scholarly journals Do Anthropogenic Activities Affect Floristic Diversity and Vegetation Structure More Than Natural Soil Properties in Hyper-Arid Desert Environments?

Diversity ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 157
Author(s):  
Ethar A. Hussein ◽  
Monier M. Abd El-Ghani ◽  
Rim S. Hamdy ◽  
Lamiaa F. Shalabi
Keyword(s):  
Cluster Analysis ◽  
Plant Species ◽  
Land Degradation ◽  
Human Activities ◽  
Solid Wastes ◽  
Anthropogenic Pressure ◽  
Data Set ◽  
Α Diversity ◽  
Arid Desert ◽  
Unprotected Area

Egypt is characterized by its hyper-arid desert environment with high temperature, scanty rainfall, high evapotranspiration rate, and patchy scattered precipitation-dependent vegetation. Located in this peculiar ecosystem, the northeastern part of the eastern desert occupies vast areas where this study was conducted. Despite some protection in this area, destruction of plant cover, soil erosion, and degradation of natural habitat are still occurring. Among the complex array of anthropogenic disturbances that directly affect species diversity, over-grazing, road construction, over-collection of plants, salinization, over-cutting, military activities, urbanization, and industrialization were encountered. The aim of this study was to assess the effect on long-lasting anthropopressure on the current floristic and ecological status of the unprotected area in comparison to the protected one. Two areas were chosen for detailed studies: protected (Wadi Degla; WD) and unprotected (Cairo-Suez road, SR). Fourteen soil variables were used to assess the soil–vegetation relationships in the two areas. An assessment of seven human activities (over-grazing, over-collection, introduced species, land degradation, urbanization, solid wastes, and military activities) was carried out at four levels of disturbance intensities. A floristic presence/absence data set of 25 plots × 56 species, including 14 plots for SR and 11 plots for WD, was employed in the analyses. The application of multivariate analysis techniques such as cluster analysis (for classification), indicator species analysis (ISA) and the multi-response permutation procedure (MRPP), canonical correspondence analysis (CCA), and redundancy analysis (RDA) for ordination were performed in the data analysis. Generally, a total of 85 plant species belonging to 68 genera and 30 families was recorded. Asteraceae, Chenopodiaceae, Fabaceae, Zygophyllaceae, Poaceae, Brassicaceae, and Geraniaceae were the largest families, constituting more than 50% of the total flora. Chamaephytes, therophytes, hemicryptophytes, and phanerophytes prevail in the life form spectrum. Chorological analysis showed that the Saharo-Arabian element, whether pure or combined with other chorotypes, dominated the current flora, whereas the Mediterranean chorotype was very poorly represented. Application of cluster analysis yielded eight vegetation groups: I–IV for the Cairo-Suez road, and V–VIII for Wadi Degla. This study indicated the disappearance of several plant communities that were previously of common occurrence such as Retama raetam, Anabasis articulata, Ephedra alata, Artemisia monosperma, Zygophyllum decumbens, Lasiurus hirsutus, and Panicum turgidum. Partial CCA (pCCA) for the unprotected area revealed that most of the variance (45.7%) was attributed to the anthropogenic variables more than soil factors (14.5%). Like what was revealed in other unprotected areas, a clear relationship between anthropogenic pressure and habitat fragmentation was observed. Long-term, intensive human activities caused vegetation degradation, species loss, and a decline in plant richness. Hence, the highest species richness value was recorded in the protected area. Over-grazing, land degradation, and military activities were not correlated with the diversity indices, whereas over-collection of plant species, urbanization, and solid wastes were significantly negatively correlated with both α-diversity and the Shannon–Wiener index. Suitable protection measures should be taken to reduce the anthropogenic pressures in this ecosystem as well as some conservation programs and management plans should be implemented to save biodiversity.

scholarly journals Effect of anthropogenic pressure on grasshopper (Orthoptera: Acridomorpha) species diversity in three forests in southern Cameroon

2020 ◽  
Vol 29 (1) ◽  
pp. 25-34
Author(s):  
Charly Oumarou Ngoute ◽  
Sévilor Kekeunou ◽  
Michel Lecoq ◽  
Armand Richard Nzoko Fiemapong ◽  
Philène Corine Aude Um Nyobe ◽  
...  
Keyword(s):  
Species Diversity ◽  
Human Activities ◽  
Diversity Index ◽  
Forest Degradation ◽  
Distribution Model ◽  
Anthropogenic Pressure ◽  
Grasshopper Species ◽  
Α Diversity ◽  
Southern Cameroon ◽  
The Impact

