understanding of the methodology suggests that relatively large amounts of 
data gathered at frequent and evenly spread sampling intervals are highly 
desirable for this methodology to be effective in most instances. 
The general linear model analysis is described at various levels of detail in 
several statistics books, such as Cochran and Cox (3), Davies (4), Federer (6), 
Kempthorne (7), Sheffe (10), and Snedecor and Cochran (11). This linear 
model approach includes analysis of variance and regression analysis. In this 
approach variations in a response variable measured over time and space are 
decomposed into assignable sources of variations and these variations are 
assumed to be additive. Tests of significance, such as F-tests or variance ratio 
tests to determine the change in the mean value of some variable from several 
sample events, are based on certain assumptions such as a normal probability 
density and independent and homogeneous variance (10). Data from samples 
taken over time frequently do not conform to these assumptions. 
Nonstationary elements, such as seasonal or diurnal, and tidal components are 
often present, and the data may be highly correlated in time. 
In the linear model approach time, space and sampling locations, along with 
replications, become a part of a planned experimental design. As a means for 
considering spatial and temporal variability in the linear model, the spatial and 
temporal distributions of biota (i.e., ichthyoplankton) are treated as a sum of 
responses due to assignable sources of factor levels. In addition, 
transformations of the response variable are sometimes used to achieve 
homogeneity of variances. Finally, because one can expect certain physical and 
biological data to be correlated, these relationships can be effectively utilized 
by carrying out multivariate analyses of variance and covariance analyses. 
In carrying out the linear model approach to monitoring and impact 
assessment, the method involves formulating hypotheses or linear contrasts for 
carrying out the statistical tests. Among these contrasts one tests for main 
effects due to a defined factor and the interaction of factors ot interest. 
SCHEME FOR ESTABLISHING SAMPLING STATIONS 
We will illustrate the use of a simple linear model by an example which 
relates to deciding where to establish monitoring stations along a 
cross-sectional area of an estuary which is of interest. For simplicity we will 
assume that cost constraints limit the number of samples to about 50. Our 
prior knowledge of the problem suggests that we should be concerned about 
the depth distribution of the given organism (say winter flounder larvae). Table 
22-1 gives a sketch of the data as they might be gathered for this analysis. No 
time effects are considered in this analysis. 
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