Comparative ecology of Quebec boreal forests: a numerical approach to modelling

1972 ◽  
Vol 50 (11) ◽  
pp. 2357-2379 ◽  
Author(s):  
Pierre Bellefleur ◽  
Allan N. Auclair
Keyword(s):  
Principal Components ◽  
Tree Species ◽  
Boreal Forests ◽  
Meteorological Parameters ◽  
Principal Component ◽  
Numerical Approach ◽  
Slope Aspect ◽  
Comparative Ecology ◽  
Environmental Models ◽  
Geographical Distances

Climatic and species–environmental models for seven regions of Quebec boreal and boreal-transition forest were developed using principal components factor analysis. Climatic models based on growth-relevant meteorological parameters indicated that despite large geographical distances, all regions were very similar. The first principal component in the climatic model accounted for 73% of the total variation in the original climatic variables. Two regions were relatively dissimilar, coinciding with differences in predominant air mass during the growth season.Analytical models of tree species and tree species with environmental variables were based on 1668 forest plots. Compositional and environmental models were similar as were major ecological species–environment relationships among the seven regions. Air and moisture drainage related to catena, natural stand disturbances related to specific edaphic parameters, and microclimate related to slope aspect accounted for 29.7%, 21.5%, and 17.4% of the variance on first, second, and third principal components, respectively. Validity of the models and problems for further investigation are discussed.

Microgeographic genetic variation of Sitka spruce in southeastern Alaska

1989 ◽  
Vol 19 (8) ◽  
pp. 1004-1013 ◽  
Author(s):  
Robert K. Campbell ◽  
William A. Pawuk ◽  
Arland S. Harris
Keyword(s):  
Genetic Variation ◽  
Standard Deviation ◽  
Principal Components ◽  
Principal Component ◽  
Extreme Case ◽  
Common Garden ◽  
Sitka Spruce ◽  
Slope Aspect ◽  
Factor Scores ◽  
Additive Genetic Variation

Microgeographic genetic variation among populations of Sitka spruce on Mitkof Island in southeastern Alaska is described. In two common-garden environments, we evaluated genotypes of 208 parent trees from 114 locations in a 17 000-ha area. Two principal components accounted for most of the variation among locations in 11 traits measured to evaluate growth vigor and rhythm of 2-year-old seedlings. Regression analyses of factor scores derived from principal components revealed genetic gradients associated with elevation, slope, aspect, and west–east and north–south direction. Large amounts of additive genetic variation in factor scores occurred among trees within locations. When this variation within locations was used as a scale, variation among locations was also large. In an extreme case, locations differed in factor scores of the first principal component by about 3.0 units of the standard deviation of additive genetic variation in factor scores. Of the total differentiation in this case, elevational range (600 m) contributed 0.7 units of standard deviation, aspect contributed 0.9 units, and distance (16 km) from north central to southeastern parts of the island contributed 1.4 units.

Structuring Assessments of Psychopathology

2006 ◽  
Vol 27 (2) ◽  
pp. 87-92 ◽  
Author(s):  
Willem K.B. Hofstee ◽  
Dick P.H. Barelds ◽  
Jos M.F. Ten Berge
Keyword(s):  
Principal Components ◽  
Personality Assessment ◽  
Clinical Sample ◽  
Principal Component ◽  
Normal Sample ◽  
Normative Sample ◽  
Assessment Data ◽  
Obsessive Compulsive ◽  
Oblique Rotation ◽  
Two Samples

Hofstee and Ten Berge (2004a) have proposed a new look at personality assessment data, based on a bipolar proportional (-1, .. . 0, .. . +1) scale, a corresponding coefficient of raw-scores likeness L = ΢XY/N, and raw-scores principal component analysis. In a normal sample, the approach resulted in a structure dominated by a first principal component, according to which most people are faintly to mildly socially desirable. We hypothesized that a more differentiated structure would arise in a clinical sample. We analyzed the scores of 775 psychiatric clients on the 132 items of the Dutch Personality Questionnaire (NPV). In comparison to a normative sample (N = 3140), the eigenvalue for the first principal component appeared to be 1.7 times as small, indicating that such clients have less personality (social desirability) in common. Still, the match between the structures in the two samples was excellent after oblique rotation of the loadings. We applied the abridged m-dimensional circumplex design, by which persons are typed by their two highest scores on the principal components, to the scores on the first four principal components. We identified five types: Indignant (1-), Resilient (1-2+), Nervous (1-2-), Obsessive-Compulsive (1-3-), and Introverted (1-4-), covering 40% of the psychiatric sample. Some 26% of the individuals had negligible scores on all type vectors. We discuss the potential and the limitations of our approach in a clinical context.

