Forward and Backward stepwise mode of DA, which were weight, standing broad jump, and 40 meters’ speed. Such variables were established as essential indicator for selecting the best player among male U12.   

scholarly journals STATISTICAL MULTIVARIATE ANALYSIS APPLIED TO ENVIRONMENTAL CHARACTERIZATION OF SOIL IN SEMIARID REGION1

2019 ◽  
Vol 32 (1) ◽  
pp. 200-210
Author(s):  
Antônio Italcy de Oliveira Júnior ◽  
Luiz Alberto Ribeiro Mendonça ◽  
Sávio de Brito Fontenele ◽  
Adriana Oliveira Araújo ◽  
Maria Gorethe de Sousa Lima Brito
Keyword(s):  
Organic Matter Content ◽  
Principal Component ◽  
Matter Content ◽  
Multivariate Statistical ◽  
Statistical Tool ◽  
Environmental Characterization ◽  
Hierarchical Grouping ◽  
Two Factors ◽  
Total Data

ABSTRACT Soil is a dynamic and complex system that requires a considerable number of samples for analysis and research purposes. Using multivariate statistical methods, favorable conditions can be created by analyzing the samples, i.e., structural reduction and simplification of the data. The objective of this study was to use multivariate statistical analysis, including factorial analysis (FA) and hierarchical groupings, for the environmental characterization of soils in semiarid regions, considering anthropic (land use and occupation) and topographic aspects (altitude, moisture, granulometry, PR, and organic-matter content). As a case study, the São José Hydrographic Microbasin, which is located in the Cariri region of Ceará, was considered. An FA was performed using the principal component method, with normalized varimax rotation. In hierarchical grouping analysis, the “farthest neighbor” method was used as the hierarchical criterion for grouping, with the measure of dissimilarity given by the “square Euclidean distance.” The FA indicated that two factors explain 75.76% of the total data variance. In the analysis of hierarchical groupings, the samples were agglomerated in three groups with similar characteristics: one with samples collected in an area of the preserved forest and two with samples collected in areas with more anthropized soils. This indicates that the statistical tool used showed sensitivity to distinguish the most conserved soils and soils with different levels of anthropization.

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