Multivariate analysis of the plankton communities in the Loosdrecht lakes: relationship with the chemical and physical environment

Hydrobiologia ◽  
1992 ◽  
Vol 233 (1-3) ◽  
pp. 105-117 ◽  
Author(s):  
O. F. R. Van Tongeren ◽  
L. Van Liere ◽  
R. D. Gulati ◽  
G. Postema ◽  
P. J. Boesewinkel-De Bruyn
Keyword(s):  
Multivariate Analysis ◽  
Physical Environment ◽  
Loosdrecht Lakes ◽  
Plankton Communities

scholarly journals Québec forest ecological classification program

1992 ◽  
Vol 68 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Jean-François Bergeron ◽  
Jean-Pierre Saucier ◽  
Denis Robert ◽  
André Robitaille
Keyword(s):  
Multivariate Analysis ◽  
Physical Environment ◽  
Forest Inventory ◽  
Forest Ecology ◽  
Site Classification ◽  
Forest Site ◽  
Ecological Classification ◽  
Forest Types ◽  
Ecological Data ◽  
Surface Deposit

In 1986, the ministère des Forêts du Québec instituted a provincial program to study forest ecosystems entitled the "Forest Ecological Classification (FEC) Program." Under this program, a multidisciplinary team was charged with conducting ecological surveys, analyzing and characterizing the variables of the physical environment, classifying vegetation and preparing integrated forest inventory maps. Their goal is to complete the ecological classification of the forests in all territories south of the 52nd parallel. To undertake such a vast project, it was necessary to prepare detailed methodological guides for data collection, data analysis and mapping. The following products are now available for many different ecological regions: classifications of forest types, toposequences, physiographic and surface deposit maps and integrated forest inventory maps. Multivariate analysis methods are used in analyzing ecological data; in this way, hierarchical classifications and ordinations can be used as the basis for identifying and describing forest types, vegetation-physical environment relationships and successional patterns. Such ecological classification products are an indispensable tool for forest managers and users. Key words: ecological classification, forest ecology, forest management, forest site classification, multivariate analysis, physical environment, Québec.

scholarly journals Loneliness amongst Low-Socioeconomic Status Elderly Singaporeans and its Association with Perceptions of the Neighbourhood Environment

2019 ◽  
Vol 16 (6) ◽  
pp. 967 ◽  
Author(s):  
Liang Wee ◽  
Tammy Tsang ◽  
Huso Yi ◽  
Sue Toh ◽  
Geok Lee ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Public Housing ◽  
Low Socioeconomic Status ◽  
Physical Environment ◽  
Home Ownership ◽  
Univariate Analysis ◽  
Rental Housing ◽  
Neighbourhood Environment ◽  
Chi Square ◽  
The Impact

In Singapore, a densely urbanised Asian city state, more than 80% of the population stays in public housing estates and the majority (90%) own their own homes. For the needy who cannot afford home ownership, public rental flats are available. We were interested in exploring social-environmental factors that are associated with loneliness among elderly residents of public rental housing in Singapore. We surveyed residents aged ≥60 in two Singapore public housing precincts in 2016. Loneliness was measured using a three-item scale. Sociodemographic information was obtained via standardised questionnaires. We used chi-square to identify associations between loneliness and sociodemographic characteristics, as well as neighbourhood perceptions (safety, convenience and the physical environment), on univariate analysis; and logistic regression for multivariate analysis. The response rate was 62.1% (528/800). On multivariate analysis, staying in a rental flat block was independently associated with loneliness (adjusted odds ratio, aOR = 2.10, 95% confidence interval (CI) = 1.32–3.36), as was staying in a poorer physical environment (aOR = 1.92, 95% CI = 1.15–3.22). Although needy Singapore residents share the same built environment as more well-to-do neighbours, differences in the impact of loneliness do exist.

Plankton succession and assemblage structure in two neighbouring littoral ecosystems in the north-west Mediterranean Sea

2005 ◽  
Vol 56 (1) ◽  
pp. 69 ◽  
Author(s):  
J.-L. Jamet ◽  
N. Jean ◽  
G. Bogé ◽  
S. Richard ◽  
D. Jamet
Keyword(s):  
Mediterranean Sea ◽  
Physical Environment ◽  
Human Activities ◽  
Early Spring ◽  
North West ◽  
Classical Scheme ◽  
The North ◽  
Seasonal Factors ◽  
Northern Winter ◽  
Plankton Communities

We studied seasonal variations in bacterial abundance and succession in phyto- and zooplankton assemblages (particularly small taxa) in two neighbouring shallow bays (near Toulon, Mediterranean Sea, France): Little Bay (polluted, eutrophic), and Niel Bay (less polluted, oligotrophic). In Little Bay, bacteria developed in northern spring and phytoplankton (Dinophyceae > 20 µm) in late northern winter–early spring. Zooplankton levels peaked at the end of northern spring and in autumn; this community was dominated by Oithona nana. In Niel Bay, bacterial levels peaked during northern spring and autumn. Phytoplankton (Dinophyceae, Bacillariophyceae) abundance was low and only peaked in June. Zooplankton levels peaked in northern mid-summer. Little Bay was influenced more by the land and by human activities than by the sea. Seasonal factors (e.g. water temperature) and sudden influences (e.g. rain and, indirectly, Mistral wind) may have modified the succession of the plankton communities in this bay. Successions did not follow Margalef’s model and the classical scheme for zooplankton. Conversely, Niel Bay functioning and plankton assemblages were most influenced by the physical environment of the sea than by the land or by human activities. Successions were closely related to the classical scheme of the Mediterranean Sea.

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

1118: The Incidence and Duration of Postoperative Ileus in Patients Undergoing Radical Cystectomy. A Multivariate Analysis of Contributing Factors

2005 ◽  
Vol 173 (4S) ◽  
pp. 303-303
Author(s):  
Diana Wiessner ◽  
Rainer J. Litz ◽  
Axel R. Heller ◽  
Mitko Georgiev ◽  
Oliver W. Hakenberg ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Radical Cystectomy ◽  
Postoperative Ileus ◽  
Contributing Factors
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