Reciprocal Averaging and Polar Ordination as Techniques for Analyzing Lotic Macroinvertebrate Communities

1980 ◽  
Vol 37 (9) ◽  
pp. 1358-1364 ◽  
Author(s):  
Joseph M. Culp ◽  
Ronald W. Davies
Keyword(s):  
Cluster Analysis ◽  
Field Data ◽  
Environmental Gradient ◽  
Environmental Data ◽  
Macroinvertebrate Communities ◽  
Reciprocal Averaging ◽  
Spatial And Temporal Patterns ◽  
Polar Ordination ◽  
Ordination Techniques ◽  
Group Relationships

Reciprocal averaging and polar ordination techniques were applied to lotic macroinvertebrate field data to determine the relative performance of these techniques in lotic benthic community analyses. It was found that, because of inherent problems with endpoint determinations, polar ordination should only be used where a well-defined environmental gradient exists. Reciprocal averaging ordination produced interpretable axes and was preferred over polar ordination because endpoint determination was objective and simultaneous species–site ordinations were produced. Reciprocal averaging ordination allowed groupings of sites similar to those determined through cluster analysis to be recognized, while providing a visual representation of between-group relationships superior to that of cluster dendrograms. Combined with subsequent analyses of environmental data, reciprocal averaging ordination can be an excellent technique for summarizing spatial and temporal patterns of lotic macroinvertebrate communities.Key words: ordination, reciprocal averaging, benthic, lotic, communities, clustering

Integration of surface-based tomographic models for zonation and multimodel guided extrapolation of sparsely known petrophysical parameters

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. EN43-EN53 ◽  
Author(s):  
Barbara Hachmöller ◽  
Hendrik Paasche
Keyword(s):  
Cluster Analysis ◽  
Field Data ◽  
Spectral Cluster ◽  
P Wave ◽  
Fuzzy Membership ◽  
Geophysical Model ◽  
Natural Gamma ◽  
Refraction Seismic ◽  
Spatially Varying ◽  
Membership Information

We integrate the information of multiple tomographic models acquired from the earth’s surface by modifying a statistical approach recently developed for the integration of cross-borehole tomographic models. In doing so, we introduce spectral cluster analysis as the new core of the model integration procedure to capture the spatial heterogeneity present in all considered tomographic models and describe this heterogeneity in a fuzzy sense. Because spectral cluster algorithms analyze model structure locally, they are considered relatively robust with regard to systematically and spatially varying imaging capabilities typical for geophysical tomographic surveys conducted on the earth’s surface. Using a synthetic aquifer example, a fuzzy spectral cluster algorithm can be used to integrate the information provided by 2D tomographic refraction seismic and DC resistivity surveys. The integrated information in the fuzzy membership domain is then used to derive an integrated zonal geophysical model outlining the major structural units present in both input models. We also explain how the fuzzy membership information can be used to identify optimal locations for sparse logging of additional target parameters, i.e., porosity information in our synthetic example. We demonstrate how this sparse porosity information can be extrapolated based on all tomographic input models. The resultant 2D porosity model matches the original porosity distribution reasonably well within the spatial resolution limits of the underlying tomographic models. Consecutively, we apply this approach to a field data base acquired over a former river channel. Sparse information about natural gamma radiation is available and extrapolated on the basis of the fuzzy membership information obtained by spectral cluster analysis of 2D P-wave velocity and electrical resistivity models. This field data shows that the presented parameter extrapolation procedure is robust, even if the locations of target parameter acquisition have not been optimized with regard to the fuzzy membership information.

A Comparative Study of Reciprocal Averaging and Other Ordination Techniques

1977 ◽  
Vol 65 (1) ◽  
pp. 157 ◽  
Author(s):  
H. G. Gauch Jr. ◽  
R. H. Whittaker ◽  
T. R. Wentworth
Keyword(s):  
Comparative Study ◽  
Reciprocal Averaging ◽  
Ordination Techniques

scholarly journals Some Considerations and Results of Specialized Pattern Cluster Analysis with FUNDOS (PPCLUSTF) for Marine Engine Field Data

1981 ◽  
Vol 16 (10) ◽  
pp. 873-882
Author(s):  
Takeshi Hashimoto ◽  
Hitoshi Kaneko ◽  
Masahiro Sada ◽  
Kazusada Ikeda
Keyword(s):  
Cluster Analysis ◽  
Field Data ◽  
Marine Engine

scholarly journals Mapping near surface global marine ecosystems through cluster analysis of environmental data