Grasshoppers are highly diversified in tropical rainforests and considered of both ecological and conservation importance. The population dynamics of central African grasshoppers, however, and the structure of their communities remain poorly studied. We report here on the impact of human activities on the diversity of grasshopper species from three localities in southern Cameroon: Ongot, more anthropized forest; Zamakoe, moderately anthropized forest; and Ngutadjap, less anthropized forest. Data were collected using sweep nets, quadrats, and pitfall traps. We analyzed how pressures from human activities affected the grasshopper species compositions using five statistical methods: (1) two non-parametric estimators for specific richness, (2) abundance, (3) abundance distribution model, (4) α diversity index, and (5) β diversity index. The results showed no significant differences in species richness between the sites (nine species at Zamakoe, seven each at Ongot and Ngutadjap). Among these species, one was specific to Ongot and Zamakoe, while one, two, and three species, respectively, were found only in Ongot, Ngutadjap, and Zamakoe. Abundance and species diversity of grasshoppers increased with anthropogenic pressure on the forests. We noticed a great similarity between the grasshopper communities of the two localities under the greatest anthropogenic pressure (Ongot and Zamakoe) compared to that of the less anthropized locality of Ngutadjap. The most common grasshopper species, Mazea granulosa, was most abundant where deforestation was highest. Species diversity was highest in the more and moderately anthropized forests, and the diversity index showed greater similarity between these two grasshopper communities compared with that of the less anthropized forest. This work enables us to better understand how the parameters of these insect communities reflect the degree of forest degradation in southern Cameroon.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Empirical Evaluation of Genetic Clustering Methods Using Multilocus Genotypes From 20 Chicken Breeds

Genetics ◽  
2001 ◽  
Vol 159 (2) ◽  
pp. 699-713
Author(s):  
Noah A Rosenberg ◽  
Terry Burke ◽  
Kari Elo ◽  
Marcus W Feldman ◽  
Paul J Freidlin ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Population Structure ◽  
Clustering Algorithm ◽  
Empirical Evaluation ◽  
Unknown Origin ◽  
Clustering Methods ◽  
Genetic Cluster ◽  
Data Set ◽  
Multilocus Genotypes ◽  
Chicken Breeds

Abstract We tested the utility of genetic cluster analysis in ascertaining population structure of a large data set for which population structure was previously known. Each of 600 individuals representing 20 distinct chicken breeds was genotyped for 27 microsatellite loci, and individual multilocus genotypes were used to infer genetic clusters. Individuals from each breed were inferred to belong mostly to the same cluster. The clustering success rate, measuring the fraction of individuals that were properly inferred to belong to their correct breeds, was consistently ~98%. When markers of highest expected heterozygosity were used, genotypes that included at least 8–10 highly variable markers from among the 27 markers genotyped also achieved >95% clustering success. When 12–15 highly variable markers and only 15–20 of the 30 individuals per breed were used, clustering success was at least 90%. We suggest that in species for which population structure is of interest, databases of multilocus genotypes at highly variable markers should be compiled. These genotypes could then be used as training samples for genetic cluster analysis and to facilitate assignments of individuals of unknown origin to populations. The clustering algorithm has potential applications in defining the within-species genetic units that are useful in problems of conservation.

scholarly journals Phytosociological surveys: tools for weed science?

2013 ◽  
Vol 31 (2) ◽  
pp. 469-482 ◽  
Author(s):  
G. Concenço ◽  
M. Tomazi ◽  
I.V.T. Correia ◽  
S.A. Santos ◽  
L. Galon
Keyword(s):  
Cluster Analysis ◽  
Plant Species ◽  
Plant Community ◽  
Plant Communities ◽  
Distinct Group ◽  
Weed Science ◽  
Comprehensive Overview ◽  
Ecological Evaluation ◽  
Natural Plant ◽  
Ecological Basis

