multivariate approaches
Recently Published Documents


TOTAL DOCUMENTS

148
(FIVE YEARS 34)

H-INDEX

22
(FIVE YEARS 3)

Author(s):  
Jacinto Elías Sedeño-Díaz ◽  
Eugenia López-López ◽  
A. Joseph Rodríguez-Romero ◽  
Karla Fierro Leos ◽  
Melissa Tiburcio Martínez ◽  
...  

2022 ◽  
pp. 395-431
Author(s):  
Duo Lin ◽  
Sufang Qiu ◽  
Yang Chen ◽  
Shangyuan Feng ◽  
Haishan Zeng

Author(s):  
Aadil Manzoor Nanda ◽  
Maqbool Yousuf ◽  
Parvaiz A. Tali ◽  
Zahoor Ul Hussan ◽  
Pervez Ahmed

Phytotaxa ◽  
2021 ◽  
Vol 523 (4) ◽  
pp. 273-283
Author(s):  
SUSANA E. FREIRE ◽  
MARIANA A. GROSSI ◽  
LAURA IHARLEGUI ◽  
CAMILA L. ABARCA ◽  
CLAUDIA MONTI ◽  
...  

Gamochaeta (Asteraceae, Gnaphalieae) consists of about 60 species primarily distributed in tropical and subtropical America. Gamochaeta americana and G. coarctata are closely related species that have been mainly differentiated by its phyllary apices, plant height, width of basal leaves, and involucre height. In order to evaluate whether G. americana and G. coarctata can be differentiated on a morphological basis, we performed a morphometric analysis. A matrix of 24 morphological characters and 99 specimens was analyzed using two multivariate approaches: Cluster Analysis and Principal Coordinate Analysis. Both, the dendrogram and the Principal Coordinate Analysis (PCoA), showed that the two species are not clearly distinguished. No discriminating morphological characters for the two species have been found. In conclusion, all available data support that G. coarctata should be considered a synonym of G. americana. Lectotype is designated for Gnaphalium purpureum var. macrophyllum, and G. americana is described and illustrated.


Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1074
Author(s):  
Salman A.H. Selmy ◽  
Salah H. Abd Abd Al-Aziz ◽  
Raimundo Jiménez-Ballesta ◽  
Francisco Jesús García-Navarro ◽  
Mohamed E. Fadl

A precise evaluation of soil quality (SQ) is important for sustainable land use planning. This study was conducted to assess soil quality using multivariate approaches. An assessment of SQ was carried out in an area of Dakhla Oasis using two methods of indicator selection, i.e., total data set (TDS) and minimum data set (MDS), and three soil quality indices (SQIs), i.e., additive quality index (AQI), weighted quality index (WQI), and Nemoro quality index (NQI). Fifty-five soil profiles were dug and samples were collected and analyzed. A total of 16 soil physicochemical parameters were selected for their sensitivity in SQ appraising to represent the TDS. The principal component analysis (PCA) was employed to establish the MDS. Statistical analyses were performed to test the accuracy and validation of each model, as well as to understand the relationship between the used methods and indices. The results of principal component analysis (PCA) showed that soil depth, gravel content, sand fraction, and exchangeable sodium percentage (ESP) were included in the MDS. High positive correlations (r ≥ 0.9) occurred between SQIs calculated using TDS and/or MDS under the three models. Moreover, the findings showed highly significant differences (p < 0.001) among SQIs within and between TDS and MDS. Approximately 80 to 85% of the total study area based on TDS, as well as 70 to 75%, according to MDS, were identified as suitable soils with slight limitations on soil quality grade (Q3, Q2, and Q1), while the remaining 20 to 30% had high to severe limitations (Q4 and Q5). The highest sensitivity (SI = 2.9) occurred by applying WQI using MDS and indicator weights based on the variance of PCA. Furthermore, the highest linear regression value (R2 = 0.88) between TDS and MDS was recorded using the same model. Because of its high sensitivity, such a model could be used for monitoring SQ changes caused by agricultural practices and environmental factors. The findings of this study have significant guiding implications and practical value in assessing the soil quality using TDS and MDS in arid areas critically and accurately.