Comparison of Principal Component Solutions in Two Populations

Methodology ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Gregor Sočan
Keyword(s):  
Simulation Study ◽  
Principal Components ◽  
Common Factor ◽  
Principal Component ◽  
Component Model ◽  
Bootstrap Procedure ◽  
Factor Loadings ◽  
Component Loadings ◽  
Principal Component Model ◽  
Two Populations

Abstract. When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Evaluation functions for integral mapping

2017 ◽  
Vol 921 (3) ◽  
pp. 24-29 ◽  
Author(s):  
S.I. Lesnykh ◽  
A.K. Cherkashin
Keyword(s):  
Principal Component Analysis ◽  
Environmental Factors ◽  
Principal Components ◽  
Principal Component ◽  
Evaluation Function ◽  
Final Value ◽  
Evaluation Functions ◽  
Geographical Environment ◽  
Environmental Background ◽  
Integral Mapping

The proposed procedure of integral mapping is based on calculation of evaluation functions on the integral indicators (II) taking into account the feature of the local geographical environment, when geosystems in the same states in the different environs have various estimates. Calculation of II is realized with application of a Principal Component Analysis for processing of the forest database, allowing to consider in II the weight of each indicator (attribute). The final value of II is equal to a difference of the first (condition of geosystem) and the second (condition of environmental background) principal components. The evaluation functions are calculated on this value for various problems of integral mapping. The environmental factors of variability is excluded from final value of II, therefore there is an opportunity to find the invariant evaluation function and to determine coefficients of this function. Concepts and functions of the theory of reliability for making the evaluation maps of the hazard of functioning and stability of geosystems are used.

scholarly journals EXPRESS: Exploration of Principal Component Analysis: Deriving PCA Visually Using Spectra

2021 ◽  
pp. 000370282098784
Author(s):  
James Renwick Beattie ◽  
Francis Esmonde-White
Keyword(s):  
Principal Components ◽  
Principal Component ◽  
Original Data ◽  
Specific Chemical ◽  
Successive Refinement ◽  
Minimal Loss ◽  
Components Analysis ◽  
The Mathematical Model ◽  
Application Specific ◽  
Reconstructed Data

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal Components Analysis (PCA) is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning PCA is not well understood by many applied analytical scientists and spectroscopists who use PCA. The meaning of features identified through PCA are often unclear. This manuscript traces the journey of the spectra themselves through the operations behind PCA, with each step illustrated by simulated spectra. PCA relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of PCA, such the scores representing ‘concentration’ or ‘weights’. The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a PCA model shows how to interpret application specific chemical meaning of the PCA loadings and how to analyze scores. A critical benefit of PCA is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

scholarly journals Quantifying the Variability of Forest Ecosystem Vulnerability in the Largest Water Tower Region Globally

2021 ◽  
Vol 18 (14) ◽  
pp. 7529
Author(s):  
Siqi Sun ◽  
Yihe Lü ◽  
Da Lü ◽  
Cong Wang
Keyword(s):  
Forest Ecosystems ◽  
Human Impacts ◽  
Regional Scale ◽  
Principal Component ◽  
Tibet Plateau ◽  
Slope Aspect ◽  
Environmental Disturbances ◽  
Restoration Planning ◽  
Development Goals ◽  
Qinghai Tibet Plateau