2020 ◽  
Vol 35 (2) ◽  
pp. 327-342 ◽  
Author(s):  
Qianshuo Zhao ◽  
Zeenatul Basher ◽  
Mark J. Costello
Keyword(s):  
Cluster Analysis ◽  
Marine Ecosystems ◽  
Environmental Data ◽  
Near Surface

scholarly journals Spatial and temporal patterns of human Puumala virus (PUUV) infections in Germany

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4255 ◽  
Author(s):  
Sarah Cunze ◽  
Judith Kochmann ◽  
Thomas Kuhn ◽  
Raphael Frank ◽  
Dorian D. Dörge ◽  
...  
Keyword(s):  
Temporal Patterns ◽  
Climatic Conditions ◽  
Reservoir Host ◽  
Human Infection ◽  
Environmental Data ◽  
Myodes Glareolus ◽  
Puumala Virus ◽  
Spatial And Temporal Patterns ◽  
Bank Voles ◽  
Spatio Temporal

Background Worldwide, the number of recorded human hantavirus infections as well as the number of affected countries is on the rise. In Europe, most human hantavirus infections are caused by the Puumala virus (PUUV), with bank voles (Myodes glareolus) as reservoir hosts. Generally, infection outbreaks have been related to environmental conditions, particularly climatic conditions, food supply for the reservoir species and land use. However, although attempts have been made, the insufficient availability of environmental data is often hampering accurate temporal and spatially explicit models of human hantavirus infections. Methods In the present study, dynamics of human PUUV infections between 2001 and 2015 were explored using ArcGIS in order to identify spatio-temporal patterns. Results Percentage cover of forest area was identified as an important factor for the spatial pattern, whereas beech mast was found explaining temporal patterns of human PUUV infections in Germany. High numbers of infections were recorded in 2007, 2010 and 2012 and areas with highest records were located in Baden-Wuerttemberg (southwest Germany) and North Rhine-Westphalia (western Germany). Conclusion More reliable data on reservoir host distribution, pathogen verification as well as an increased awareness of physicians are some of the factors that should improve future human infection risk assessments in Germany.

Extent and pattern of genetic diversity in linseed(Linum usitatissimum L) under irrigated ecology

2018 ◽  
Vol 5 (01) ◽  
Author(s):  
M. P. CHAUHAN ◽  
H. K. SINGH ◽  
JAY KUMAR YADAV ◽  
M K. MAURYA
Keyword(s):  
Genetic Diversity ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Analysis Of Variance ◽  
Linum Usitatissimum ◽  
Morphological Traits ◽  
Field Data ◽  
Relative Importance ◽  
Linum Usitatissimum L ◽  
Phenotypic Divergence

Sixty six genotypes of linseed were analysed for the morphological traits to investigate the genetic diversity between and within the genotypes. The field data was initially subjected to analysis of variance. There were highly significant differences among the genotypes for all the traits indicating the presence of variability among the genotypes and the possibility to undertake cluster analysis. The phenotypic divergence and relative importance were estimated by multivariate analysis. The cluster analysis classified linseed genotypes in to nine major groups. The maximum intercluster diversity was observed between cluster VIII and V. Based on mean performance of the genotypes and intercluster distance the crosses between ICAR Sel-1 and L-9, NDC 2005-34, H660, LCK 87042, NDL2005-22, GS335 is recommended to get use full transgressive sergeants in linseed.

scholarly journals Spatial Multivariate Cluster Analysis for Defining Target Population of Environments in West Africa for Yam Breeding

2019 ◽  
Vol 10 (3) ◽  
pp. 1-30 ◽  
Author(s):  
Tunrayo R. Alabi ◽  
Patrick Olusanmi Adebola ◽  
Asrat Asfaw ◽  
David De Koeyer ◽  
Antonio Lopez-Montes ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
West Africa ◽  
Poverty Reduction ◽  
Principal Component ◽  
Environmental Data ◽  
Wealth Creation ◽  
Multivariate Cluster Analysis ◽  
Breeding Research ◽  
Multivariate Cluster ◽  
The Impact

Yam (Dioscorea spp.) is a major staple crop with high agricultural and cultural significance for over 300 million people in West Africa. Despite its importance, productivity is miserably low. A better understanding of the environmental context in the region is essential to unlock the crop's potential for food security and wealth creation. The article aims to characterize the production environments into homologous mega-environments, having operational significance for breeding research. Principal component analysis (PCA) was performed separately on environmental data related to climate, soil, topography, and vegetation. Significant PCA layers were used in spatial multivariate cluster analysis. Seven clusters were identified for West Africa; four were country-specific; the rest were region-wide in extent. Clustering results are valuable inputs to optimize yam varietal selection and testing within and across the countries in West Africa. The impact of breeding research on poverty reduction and problems of market accessibility in yam production zones were highlighted.

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