In simple terms, a phytosociological survey is a group of ecological evaluation methods whose aim is to provide a comprehensive overview of both the composition and distribution of plant species in a given plant community. To understand the applicability of phytosociological surveys for weed science, as well as their validity, their ecological basis should be understood and the most suitable ones need to be chosen, because cultivated fields present a relatively distinct group of selecting factors when compared to natural plant communities. For weed science, the following sequence of steps is proposed as the most suitable: (1) overall infestation; (2) phytosociological tables/graphs; (3) intra-characterization by diversity; (4) inter-characterization and grouping by cluster analysis. A summary of methods is established in order to assist Weed Science researchers through their steps into the realm of phytosociology.

scholarly journals Analysis of land cover change and its driving forces in a desert oasis landscape of Xinjiang, northwest China

Solid Earth ◽  
2014 ◽  
Vol 5 (2) ◽  
pp. 1071-1085 ◽  
Author(s):  
T. Amuti ◽  
G. Luo
Keyword(s):  
Land Cover ◽  
Land Degradation ◽  
Driving Forces ◽  
Northwest China ◽  
Landsat Images ◽  
Desert Oasis ◽  
Standard Classification ◽  
Effects Of Drought ◽  
Arid Desert ◽  
Southern Xinjiang

Abstract. The combined effects of drought, warming and the changes in land cover have caused severe land degradation for several decades in the extremely arid desert oases of southern Xinjiang, northwest China. Land cover classifications of Landsat images in 1990, 2000 and 2008 were performed based on the multistage supervised classification scheme using the maximum likelihood classifier integrated with conventional vegetation and soil indexes, which improved overall accuracies by 4–5% compared to the standard classification method. Based on the detection of changes in land cover during 1990–2008 using remote sensing (RS) and a geographic information system (GIS), it can be found that the oasis significantly (+35%) increased, while the area of ecotone decreased (−43%). The major trends of the land cover changes were the notable growth of the oasis and the reduction of the desert–oasis ecotone. These changes were mainly a result of the intensified human activities such as land and water exploitation as well as overgrazing. The results of this study indicate that the oasis environment will be deteriorated by increase in potential areas of land degradation if the trend of desert moving further inward and the shrinking of the ecotone continues over the next decades.

scholarly journals Cluster analysis of the results of intraoperative optical spectroscopic diagnostics In brain glioma neurosurgery

2019 ◽  
Vol 7 (4) ◽  
pp. 23-34
Author(s):  
I. A. Osmakov ◽  
T. A. Savelieva ◽  
V. B. Loschenov ◽  
S. A. Goryajnov ◽  
A. A. Potapov
Keyword(s):  
Cluster Analysis ◽  
Optical Spectroscopy ◽  
Protoporphyrin Ix ◽  
Spectroscopy Data ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Broadband Radiation ◽  
Degree Of Malignancy ◽  
Normal White Matter

The paper presents the results of a comparative study of methods of cluster analysis of optical intraoperative spectroscopy data during surgery of glial tumors with varying degree of malignancy. The analysis was carried out both for individual patients and for the entire dataset. The data were obtained using combined optical spectroscopy technique, which allowed simultaneous registration of diffuse reflectance spectra of broadband radiation in the 500–600 nm spectral range (for the analysis of tissue blood supply and the degree of hemoglobin oxygenation), fluorescence spectra of 5‑ALA induced protoporphyrin IX (Pp IX) (for analysis of the malignancy degree) and signal of diffusely reflected laser light used to excite Pp IX fluorescence (to take into account the scattering properties of tissues). To determine the threshold values of these parameters for the tumor, the infltration zone and the normal white matter, we searched for the natural clusters in the available intraoperative optical spectroscopy data and compared them with the results of the pathomorphology. It was shown that, among the considered clustering methods, EM‑algorithm and k‑means methods are optimal for the considered data set and can be used to build a decision support system (DSS) for spectroscopic intraoperative navigation in neurosurgery. Results of clustering relevant to thepathological studies were also obtained using the methods of spectral and agglomerative clustering. These methods can be used to postprocess combined spectroscopy data.

scholarly journals Finding of rare species of plats in the southern part of the Sinyukha river basin

2021 ◽  
pp. 37-45
Author(s):  
K. V. Zhulenko
Keyword(s):  
Protected Areas ◽  
River Basin ◽  
Plant Species ◽  
Current Distribution ◽  
Rare Species ◽  
Rare Plant ◽  
Anthropogenic Pressure ◽  
Natural Habitats ◽  
Rare Plant Species ◽  
New Localities

