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A matrix of environmental attributes and shoreline classes allowed us to infer at a manageable scale which biotic and physical properties needed more detailed analysis.
A matrix of environmental attributes and shoreline classes was used to identify combinations for detailed assessment of water level change impacts.
To select the combination of variables that best matched the biological distribution patterns, a similarity matrix of environmental variables based on Euclidean distances was linked to the taxonomic dissimilarities patterns (Theta matrix) among LMEs using the BEST [55] routine.
In the standard model, P = G+ E, where G is the (additive) genetic covariance matrix and E the matrix of environmental variances and covariances.
However, it is often hampered by its detection limit (1,000 cells/ml seawater) or various interferences from the matrix of environmental samples (Kuypers et al. 2003; Pavlekovic et al. 2009).
Also, as P is a function of G and E, where E is the matrix of environmental covariation [ 57], it is necessary to separate, as much as possible, the effects of environment from the effects of geographic marginality.
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Finally, the MSA-DLLME method was successfully applied for preconcentration and trace determination of RB and RG in different matrices of environmental waters, soft drink and cosmetic products.
Co-precipitation procedure has widely been employed for preconcentration and separation of metal ions from the matrices of environmental samples.
Correlation of the matrices of environmental variables with the biological, by means of a BIOENV routine, indicated that the variables that best explained patterns of spatial distribution of LMEs, based on their biological information (ρ = 61.1%) were: photoperiod, rain fall anomalies, SST, chlorophyll-a anomalies and the index of inorganic pollution (Table 5).
A PEC/TC matrix of this environmental change/infectious disease example illustrates that there is strong risk factor evidence for the relationship between the proximal factors of water quality as well as sanitation and hygiene levels and transmission of enteric pathogens.
The kinship between two haploids i and j, K i, j, is simply the proportion of the genome shared IBD, and the phenotypic covariance matrix is Cov (Y ) = ∑ τ = 1 n v τ K τ + Cov (E ), (24 where K τ is a symmetric matrix with 1's on the diagonal and off-diagonal elements [ K τ ] i j = (K i, j − K 0 1 − K 0 ) τ, (25)and Cov(E) is the covariance matrix of the environmental effects.
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