2021 ◽  
Author(s):  
Andrea Magnini ◽  
Michele Lombardi ◽  
Simone Persiano ◽  
Antonio Tirri ◽  
Francesco Lo Conti ◽  
...  

Abstract. Recent literature shows several examples of simplified approaches that perform flood hazard (FH) assessment and mapping across large geographical areas on the basis of fast-computing geomorphic descriptors. These approaches may consider a single index (univariate) or use a set of indices simultaneously (multivariate). What is the potential and accuracy of multivariate approaches relative to univariate ones? Can we effectively use these methods for extrapolation purposes, i.e. FH assessment outside the region used for setting up the model? Our study addresses these open problems by considering two separate issues: (1) mapping flood-prone areas, and (2) predicting the expected water depth for a given inundation scenario. We blend seven geomorphic descriptors through Decision Tree models trained on target FH maps, referring to a large study area (≈105 km2). We discuss the potential of multivariate approaches relative to the performance of a selected univariate model and on the basis of multiple extrapolation experiments, where models are tested outside their training region. Our results show that multivariate approaches may (a) significantly enhance flood-prone area delineation (overall accuracy: 93 %) relative to univariate ones (overall accuracy: 84 %), (b) provide accurate predictions of expected inundation depths (determination coefficient ≈0.7), and (c) produce encouraging results in extrapolation.


2021 ◽  
Vol 8 ◽  
Author(s):  
Kristen L. Omori ◽  
Cindy A. Tribuzio ◽  
Elizabeth A. Babcock ◽  
John M. Hoenig

International and national laws governing the management of living marine resources generally require specification of harvest limits. To assist with the management of data-limited species, stocks are often grouped into complexes and assessed and managed as a single unit. The species that comprise a complex should have similar life history, susceptibility to the fishing gear, and spatial distribution, such that common management measures will likely lead to sustainable harvest of all species in the complex. However, forming complexes to meet these standards is difficult due to the lack of basic biological or fisheries data to inform estimates of biological vulnerability and fishery susceptibility. A variety of cluster and ordination techniques are applied to bycatch rockfish species in the Gulf of Alaska (GOA) as a case study to demonstrate how groupings may differ based on the multivariate techniques used and the availability and reliability of life history, fishery independent survey, and fishery catch data. For GOA rockfish, our results demonstrate that fishing gear primarily defined differences in species composition, and we suggest that these species be grouped by susceptibility to the main fishing gears while monitoring those species with high vulnerabilities to overfishing. Current GOA rockfish complex delineations (i.e., Other Rockfish and Demersal Shelf Rockfish) are consistent with the results of this study, but should be expanded across the entire GOA. Differences observed across species groupings for the variety of data types and multivariate approaches utilized demonstrate the importance of exploring a diversity of methods. As best practice in identifying species complexes, we suggest using a productivity-susceptibility analysis or expert judgment to begin groupings. Then a variety of multivariate techniques and data sources should be used to identify complexes, while balancing an appropriate number of manageable groups. Thus, optimal species complex groupings should be determined by commonality and consistency among a variety of multivariate methods and datasets.


2021 ◽  
Vol 16 ◽  
pp. 457-468
Author(s):  
Saoussan Bouchareb ◽  
Mohamed Salah Chiadmi ◽  
Fouzia Ghaiti

In our study we use the univariate and multivariate GARCH models to analyze the volatility behavior of the daily data of four Mediterranean stock markets (Morocco, Turkey, Spain, and France) spanning the period 2000-2020. We find a strong evidence of persisting of volatility in each of these markets. Results also indicate that both the univariate and the multivariate approaches capture well the ARCH and GARCH effects. We analyze the conditional covariances, and co-volatility spillovers between the Moroccan stock market and the three other Mediterranean stock markets. In order to study co-volatility spillovers, our work is built on the diagonal BEKK model especially the conditional covariances.


Sign in / Sign up

Export Citation Format

Share Document