Forests are critical ecosystems for environmental regulation and ecological security maintenance, especially at high altitudes that exhibit sensitivity to climate change and human activities. The Qinghai-Tibet Plateau—the world’s largest water tower region—has been breeding many large rivers in Asia where forests play important roles in water regulation and water quality improvement. However, the vulnerability of these forest ecosystems at the regional scale is still largely unknown. Therefore, the aim of this research is to quantitatively assess the temporal–spatial variability of forest vulnerability on the Qinghai-Tibet Plateau to illustrate the capacity of forests to withstand disturbances. Geographic information system (GIS) and the spatial principal component analysis (SPCA) were used to develop a forest vulnerable index (FVI) to assess the vulnerability of forest ecosystems. This research incorporates 15 factors covering the natural context, environmental disturbances, and socioeconomic impact. Results indicate that the measure of vulnerability was unevenly distributed spatially across the study area, and the whole trend has intensified since 2000. The three factors that contribute the most to the vulnerability of natural contexts, environmental disturbances, and human impacts are slope aspect, landslides, and the distance to the farmland, respectively. The vulnerability is higher in forest areas with lower altitudes, steeper slopes, and southerly directions. These evaluation results can be helpful for forest management in high altitude water tower regions in the forms of forest conservation or restoration planning and implementation towards sustainable development goals.

scholarly journals Wood-inhabiting fungal responses to forest naturalness vary among morpho-groups

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Purhonen Jenna ◽  
Abrego Nerea ◽  
Komonen Atte ◽  
Huhtinen Seppo ◽  
Kotiranta Heikki ◽  
...  
Keyword(s):  
Tree Species ◽  
Boreal Forests ◽  
Negative Impact ◽  
Important Variable ◽  
Host Tree ◽  
Conservation Areas ◽  
Fungal Community Composition ◽  
Forest Sites ◽  
Protected Forests ◽  
Host Tree Species

AbstractThe general negative impact of forestry on wood-inhabiting fungal diversity is well recognized, yet the effect of forest naturalness is poorly disentangled among different fungal groups inhabiting dead wood of different tree species. We studied the relationship between forest naturalness, log characteristics and diversity of different fungal morpho-groups inhabiting large decaying logs of similar quality in spruce dominated boreal forests. We sampled all non-lichenized fruitbodies from birch, spruce, pine and aspen in 12 semi-natural forest sites of varying level of naturalness. The overall fungal community composition was mostly determined by host tree species. However, when assessing the relevance of the environmental variables separately for each tree species, the most important variable varied, naturalness being the most important explanatory variable for fungi inhabiting pine and aspen. More strikingly, the overall species richness increased as the forest naturalness increased, both at the site and log levels. At the site scale, the pattern was mostly driven by the discoid and pyrenoid morpho-groups inhabiting pine, whereas at the log scale, it was driven by pileate and resupinate morpho-groups inhabiting spruce. Although our study demonstrates that formerly managed protected forests serve as effective conservation areas for most wood-inhabiting fungal groups, it also shows that conservation planning and management should account for group- or host tree -specific responses.

The factor structure of the GHQ-60 in a community sample

1988 ◽  
Vol 18 (1) ◽  
pp. 211-218 ◽  
Author(s):  
J. L. Vazquez-Barquero ◽  
P. Williams ◽  
J. F. Diez-Manrique ◽  
J. Lequerica ◽  
A. Arenal
Keyword(s):  
Factor Structure ◽  
Principal Components ◽  
Good Description ◽  
Social Performance ◽  
Community Sample ◽  
General Health Questionnaire ◽  
Principal Component ◽  
Northern Spain ◽  
Relative Operating Characteristic ◽  
Component Structure

SynopsisThe factor structure of the 60-item version of the General Health Questionnaire was explored, using data collected in a community study in a rural area of northern Spain. Six principal components, similar to those previously reported with this instrument, were found to provide a good description of the data structure.The 30-item and 12-item versions of the GHQ were then disembedded from the parent version, and further principal components analyses carried out. Again, the results were similar to previous studies: in each of the three versions analysed here, the two most important components represented a disturbance of mood (‘general dysphoria’)– including aspects of anxiety, depression and irritability– and a disturbance of social performance (‘social function/optimism’).The principal component structure of the GHQ-60 was then utilized to calculate factor scores, and these were compared with PSE ratings using Relative Operating Characteristic (ROC) analysis. While four of the six factors discriminated well (area under the ROC curve 0–75 or more) between PSE ‘cases’ and ‘non-cases’, only one, depressive thoughts, was a good discriminator between depressed and non-depressed PSE ‘cases’.

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