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A method for assigning environmental variables to observed biological changes in benthic communities is proposed. The approach requires biological and environmental sampling at the same sites. Additionally, a biological gradient or trend such as a change in observed species or a significant change in their relative abundances is necessary in order to connect the biological observations to the environmental measurements. Whether there is a statistical significant correspondence between the environmental measurements and the biological changes is tested after quantifying the biological changes by using the community disturbance index (CDI). Finally, the environmental variables that are most strongly associated with the biological changes are identified, and it is proposed that these are strong candidates as the pollutants responsible for the biological changes observed. However, this cannot be confirmed using the monitored data only. The approach is tested on data collected in monitoring surveys at the Ekofisk oil field in the North Sea. The results indicate the method is feasible for assigning environmental variables to observed biological changes.

Abstract: Offshore oil production is regularly monitored by environmental surveys of sediments close to the oil platforms. The type and abundance of benthic species at the different sampling sites are used as an indicator of possible pollution. Presently, the data for each survey are analysed independently of data from earlier surveys. Accordingly, it can be difficult to draw conclusions about trends and changes in the environmental state of the respective oil fields. In this work, it is suggested to organise the data from successive surveys in a three-way array, and use multi-way decomposition, here Tucker3, to identify possible changes in the benthic communities over the monitoring period. The feasibility of the approach is tested on two data sets from the Tordis and Embla oil fields, which both are located at the Norwegian Continental Shelf. The multi-way decomposition provides useful information about the changes over the monitoring period for both fields. [Copyright &y& Elsevier]

A method for validation of the reference set in Soft Independent Modelling of Class Analogies (SIMCA) is proposed. The reference set is used to build the SIMCA model and the remaining samples are fitted to this model. Thus, it is important that the reference set is representative for the reference class. In this work it is suggested that the reference set can be validated by the jackknife procedure. The jackknife estimate of standard error for the reference set is determined by successively leaving one sample out. It is proposed that the standard error should be minimised for an optimal reference set. Minimisation of the standard error should be balanced with the loss of variation span for the reference set to avoid a too narrow reference class. The reference sets are optimised by changing the composition of the reference set. The suggested validation method is tested on two data sets from environmental monitoring surveys of oil fields in the North Sea. [Copyright &y& Elsevier]

Abstract: A multivariate index assessing community stress in marine benthic count data is compared to other multivariate methods. Both ordinations and clustering methods are included in the comparison and so is AMBI (AZTI marine biotic index). The community disturbance index (CDI) requires the data to contain undisturbed reference samples. If this requirement is met, the calculations are carried out in two steps. Firstly, the reference samples are separated and used to build a model representing the natural variation. Subsequently, the remaining samples are compared to the model and the respective disturbance indices are calculated. The CDI has the same sensitivity as the traditional multivariate methods. Additionally, one obtains a quantification of the relative level of disturbance. The comparison is performed using data from monitoring surveys at three different oilfields in the North Sea: Troll, Ekofisk and Oseberg-Brage. The samples are collected in 1996, 1990 and 1996, respectively. [Copyright &y& Elsevier]