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 ## Change log
 
+### Version 1.0.6
+
+#### Major bug fix - p_overall_trend
+
+For smooth models, the degrees of freedom used in the likelihood ratio test of 
+an overall temporal trend are now correct. Previously, they were too small (by 
+one) resulting in values of `p_overall_trend` that were too significant. 
+Fortunately, this had no effect on model selection as the chosen model is 
+based on AICc or AIC (depending on the distribution of the response) and the 
+p-values are calculated after the model is chosen.
+
+The statistical interpretation of smooth models in `report_assessment` is now 
+more nuanced. Smooth models chosen by AIC or AICc are not necessarily significant
+at the conventional 5% level. The significance of the final model is given by 
+`p_overall_trend` which is based on a likelihood ratio test that compares the 
+smooth model and the mean model (and essentially tests for any evidence of a 
+change in concentrations over time). This degree of significance is now 
+characterised as weak (p >= 0.05), moderate (0.05 < p <= 0.01) and strong (p < 
+0.01). In theory, a smooth model chosen by AICc could have a 
+`p_overall_trend` as high as 0.135, but this can only happen when there are many
+years of data and the improvement in AICc between the smooth model and the mean
+model is marginal.
+
+Note that, for smooth models, the significance of the overall trend can be split 
+into the significance of the nonlinear component (`p_nonlinear_trend` based on a 
+likelihood ratio test that compares the smooth model with the linear model) and 
+the linear component (`p_linear_trend` based on a likelihood ratio test that 
+compares the linear model with the mean model). Both these p-values were 
+calculated correctly in previous releases. 
+
+
 ### Version 1.0.5
 
 This release is used to run the OSPAR 2026 assessment.