The assessment methodology for biological effects measurements is essentially the same as that for chemical concentrations in biota. However, modifications are required for some biological effects and these are described below.

**Glutathionine transferase, acetylcholine esterase activity, aminolevulinic acid dehydratase**

Low values of these variables indicate unhealthy organisms, so status is assessed using the *lower* one-sided 95% confidence limit on the fitted mean value in the most recent monitoring year. For example, if the lower confidence limit is above the Background Assessment Concentration (BAC), then the mean value in the most recent monitoring year is significantly above the BAC and levels are said to be ‘at background’.

**Scope for growth**

The measurements are not log transformed because scope for growth can be negative and because the data are approximately normally distributed on the untransformed scale. Consequently, all models are of temporal changes on the original scale. Further, low scope for growth indicates unhealthy organisms, so status is assessed using the *lower* one-sided 95% confidence limit on the fitted mean scope for growth in the most recent monitoring year. As the data have not been transformed before modelling, the lower confidence limit is compared directly to the assessment criteria; i.e. there is no need for any back-transformation.

**Neutral red retention time, lysosomal labilisation period**

The measurements are ordinal (they only take a discrete set of ordered values) and should be modelled using a cumulative link mixed model. However, at present no time series has more than two years of data, so such methods have not yet been implemented. Instead, an ad-hoc assessment of status is made by calculating the mean measurement each year and comparing this value (1 year of data) or the maximum of the two values (2 years of data) to the assessment criteria. High values indicate healthy organisms.

**Comet assay**

The measurements are proportions and should be modelled assuming they have a beta-binomial (or quasi-binomial) distribution. However, at present no time series has more than two years of data, so such methods have not yet been implemented. Instead, an ad-hoc assessment of status is made by calculating the mean proportion each year and comparing this value (1 year of data) or the maximum of the two values (2 years of data) to the assessment criteria.

**Micronucleus assay**

The measurements are proportions and should be modelled assuming they have a binomial distribution. However, at present no time series has more than two years of data, so such methods have not yet been implemented. Instead, an ad-hoc assessment of status is made by calculating the weighted mean proportion each year (weighted by the total number of cells) and comparing this value (1 year of data) or the maximum of the two values (2 years of data) to the assessment criteria.

**Stress on stress**

The measurements are survival times (in days) and should be modelled assuming they have a discrete distribution appropriate for survival analysis. However, at present no time series has more than two years of data, so such methods have not yet been implemented. Instead, an ad-hoc assessment of status is made by calculating the mean survival time each year (weighted by the total number of cells) and comparing this value (1 year of data) or the maximum of the two values (2 years of data) to the assessment criteria. High values indicate healthy organisms.