14 
not be repeated here. The experimental steps can be 
roughly outlined as: (1) definition of the “community” 
of animals or plants or both to be studied, (2) carry- 
ing out of the census, (3) running of the factor analy- 
sis, (4) identification of the factors and rotation to a 
specified hypothesis, (5) formulation of the specification 
equations (first approximation), (6) discovery and an- 
alysis of nonlinear, factor-variable relationships and of 
nonadditive factors (second approximation), and (7) 
discovery and measurement of specific factors for each 
species (third approximation) . 
As the size of the community studied increases, the 
number of significant common factors discovered also 
increases. By increasing the number of species measured, 
a factor originally specific to one species may now in- 
fluence a second species and can be picked out by the 
factor analysis. As more species are considered, more 
factors must be identified. 
Because of the tremendous amount of field work 
and experimentation needed for this technique, the de- 
cision to stop at the first, second, or third approxima- 
tion will depend on how close the first approximation 
accurately predicts future changes (or spatial changes) 
in the species of the community and on how much time 
and money are available. 
A rough approximation is often all that is needed. 
A farmer usually wants to know only which species, 
any, of a set of possible pests will be abundant enou¢ 
to damage his crops, given a set of conditions that | 
can predict (e.g., will the application of a certain pest 
cide in the spring cause an increase in the populatio 
of some potential pest species later in the year?). VF 
is not particularly interested in the cxact level of ea 
population. 
The factor analysis technique is applicable to mod 
ing communities in both space and time. The fact 
analysis approach is an improvement over the multiy 
regression approach (actually a form of factor analysi 
in indicating not only how many factors to look f 
but also which species are influenced by which fact 
and the extent of the influences. The psychologists ha 
also found empirically (Kawash, personal communi 
tion) that the results of a factor analysis modeling of 
siutation using the specification equations tend to 
much more useful when applied to similar situatic 
(such as perhaps a model of one river being more a 
plicable to the fishes in an adjacent river), than are t 
multiple regression equations. 
Factor analysis is an extensive and complicated si 
ject. Just how useful this proposed technique will pre 
can only be known after it has been more extensiv 
used and studied. 
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ple structure. Psychology Bulletin 59:389-400. 
Dacneuie, P. 1965. L’étude des communautés végétales par 
L’analyse statistique des liasons entre les espéces et les varia- 
bles écologiques: un exemple. Biometrics 21:890-907. 
Goopat., D. W. 1954. Objective methods for the classifica- 
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Harman, H. H. 1967. Modern Factor Analysis. 2nd ed. Uni- 
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Hunter, A. S. 1966. High-altitude Drosophila of Colombia 
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Katser, H. F. 1958. The varimax criterion for analytic rota- 
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