Introduction. The Sinyukha river basin, in particular its southern part, is an area with a high level of anthropogenic pressure and a significant level of agricultural development (the proportion of agricultural land is more than 80%), with fragmented natural habitats. Detailed chorological study is needed to supplement the pattern of the distribution of rare plant species, to develop measures for their conservation, to optimize the existing network of protected areas in the region.Рurpose of the study isto analyze the current distribution and describe new finds of some rare plant species in the southern part of the Sinyukha river basin.Methods. The research was conducted in April-June 2021. We surveyed the area of the Sinyukha river valley from the village of Kalamazovo (Vilshansky district, Kirovohrad region) to its confluence with the Southern Bug River in Pervomaisk (Mykolayiv region), as well as – the valleys of its tributaries – Chorny Tashlyk, Malyi Tashlyk and Sukhyi Tashlyk. When locating a rare species, the plants were photographed and georeferenced at a point with GPS-navigator. Species cover is given according to the Broun-Blanquet scale. The distribution maps were performed by free QGIS software.Results.We revealed new and confirmed known localities of 20 rare species:Adonis vernalis, Asplenium septentrionale, Astragalus dasyanthus, A. odessanus, Bellevalia sarmatica, Clematis integrifolia, Crocus reticulatus, Dianthus hypanicus, Ephedra distachya, Hyacinthella leucophaea, Iris pontica, Iris pumila, Ornithogalum boucheanum, Pulsatilla pratensis, Primula veris, Sedum borissovae, Stipa capillata, S. lessingiana, S. pennata, Tulipa hypanica. Among the 20 identified rare species one has the category VU (Vulnerable) in the IUCN red list and belongs to the list of Resolution 6 of the Berne Convention; three species are narrowly local endemics of the Dnieper Upland; 11 are listed in the Red Book of Ukraine (5 of them have the status vulnerable, 1 – rare, 5 – insufficiently known); 5 species are regionally rare in Kirovohrad and 8 – in Mykolayiv regions. Most of the revealed species have a cover less than 5%. Only 9 of the 20 registered rare species characterized by more than five localities within the studied area. Originality. New localities of 20 rare species of plants of different levels of protection have been revealed. Prospects for conservation valuableof their habitats are offered.Conclusion. We have identified a significant number of new localities of rare plant species that are not covered by proper protection. This indicates the need for more detailed chorological research to elucidate the current distribution of rare species and the creation of new protected areas. Key words:rare species; threat category; red lists; natural habitats; chorology.

Exploring the Unknown Nature of Data

2010 ◽  
pp. 1-27
Author(s):  
Rui Xu ◽  
Donald C. Wunsch II
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Data Structures ◽  
Clustering Algorithms ◽  
Ground Truth ◽  
Human Beings ◽  
Data Set ◽  
Learning Capabilities ◽  
Good Learning ◽  
Hidden Data

To classify objects based on their features and characteristics is one of the most important and primitive activities of human beings. The task becomes even more challenging when there is no ground truth available. Cluster analysis allows new opportunities in exploring the unknown nature of data through its aim to separate a finite data set, with little or no prior information, into a finite and discrete set of “natural,” hidden data structures. Here, the authors introduce and discuss clustering algorithms that are related to machine learning and computational intelligence, particularly those based on neural networks. Neural networks are well known for their good learning capabilities, adaptation, ease of implementation, parallelization, speed, and flexibility, and they have demonstrated many successful applications in cluster analysis. The applications of cluster analysis in real world problems are also illustrated. Portions of the chapter are taken from Xu and Wunsch (2008).

Cluster Analysis with Various Algorithms for Mixed Data

2017 ◽  
pp. 282-317
Author(s):  
Abha Sharma ◽  
R. S. Thakur
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithms ◽  
Complex Problem ◽  
Experimental Results ◽  
Mixed Data ◽  
Data Set ◽  
Fuzzy C Means ◽  
Conversion Method ◽  
Inherent Structure ◽  
Numeric Data

Analyzing clustering of mixed data set is a complex problem. Very useful clustering algorithms like k-means, fuzzy c-means, hierarchical methods etc. developed to extract hidden groups from numeric data. In this paper, the mixed data is converted into pure numeric with a conversion method, the various algorithm of numeric data has been applied on various well known mixed datasets, to exploit the inherent structure of the mixed data. Experimental results shows how smoothly the mixed data is giving better results on universally applicable clustering algorithms for numeric data.

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