Baseline study Fish, fry and commercial fishery Nysted Offshore ...

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3. Univariate or multivariate analysis?
As it appears from the previous, there is a fundamental choice to make regarding the
analytical method for calculations the test statistics within the >BACI design. Should
the method be uni- or multivariate? It is crucial with respect to that choice whether the
variables of the results are correlated or not. In relation to the data in the present study it
is important to consider if the occurrence of the fish species are independent of each
other or if the presence of one species increase the probability to encounter certain
species in the same area. Dependent variables are the rule of thumb in this kind of field
study and accordingly so in the present investigation. The dependency was tested based
on the null hypothesis of “no correlation different from zero among the p species” using
Bartlett’s test for spericity. Intuitively it makes sense that certain species occur as
functional groups attached to certain bottom conditions, food, water quality etc. The
presence of correlation is the first indication to use a multivariate analysis to get the
most information from the data.
3.1. Univariate analysis
If the data set is tested using a univariate BACI-analysis of the data from each
individual species, then there are a number of conditions, which should be noted.
• It is difficult to fulfil the requirements of normality and homogeneity of variance
for most of the species even after transformation These demands must be meet
to complete an ANOVA in its ordinary linear form. The main reason is the
presence of empty samples when the data are considered separately for each
species.
• Furthermore, it is important to note the danger of deriving conclusions
concerning overall significance based on what can be called mass tests. It is
implicit to the choice of level of significance that one out of 20 analysis should
show significance even if no interaction-effect in the BACI model exists in the
real world. If the results from each of the individual species are gathered to a an
overall level of significance then it is essential to adjust the level of significance
for each test according to number of species. If five independent species are
analysed using an univariate ANOVA then the level of significance should be
adjusted as follows: 1-(1-0.05) 1/5 = 1.02%. Or if the level is fixated at 5 % in
each sub-test, then there is a probability of 22.6 % for significance in at least one
of the five sub-tests.
3.2. Multivariate analysis
When the variables in question (species) are correlated as in the present baseline study,
then the natural choice of method would be a multivariate analysis. If not for the test of
significance, the ordination techniques can be used to explore the structures of the data
and therefore be guidance to the final test of significance.